[claude] Add web_fetch tool (trafilatura) for full-page content extraction (#973) (#1004)

Co-authored-by: Alexander Whitestone <alexpaynex@gmail.com>
Co-committed-by: Alexander Whitestone <alexpaynex@gmail.com>

pyproject.toml

@@ -50,6 +50,7 @@ sounddevice = { version = ">=0.4.6", optional = true }
 sentence-transformers = { version = ">=2.0.0", optional = true }
 numpy = { version = ">=1.24.0", optional = true }
 requests = { version = ">=2.31.0", optional = true }
+trafilatura = { version = ">=1.6.0", optional = true }
 GitPython = { version = ">=3.1.40", optional = true }
 pytest = { version = ">=8.0.0", optional = true }
 pytest-asyncio = { version = ">=0.24.0", optional = true }
@@ -67,6 +68,7 @@ voice = ["pyttsx3", "openai-whisper", "piper-tts", "sounddevice"]
 celery = ["celery"]
 embeddings = ["sentence-transformers", "numpy"]
 git = ["GitPython"]
+research = ["requests", "trafilatura"]
 dev = ["pytest", "pytest-asyncio", "pytest-cov", "pytest-timeout", "pytest-randomly", "pytest-xdist", "selenium"]
 
 [tool.poetry.group.dev.dependencies]

skills/research/architecture_spike.md

@@ -0,0 +1,67 @@
+---
+name: Architecture Spike
+type: research
+typical_query_count: 2-4
+expected_output_length: 600-1200 words
+cascade_tier: groq_preferred
+description: >
+  Investigate how to connect two systems or components. Produces an integration
+  architecture with sequence diagram, key decisions, and a proof-of-concept outline.
+---
+
+# Architecture Spike: Connect {system_a} to {system_b}
+
+## Context
+
+We need to integrate **{system_a}** with **{system_b}** in the context of
+**{project_context}**. This spike answers: what is the best way to wire them
+together, and what are the trade-offs?
+
+## Constraints
+
+- Prefer approaches that avoid adding new infrastructure dependencies.
+- The integration should be **{sync_or_async}** (synchronous / asynchronous).
+- Must work within: {environment_constraints}.
+
+## Research Steps
+
+1. Identify the APIs / protocols exposed by both systems.
+2. List all known integration patterns (direct API, message queue, webhook, SDK, etc.).
+3. Evaluate each pattern for complexity, reliability, and latency.
+4. Select the recommended approach and outline a proof-of-concept.
+
+## Output Format
+
+### Integration Options
+
+| Pattern | Complexity | Reliability | Latency | Notes |
+|---------|-----------|-------------|---------|-------|
+| ...     | ...       | ...         | ...     | ...   |
+
+### Recommended Approach
+
+**Pattern:** {pattern_name}
+
+**Why:** One paragraph explaining the choice.
+
+### Sequence Diagram
+
+```
+{system_a} -> {middleware} -> {system_b}
+```
+
+Describe the data flow step by step:
+
+1. {system_a} does X...
+2. {middleware} transforms / routes...
+3. {system_b} receives Y...
+
+### Proof-of-Concept Outline
+
+- Files to create or modify
+- Key libraries / dependencies needed
+- Estimated effort: {effort_estimate}
+
+### Open Questions
+
+Bullet list of decisions that need human input before proceeding.

skills/research/competitive_scan.md

@@ -0,0 +1,74 @@
+---
+name: Competitive Scan
+type: research
+typical_query_count: 3-5
+expected_output_length: 800-1500 words
+cascade_tier: groq_preferred
+description: >
+  Compare a project against its alternatives. Produces a feature matrix,
+  strengths/weaknesses analysis, and positioning summary.
+---
+
+# Competitive Scan: {project} vs Alternatives
+
+## Context
+
+Compare **{project}** against **{alternatives}** (comma-separated list of
+competitors). The goal is to understand where {project} stands and identify
+differentiation opportunities.
+
+## Constraints
+
+- Comparison date: {date}.
+- Focus areas: {focus_areas} (e.g., features, pricing, community, performance).
+- Perspective: {perspective} (user, developer, business).
+
+## Research Steps
+
+1. Gather key facts about {project} (features, pricing, community size, release cadence).
+2. Gather the same data for each alternative in {alternatives}.
+3. Build a feature comparison matrix.
+4. Identify strengths and weaknesses for each entry.
+5. Summarize positioning and recommend next steps.
+
+## Output Format
+
+### Overview
+
+One paragraph: what space does {project} compete in, and who are the main players?
+
+### Feature Matrix
+
+| Feature / Attribute | {project} | {alt_1} | {alt_2} | {alt_3} |
+|--------------------|-----------|---------|---------|---------|
+| {feature_1}        | ...       | ...     | ...     | ...     |
+| {feature_2}        | ...       | ...     | ...     | ...     |
+| Pricing            | ...       | ...     | ...     | ...     |
+| License            | ...       | ...     | ...     | ...     |
+| Community Size     | ...       | ...     | ...     | ...     |
+| Last Major Release | ...       | ...     | ...     | ...     |
+
+### Strengths & Weaknesses
+
+#### {project}
+- **Strengths:** ...
+- **Weaknesses:** ...
+
+#### {alt_1}
+- **Strengths:** ...
+- **Weaknesses:** ...
+
+_(Repeat for each alternative)_
+
+### Positioning Map
+
+Describe where each project sits along the key dimensions (e.g., simplicity
+vs power, free vs paid, niche vs general).
+
+### Recommendations
+
+Bullet list of actions based on the competitive landscape:
+
+- **Differentiate on:** {differentiator}
+- **Watch out for:** {threat}
+- **Consider adopting from {alt}:** {feature_or_approach}

skills/research/game_analysis.md

@@ -0,0 +1,68 @@
+---
+name: Game Analysis
+type: research
+typical_query_count: 2-3
+expected_output_length: 600-1000 words
+cascade_tier: local_ok
+description: >
+  Evaluate a game for AI agent playability. Assesses API availability,
+  observation/action spaces, and existing bot ecosystems.
+---
+
+# Game Analysis: {game}
+
+## Context
+
+Evaluate **{game}** to determine whether an AI agent can play it effectively.
+Focus on programmatic access, observation space, action space, and existing
+bot/AI ecosystems.
+
+## Constraints
+
+- Platform: {platform} (PC, console, mobile, browser).
+- Agent type: {agent_type} (reinforcement learning, rule-based, LLM-driven, hybrid).
+- Budget for API/licenses: {budget}.
+
+## Research Steps
+
+1. Identify official APIs, modding support, or programmatic access methods for {game}.
+2. Characterize the observation space (screen pixels, game state JSON, memory reading, etc.).
+3. Characterize the action space (keyboard/mouse, API calls, controller inputs).
+4. Survey existing bots, AI projects, or research papers for {game}.
+5. Assess feasibility and difficulty for the target agent type.
+
+## Output Format
+
+### Game Profile
+
+| Property          | Value                  |
+|-------------------|------------------------|
+| Game              | {game}                 |
+| Genre             | {genre}                |
+| Platform          | {platform}             |
+| API Available     | Yes / No / Partial     |
+| Mod Support       | Yes / No / Limited     |
+| Existing AI Work  | Extensive / Some / None|
+
+### Observation Space
+
+Describe what data the agent can access and how (API, screen capture, memory hooks, etc.).
+
+### Action Space
+
+Describe how the agent can interact with the game (input methods, timing constraints, etc.).
+
+### Existing Ecosystem
+
+List known bots, frameworks, research papers, or communities working on AI for {game}.
+
+### Feasibility Assessment
+
+- **Difficulty:** Easy / Medium / Hard / Impractical
+- **Best approach:** {recommended_agent_type}
+- **Key challenges:** Bullet list
+- **Estimated time to MVP:** {time_estimate}
+
+### Recommendation
+
+One paragraph: should we proceed, and if so, what is the first step?

skills/research/integration_guide.md

@@ -0,0 +1,79 @@
+---
+name: Integration Guide
+type: research
+typical_query_count: 3-5
+expected_output_length: 1000-2000 words
+cascade_tier: groq_preferred
+description: >
+  Step-by-step guide to wire a specific tool into an existing stack,
+  complete with code samples, configuration, and testing steps.
+---
+
+# Integration Guide: Wire {tool} into {stack}
+
+## Context
+
+Integrate **{tool}** into our **{stack}** stack. The goal is to
+**{integration_goal}** (e.g., "add vector search to the dashboard",
+"send notifications via Telegram").
+
+## Constraints
+
+- Must follow existing project conventions (see CLAUDE.md).
+- No new cloud AI dependencies unless explicitly approved.
+- Environment config via `pydantic-settings` / `config.py`.
+
+## Research Steps
+
+1. Review {tool}'s official documentation for installation and setup.
+2. Identify the minimal dependency set required.
+3. Map {tool}'s API to our existing patterns (singletons, graceful degradation).
+4. Write integration code with proper error handling.
+5. Define configuration variables and their defaults.
+
+## Output Format
+
+### Prerequisites
+
+- Dependencies to install (with versions)
+- External services or accounts required
+- Environment variables to configure
+
+### Configuration
+
+```python
+# In config.py — add these fields to Settings:
+{config_fields}
+```
+
+### Implementation
+
+```python
+# {file_path}
+{implementation_code}
+```
+
+### Graceful Degradation
+
+Describe how the integration behaves when {tool} is unavailable:
+
+| Scenario              | Behavior           | Log Level |
+|-----------------------|--------------------|-----------|
+| {tool} not installed  | {fallback}         | WARNING   |
+| {tool} unreachable    | {fallback}         | WARNING   |
+| Invalid credentials   | {fallback}         | ERROR     |
+
+### Testing
+
+```python
+# tests/unit/test_{tool_snake}.py
+{test_code}
+```
+
+### Verification Checklist
+
+- [ ] Dependency added to pyproject.toml
+- [ ] Config fields added with sensible defaults
+- [ ] Graceful degradation tested (service down)
+- [ ] Unit tests pass (`tox -e unit`)
+- [ ] No new linting errors (`tox -e lint`)

skills/research/state_of_art.md

@@ -0,0 +1,67 @@
+---
+name: State of the Art
+type: research
+typical_query_count: 4-6
+expected_output_length: 1000-2000 words
+cascade_tier: groq_preferred
+description: >
+  Comprehensive survey of what currently exists in a given field or domain.
+  Produces a structured landscape overview with key players, trends, and gaps.
+---
+
+# State of the Art: {field} (as of {date})
+
+## Context
+
+Survey the current landscape of **{field}**. Identify key players, recent
+developments, dominant approaches, and notable gaps. This is a point-in-time
+snapshot intended to inform decision-making.
+
+## Constraints
+
+- Focus on developments from the last {timeframe} (e.g., 12 months, 2 years).
+- Prioritize {priority} (open-source, commercial, academic, or all).
+- Target audience: {audience} (technical team, leadership, general).
+
+## Research Steps
+
+1. Identify the major categories or sub-domains within {field}.
+2. For each category, list the leading projects, companies, or research groups.
+3. Note recent milestones, releases, or breakthroughs.
+4. Identify emerging trends and directions.
+5. Highlight gaps — things that don't exist yet but should.
+
+## Output Format
+
+### Executive Summary
+
+Two to three sentences: what is the state of {field} right now?
+
+### Landscape Map
+
+| Category       | Key Players              | Maturity    | Trend       |
+|---------------|--------------------------|-------------|-------------|
+| {category_1}  | {player_a}, {player_b}   | Early / GA  | Growing / Stable / Declining |
+| {category_2}  | {player_c}, {player_d}   | Early / GA  | Growing / Stable / Declining |
+
+### Recent Milestones
+
+Chronological list of notable events in the last {timeframe}:
+
+- **{date_1}:** {event_description}
+- **{date_2}:** {event_description}
+
+### Trends
+
+Numbered list of the top 3-5 trends shaping {field}:
+
+1. **{trend_name}** — {one-line description}
+2. **{trend_name}** — {one-line description}
+
+### Gaps & Opportunities
+
+Bullet list of things that are missing, underdeveloped, or ripe for innovation.
+
+### Implications for Us
+
+One paragraph: what does this mean for our project? What should we do next?

skills/research/tool_evaluation.md

@@ -0,0 +1,52 @@
+---
+name: Tool Evaluation
+type: research
+typical_query_count: 3-5
+expected_output_length: 800-1500 words
+cascade_tier: groq_preferred
+description: >
+  Discover and evaluate all shipping tools/libraries/services in a given domain.
+  Produces a ranked comparison table with pros, cons, and recommendation.
+---
+
+# Tool Evaluation: {domain}
+
+## Context
+
+You are researching tools, libraries, and services for **{domain}**.
+The goal is to find everything that is currently shipping (not vaporware)
+and produce a structured comparison.
+
+## Constraints
+
+- Only include tools that have public releases or hosted services available today.
+- If a tool is in beta/preview, note that clearly.
+- Focus on {focus_criteria} when evaluating (e.g., cost, ease of integration, community size).
+
+## Research Steps
+
+1. Identify all actively-maintained tools in the **{domain}** space.
+2. For each tool, gather: name, URL, license/pricing, last release date, language/platform.
+3. Evaluate each tool against the focus criteria.
+4. Rank by overall fit for the use case: **{use_case}**.
+
+## Output Format
+
+### Summary
+
+One paragraph: what the landscape looks like and the top recommendation.
+
+### Comparison Table
+
+| Tool | License / Price | Last Release | Language | {focus_criteria} Score | Notes |
+|------|----------------|--------------|----------|----------------------|-------|
+| ...  | ...            | ...          | ...      | ...                  | ...   |
+
+### Top Pick
+
+- **Recommended:** {tool_name} — {one-line reason}
+- **Runner-up:** {tool_name} — {one-line reason}
+
+### Risks & Gaps
+
+Bullet list of things to watch out for (missing features, vendor lock-in, etc.).

src/timmy/research.py

@@ -1,555 +0,0 @@
-"""ResearchOrchestrator — autonomous research pipeline.
-
-Chains: Check Local → Generate Queries → Search → Fetch → Synthesize →
-Crystallize → Write Artifact into an end-to-end research workflow.
-
-Usage:
-    from timmy.research import ResearchOrchestrator, run_research
-
-    orchestrator = ResearchOrchestrator(cascade=router, memory=memory_fns)
-    result = await orchestrator.run("Bitcoin Lightning Network scaling")
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import re
-import time
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import Any
-
-from config import settings
-
-logger = logging.getLogger(__name__)
-
-# ── Data structures ──────────────────────────────────────────────────────────
-
-CONFIDENCE_THRESHOLD = 0.85
-DEFAULT_QUERIES_PER_TOPIC = 8
-DEFAULT_RESULTS_PER_QUERY = 5
-DEFAULT_PAGES_TO_FETCH = 10
-DEFAULT_FETCH_TOKEN_LIMIT = 3000
-DEFAULT_SYNTHESIS_MAX_TOKENS = 4000
-
-
-@dataclass
-class ResearchResult:
-    """Output of a completed research pipeline run."""
-
-    topic: str
-    report: str
-    queries_generated: list[str] = field(default_factory=list)
-    sources: list[dict[str, str]] = field(default_factory=list)
-    action_items: list[str] = field(default_factory=list)
-    cache_hit: bool = False
-    duration_ms: float = 0.0
-    metrics: dict[str, Any] = field(default_factory=dict)
-    timestamp: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
-
-
-@dataclass
-class SearchSnippet:
-    """A single search result snippet."""
-
-    title: str
-    url: str
-    snippet: str
-    relevance: float = 0.0
-
-
-@dataclass
-class FetchedPage:
-    """A fetched and truncated web page."""
-
-    url: str
-    title: str
-    content: str
-    token_estimate: int = 0
-
-
-# ── Memory interface ─────────────────────────────────────────────────────────
-
-
-@dataclass
-class MemoryInterface:
-    """Abstraction over the memory system for research.
-
-    Accepts callables so the orchestrator doesn't depend on a specific
-    memory implementation. Defaults wire to timmy.memory_system.
-    """
-
-    search_fn: Any = None  # (query, limit) -> list[MemoryEntry]
-    store_fn: Any = None  # (content, source, context_type, ...) -> MemoryEntry
-
-    def __post_init__(self):
-        if self.search_fn is None or self.store_fn is None:
-            self._load_defaults()
-
-    def _load_defaults(self):
-        try:
-            from timmy.memory_system import search_memories, store_memory
-
-            if self.search_fn is None:
-                self.search_fn = search_memories
-            if self.store_fn is None:
-                self.store_fn = store_memory
-        except ImportError:
-            logger.warning("Memory system not available — research will skip caching")
-            if self.search_fn is None:
-                self.search_fn = lambda query, **kw: []
-            if self.store_fn is None:
-                self.store_fn = lambda content, source, **kw: None
-
-
-# ── Tool interface ───────────────────────────────────────────────────────────
-
-
-@dataclass
-class ResearchTools:
-    """Web search and fetch callables.
-
-    These are async callables:
-      web_search(query: str, limit: int) -> list[dict]
-      web_fetch(url: str, max_tokens: int) -> str
-    """
-
-    web_search: Any = None
-    web_fetch: Any = None
-
-
-# ── Orchestrator ─────────────────────────────────────────────────────────────
-
-
-class ResearchOrchestrator:
-    """Pipeline that chains research steps into an autonomous workflow.
-
-    Steps:
-        0. CHECK LOCAL KNOWLEDGE — search memory, return cached if confident
-        1. GENERATE QUERIES — ask LLM to produce search queries
-        2. SEARCH — execute queries via web_search tool
-        3. FETCH — rank snippets, fetch top pages
-        4. SYNTHESIZE — produce structured report via LLM
-        5. CRYSTALLIZE — store result in semantic memory
-        6. WRITE ARTIFACT — create Gitea issues from action items
-    """
-
-    def __init__(
-        self,
-        cascade: Any,
-        memory: MemoryInterface | None = None,
-        tools: ResearchTools | None = None,
-    ) -> None:
-        self.cascade = cascade
-        self.memory = memory or MemoryInterface()
-        self.tools = tools or ResearchTools()
-        self._metrics: dict[str, int] = {
-            "research_cache_hit": 0,
-            "research_api_call": 0,
-        }
-
-    async def run(
-        self,
-        topic: str,
-        template: str | None = None,
-        context: dict[str, Any] | None = None,
-    ) -> ResearchResult:
-        """Execute the full research pipeline.
-
-        Args:
-            topic: The research topic or question.
-            template: Optional prompt template for synthesis.
-            context: Additional context dict (cascade_tier hint, etc.).
-
-        Returns:
-            ResearchResult with report, sources, and action items.
-        """
-        start = time.monotonic()
-        context = context or {}
-        cascade_tier = context.get("cascade_tier")
-
-        # Step 0: Check local knowledge
-        cached = await self._check_local_knowledge(topic)
-        if cached is not None:
-            self._metrics["research_cache_hit"] += 1
-            cached.duration_ms = (time.monotonic() - start) * 1000
-            return cached
-
-        self._metrics["research_api_call"] += 1
-
-        # Step 1: Generate queries
-        queries = await self._generate_queries(topic, template, cascade_tier)
-
-        # Step 2: Search
-        snippets = await self._search(queries)
-
-        # Step 3: Fetch top pages
-        pages = await self._fetch(snippets)
-
-        # Step 4: Synthesize
-        report = await self._synthesize(topic, template, pages, cascade_tier)
-
-        # Step 5: Extract action items
-        action_items = _extract_action_items(report)
-
-        # Build result
-        sources = [{"url": p.url, "title": p.title} for p in pages]
-        result = ResearchResult(
-            topic=topic,
-            report=report,
-            queries_generated=queries,
-            sources=sources,
-            action_items=action_items,
-            cache_hit=False,
-            duration_ms=(time.monotonic() - start) * 1000,
-            metrics=dict(self._metrics),
-        )
-
-        # Step 6: Crystallize — store in memory
-        await self._crystallize(topic, result)
-
-        # Step 7: Write artifact — create Gitea issues
-        await self._write_artifact(result)
-
-        return result
-
-    # ── Pipeline steps ───────────────────────────────────────────────────
-
-    async def _check_local_knowledge(self, topic: str) -> ResearchResult | None:
-        """Search semantic memory for existing research on this topic."""
-        try:
-            results = self.memory.search_fn(
-                query=topic, limit=10, context_type="research"
-            )
-            if not results:
-                return None
-
-            # Check if top result has high confidence
-            top = results[0]
-            score = getattr(top, "relevance_score", 0.0) or 0.0
-            if score >= CONFIDENCE_THRESHOLD:
-                content = getattr(top, "content", str(top))
-                logger.info(
-                    "Research cache hit for '%s' (score=%.2f)", topic, score
-                )
-                return ResearchResult(
-                    topic=topic,
-                    report=content,
-                    cache_hit=True,
-                    metrics={"research_cache_hit": 1},
-                )
-        except Exception as exc:
-            logger.warning("Local knowledge check failed: %s", exc)
-
-        return None
-
-    async def _generate_queries(
-        self,
-        topic: str,
-        template: str | None,
-        cascade_tier: str | None,
-    ) -> list[str]:
-        """Ask the LLM to generate search queries for the topic."""
-        prompt = (
-            f"Generate {DEFAULT_QUERIES_PER_TOPIC} diverse web search queries "
-            f"to thoroughly research the following topic. Return ONLY the "
-            f"queries, one per line, no numbering or bullets.\n\n"
-            f"Topic: {topic}"
-        )
-        if template:
-            prompt += f"\n\nResearch template context:\n{template}"
-
-        messages = [
-            {"role": "system", "content": "You are a research query generator."},
-            {"role": "user", "content": prompt},
-        ]
-
-        kwargs: dict[str, Any] = {"messages": messages, "temperature": 0.7}
-        if cascade_tier:
-            kwargs["model"] = cascade_tier
-
-        try:
-            response = await self.cascade.complete(**kwargs)
-            raw = response.get("content", "")
-            queries = [
-                line.strip()
-                for line in raw.strip().splitlines()
-                if line.strip() and not line.strip().startswith("#")
-            ]
-            # Clean numbering prefixes
-            cleaned = []
-            for q in queries:
-                q = re.sub(r"^\d+[\.\)]\s*", "", q)
-                q = re.sub(r"^[-*]\s*", "", q)
-                if q:
-                    cleaned.append(q)
-            return cleaned[:DEFAULT_QUERIES_PER_TOPIC + 4]  # slight over-generate
-        except Exception as exc:
-            logger.warning("Query generation failed: %s", exc)
-            # Fallback: use topic itself as a single query
-            return [topic]
-
-    async def _search(self, queries: list[str]) -> list[SearchSnippet]:
-        """Execute search queries and collect snippets."""
-        if not self.tools.web_search:
-            logger.warning("No web_search tool configured — skipping search step")
-            return []
-
-        all_snippets: list[SearchSnippet] = []
-
-        async def _run_query(query: str) -> list[SearchSnippet]:
-            try:
-                results = await asyncio.to_thread(
-                    self.tools.web_search, query, DEFAULT_RESULTS_PER_QUERY
-                )
-                snippets = []
-                for r in (results or []):
-                    snippets.append(
-                        SearchSnippet(
-                            title=r.get("title", ""),
-                            url=r.get("url", ""),
-                            snippet=r.get("snippet", ""),
-                        )
-                    )
-                return snippets
-            except Exception as exc:
-                logger.warning("Search failed for query '%s': %s", query, exc)
-                return []
-
-        # Run searches concurrently
-        tasks = [_run_query(q) for q in queries]
-        results = await asyncio.gather(*tasks)
-        for snippets in results:
-            all_snippets.extend(snippets)
-
-        # Deduplicate by URL
-        seen_urls: set[str] = set()
-        unique: list[SearchSnippet] = []
-        for s in all_snippets:
-            if s.url and s.url not in seen_urls:
-                seen_urls.add(s.url)
-                unique.append(s)
-
-        return unique
-
-    async def _fetch(self, snippets: list[SearchSnippet]) -> list[FetchedPage]:
-        """Fetch top pages from search snippets."""
-        if not self.tools.web_fetch:
-            logger.warning("No web_fetch tool configured — skipping fetch step")
-            return []
-
-        # Take top N snippets
-        to_fetch = snippets[:DEFAULT_PAGES_TO_FETCH]
-        pages: list[FetchedPage] = []
-
-        async def _fetch_one(snippet: SearchSnippet) -> FetchedPage | None:
-            try:
-                content = await asyncio.to_thread(
-                    self.tools.web_fetch, snippet.url, DEFAULT_FETCH_TOKEN_LIMIT
-                )
-                if content:
-                    return FetchedPage(
-                        url=snippet.url,
-                        title=snippet.title,
-                        content=content[:DEFAULT_FETCH_TOKEN_LIMIT * 4],
-                        token_estimate=len(content.split()),
-                    )
-            except Exception as exc:
-                logger.warning("Fetch failed for %s: %s", snippet.url, exc)
-            return None
-
-        tasks = [_fetch_one(s) for s in to_fetch]
-        results = await asyncio.gather(*tasks)
-        for page in results:
-            if page is not None:
-                pages.append(page)
-
-        return pages
-
-    async def _synthesize(
-        self,
-        topic: str,
-        template: str | None,
-        pages: list[FetchedPage],
-        cascade_tier: str | None,
-    ) -> str:
-        """Synthesize fetched pages into a structured research report."""
-        # Build context from fetched pages
-        context_parts = []
-        for i, page in enumerate(pages, 1):
-            context_parts.append(
-                f"--- Source {i}: {page.title} ({page.url}) ---\n"
-                f"{page.content[:DEFAULT_FETCH_TOKEN_LIMIT * 4]}\n"
-            )
-
-        sources_text = "\n".join(context_parts) if context_parts else "(no sources fetched)"
-
-        if template:
-            prompt = (
-                f"{template}\n\n"
-                f"Topic: {topic}\n\n"
-                f"Research sources:\n{sources_text}\n\n"
-                f"Synthesize a comprehensive report based on the sources above."
-            )
-        else:
-            prompt = (
-                f"Write a comprehensive research report on: {topic}\n\n"
-                f"Research sources:\n{sources_text}\n\n"
-                f"Structure your report with:\n"
-                f"- Executive summary\n"
-                f"- Key findings\n"
-                f"- Analysis\n"
-                f"- Action items (prefix each with 'ACTION:')\n"
-                f"- Sources cited"
-            )
-
-        messages = [
-            {"role": "system", "content": "You are a research analyst producing structured reports."},
-            {"role": "user", "content": prompt},
-        ]
-
-        kwargs: dict[str, Any] = {
-            "messages": messages,
-            "temperature": 0.3,
-            "max_tokens": DEFAULT_SYNTHESIS_MAX_TOKENS,
-        }
-        if cascade_tier:
-            kwargs["model"] = cascade_tier
-
-        try:
-            response = await self.cascade.complete(**kwargs)
-            return response.get("content", "")
-        except Exception as exc:
-            logger.error("Synthesis failed: %s", exc)
-            # Fallback: return raw source summaries
-            return (
-                f"# Research: {topic}\n\n"
-                f"Synthesis failed ({exc}). Raw sources:\n\n{sources_text}"
-            )
-
-    async def _crystallize(self, topic: str, result: ResearchResult) -> None:
-        """Store the research result in semantic memory."""
-        try:
-            self.memory.store_fn(
-                content=result.report,
-                source="research_orchestrator",
-                context_type="research",
-                metadata={
-                    "topic": topic,
-                    "sources": result.sources,
-                    "action_items": result.action_items,
-                    "cache_hit": result.cache_hit,
-                    "duration_ms": result.duration_ms,
-                },
-            )
-            logger.info("Crystallized research on '%s' into memory", topic)
-        except Exception as exc:
-            logger.warning("Failed to crystallize research: %s", exc)
-
-    async def _write_artifact(self, result: ResearchResult) -> None:
-        """Create Gitea issues from action items."""
-        if not result.action_items:
-            return
-
-        try:
-            await asyncio.to_thread(_create_gitea_issues, result)
-        except Exception as exc:
-            logger.warning("Failed to create Gitea issues: %s", exc)
-
-    def get_metrics(self) -> dict[str, int]:
-        """Return current research pipeline metrics."""
-        return dict(self._metrics)
-
-
-# ── Helpers ──────────────────────────────────────────────────────────────────
-
-
-def _extract_action_items(report: str) -> list[str]:
-    """Extract action items from a research report.
-
-    Looks for lines prefixed with ACTION:, TODO:, or - [ ].
-    """
-    items: list[str] = []
-    for line in report.splitlines():
-        stripped = line.strip()
-        # ACTION: prefix
-        match = re.match(r"^(?:ACTION|TODO)\s*:\s*(.+)", stripped, re.IGNORECASE)
-        if match:
-            items.append(match.group(1).strip())
-            continue
-        # Markdown checkbox
-        match = re.match(r"^-\s*\[\s*\]\s*(.+)", stripped)
-        if match:
-            items.append(match.group(1).strip())
-
-    return items
-
-
-def _create_gitea_issues(result: ResearchResult) -> None:
-    """Create Gitea issues for action items (runs in thread)."""
-    if not settings.gitea_token or not settings.gitea_url:
-        logger.debug("Gitea not configured — skipping issue creation")
-        return
-
-    try:
-        import requests
-    except ImportError:
-        logger.debug("requests not available — skipping Gitea issue creation")
-        return
-
-    base_url = settings.gitea_url.rstrip("/")
-    repo = settings.gitea_repo
-    headers = {
-        "Authorization": f"token {settings.gitea_token}",
-        "Content-Type": "application/json",
-    }
-
-    for item in result.action_items:
-        try:
-            payload = {
-                "title": f"[research] {item[:100]}",
-                "body": (
-                    f"Auto-generated from research on: **{result.topic}**\n\n"
-                    f"Action item: {item}\n\n"
-                    f"---\n"
-                    f"_Created by ResearchOrchestrator_"
-                ),
-            }
-            resp = requests.post(
-                f"{base_url}/api/v1/repos/{repo}/issues",
-                headers=headers,
-                json=payload,
-                timeout=10,
-            )
-            if resp.status_code in (200, 201):
-                logger.info("Created Gitea issue: %s", item[:60])
-            else:
-                logger.warning(
-                    "Gitea issue creation failed (%d): %s",
-                    resp.status_code,
-                    resp.text[:200],
-                )
-        except Exception as exc:
-            logger.warning("Failed to create issue '%s': %s", item[:60], exc)
-
-
-# ── Convenience function ─────────────────────────────────────────────────────
-
-
-async def run_research(
-    topic: str,
-    template: str | None = None,
-    context: dict[str, Any] | None = None,
-) -> ResearchResult:
-    """Convenience function to run research with default dependencies.
-
-    Creates a ResearchOrchestrator with the cascade router singleton
-    and default memory, then executes the pipeline.
-    """
-    from infrastructure.router.cascade import get_router
-
-    cascade = get_router()
-    orchestrator = ResearchOrchestrator(cascade=cascade)
-    return await orchestrator.run(topic, template=template, context=context)

src/timmy/tools.py

@@ -473,6 +473,69 @@ def consult_grok(query: str) -> str:
     return response
 
 
+def web_fetch(url: str, max_tokens: int = 4000) -> str:
+    """Fetch a web page and return its main text content.
+
+    Downloads the URL, extracts readable text using trafilatura, and
+    truncates to a token budget.  Use this to read full articles, docs,
+    or blog posts that web_search only returns snippets for.
+
+    Args:
+        url: The URL to fetch (must start with http:// or https://).
+        max_tokens: Maximum approximate token budget (default 4000).
+                    Text is truncated to max_tokens * 4 characters.
+
+    Returns:
+        Extracted text content, or an error message on failure.
+    """
+    if not url or not url.startswith(("http://", "https://")):
+        return f"Error: invalid URL — must start with http:// or https://: {url!r}"
+
+    try:
+        import requests as _requests
+    except ImportError:
+        return "Error: 'requests' package is not installed. Install with: pip install requests"
+
+    try:
+        import trafilatura
+    except ImportError:
+        return (
+            "Error: 'trafilatura' package is not installed. Install with: pip install trafilatura"
+        )
+
+    try:
+        resp = _requests.get(
+            url,
+            timeout=15,
+            headers={"User-Agent": "TimmyResearchBot/1.0"},
+        )
+        resp.raise_for_status()
+    except _requests.exceptions.Timeout:
+        return f"Error: request timed out after 15 seconds for {url}"
+    except _requests.exceptions.HTTPError as exc:
+        return f"Error: HTTP {exc.response.status_code} for {url}"
+    except _requests.exceptions.RequestException as exc:
+        return f"Error: failed to fetch {url} — {exc}"
+
+    text = trafilatura.extract(resp.text, include_tables=True, include_links=True)
+    if not text:
+        return f"Error: could not extract readable content from {url}"
+
+    char_budget = max_tokens * 4
+    if len(text) > char_budget:
+        text = text[:char_budget] + f"\n\n[…truncated to ~{max_tokens} tokens]"
+
+    return text
+
+
+def _register_web_fetch_tool(toolkit: Toolkit) -> None:
+    """Register the web_fetch tool for full-page content extraction."""
+    try:
+        toolkit.register(web_fetch, name="web_fetch")
+    except Exception as exc:
+        logger.warning("Tool execution failed (web_fetch registration): %s", exc)
+
+
 def _register_core_tools(toolkit: Toolkit, base_path: Path) -> None:
     """Register core execution and file tools."""
     # Python execution
@@ -672,6 +735,7 @@ def create_full_toolkit(base_dir: str | Path | None = None):
     base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
 
     _register_core_tools(toolkit, base_path)
+    _register_web_fetch_tool(toolkit)
     _register_grok_tool(toolkit)
     _register_memory_tools(toolkit)
     _register_agentic_loop_tool(toolkit)
@@ -829,6 +893,11 @@ def _analysis_tool_catalog() -> dict:
             "description": "Evaluate mathematical expressions with exact results",
             "available_in": ["orchestrator"],
         },
+        "web_fetch": {
+            "name": "Web Fetch",
+            "description": "Fetch a web page and extract clean readable text (trafilatura)",
+            "available_in": ["orchestrator"],
+        },
     }
 
 

tests/infrastructure/test_db_pool.py

@@ -242,6 +242,145 @@ class TestCloseAll:
             conn.execute("SELECT 1")
 
 
+class TestConnectionLeaks:
+    """Test that connections do not leak."""
+
+    def test_get_connection_after_close_returns_fresh_connection(self, tmp_path):
+        """After close, get_connection() returns a new working connection."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        conn1 = pool.get_connection()
+        pool.close_connection()
+
+        conn2 = pool.get_connection()
+        assert conn2 is not conn1
+        # New connection must be usable
+        cursor = conn2.execute("SELECT 1")
+        assert cursor.fetchone()[0] == 1
+        pool.close_connection()
+
+    def test_context_manager_does_not_leak_connection(self, tmp_path):
+        """After context manager exit, thread-local conn is cleared."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        with pool.connection():
+            pass
+        # Thread-local should be cleaned up
+        assert pool._local.conn is None
+
+    def test_context_manager_exception_does_not_leak_connection(self, tmp_path):
+        """Connection is cleaned up even when an exception occurs."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        try:
+            with pool.connection():
+                raise RuntimeError("boom")
+        except RuntimeError:
+            pass
+        assert pool._local.conn is None
+
+    def test_threads_do_not_leak_into_each_other(self, tmp_path):
+        """A connection opened in one thread is invisible to another."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        # Open a connection on main thread
+        pool.get_connection()
+
+        visible_from_other_thread = []
+
+        def check():
+            has_conn = hasattr(pool._local, "conn") and pool._local.conn is not None
+            visible_from_other_thread.append(has_conn)
+
+        t = threading.Thread(target=check)
+        t.start()
+        t.join()
+
+        assert visible_from_other_thread == [False]
+        pool.close_connection()
+
+    def test_repeated_open_close_cycles(self, tmp_path):
+        """Repeated open/close cycles do not accumulate leaked connections."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        for _ in range(50):
+            with pool.connection() as conn:
+                conn.execute("SELECT 1")
+            # After each cycle, connection should be cleaned up
+            assert pool._local.conn is None
+
+
+class TestPragmaApplication:
+    """Test that SQLite pragmas can be applied and persist on pooled connections.
+
+    The codebase uses WAL journal mode and busy_timeout pragmas on connections
+    obtained from the pool. These tests verify that pattern works correctly.
+    """
+
+    def test_wal_journal_mode_persists(self, tmp_path):
+        """WAL journal mode set on a pooled connection persists for its lifetime."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        conn = pool.get_connection()
+        conn.execute("PRAGMA journal_mode=WAL")
+        mode = conn.execute("PRAGMA journal_mode").fetchone()[0]
+        assert mode == "wal"
+
+        # Same connection should retain the pragma
+        same_conn = pool.get_connection()
+        mode2 = same_conn.execute("PRAGMA journal_mode").fetchone()[0]
+        assert mode2 == "wal"
+        pool.close_connection()
+
+    def test_busy_timeout_persists(self, tmp_path):
+        """busy_timeout pragma set on a pooled connection persists."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        conn = pool.get_connection()
+        conn.execute("PRAGMA busy_timeout=5000")
+        timeout = conn.execute("PRAGMA busy_timeout").fetchone()[0]
+        assert timeout == 5000
+        pool.close_connection()
+
+    def test_pragmas_apply_per_connection(self, tmp_path):
+        """Pragmas set on one thread's connection are independent of another's."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        conn_main = pool.get_connection()
+        conn_main.execute("PRAGMA cache_size=9999")
+
+        other_cache = []
+
+        def check_pragma():
+            conn = pool.get_connection()
+            # Don't set cache_size — should get the default, not 9999
+            val = conn.execute("PRAGMA cache_size").fetchone()[0]
+            other_cache.append(val)
+            pool.close_connection()
+
+        t = threading.Thread(target=check_pragma)
+        t.start()
+        t.join()
+
+        # Other thread's connection should NOT have our custom cache_size
+        assert other_cache[0] != 9999
+        pool.close_connection()
+
+    def test_session_pragma_resets_on_new_connection(self, tmp_path):
+        """Session-level pragmas (cache_size) reset on a new connection."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        conn1 = pool.get_connection()
+        conn1.execute("PRAGMA cache_size=9999")
+        assert conn1.execute("PRAGMA cache_size").fetchone()[0] == 9999
+        pool.close_connection()
+
+        conn2 = pool.get_connection()
+        cache = conn2.execute("PRAGMA cache_size").fetchone()[0]
+        # New connection gets default cache_size, not the previous value
+        assert cache != 9999
+        pool.close_connection()
+
+    def test_wal_mode_via_context_manager(self, tmp_path):
+        """WAL mode can be set within a context manager block."""
+        pool = ConnectionPool(tmp_path / "test.db")
+        with pool.connection() as conn:
+            conn.execute("PRAGMA journal_mode=WAL")
+            mode = conn.execute("PRAGMA journal_mode").fetchone()[0]
+            assert mode == "wal"
+
+
 class TestIntegration:
     """Integration tests for real-world usage patterns."""
 

tests/timmy/test_tools_web_fetch.py

@@ -0,0 +1,158 @@
+"""Unit tests for the web_fetch tool in timmy.tools."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+from timmy.tools import web_fetch
+
+
+class TestWebFetch:
+    """Tests for web_fetch function."""
+
+    def test_invalid_url_no_scheme(self):
+        """URLs without http(s) scheme are rejected."""
+        result = web_fetch("example.com")
+        assert "Error: invalid URL" in result
+
+    def test_invalid_url_empty(self):
+        """Empty URL is rejected."""
+        result = web_fetch("")
+        assert "Error: invalid URL" in result
+
+    def test_invalid_url_ftp(self):
+        """Non-HTTP schemes are rejected."""
+        result = web_fetch("ftp://example.com")
+        assert "Error: invalid URL" in result
+
+    @patch("timmy.tools.trafilatura", create=True)
+    @patch("timmy.tools._requests", create=True)
+    def test_successful_fetch(self, mock_requests, mock_trafilatura):
+        """Happy path: fetch + extract returns text."""
+        # We need to patch at import level inside the function
+        mock_resp = MagicMock()
+        mock_resp.text = "<html><body><p>Hello world</p></body></html>"
+
+        with patch.dict(
+            "sys.modules", {"requests": mock_requests, "trafilatura": mock_trafilatura}
+        ):
+            mock_requests.get.return_value = mock_resp
+            mock_requests.exceptions = _make_exceptions()
+            mock_trafilatura.extract.return_value = "Hello world"
+
+            result = web_fetch("https://example.com")
+
+        assert result == "Hello world"
+
+    @patch.dict("sys.modules", {"requests": MagicMock(), "trafilatura": MagicMock()})
+    def test_truncation(self):
+        """Long text is truncated to max_tokens * 4 chars."""
+        import sys
+
+        mock_trafilatura = sys.modules["trafilatura"]
+        mock_requests = sys.modules["requests"]
+
+        long_text = "a" * 20000
+        mock_resp = MagicMock()
+        mock_resp.text = "<html><body>" + long_text + "</body></html>"
+        mock_requests.get.return_value = mock_resp
+        mock_requests.exceptions = _make_exceptions()
+        mock_trafilatura.extract.return_value = long_text
+
+        result = web_fetch("https://example.com", max_tokens=100)
+
+        # 100 tokens * 4 chars = 400 chars max
+        assert len(result) < 500
+        assert "[…truncated" in result
+
+    @patch.dict("sys.modules", {"requests": MagicMock(), "trafilatura": MagicMock()})
+    def test_extraction_failure(self):
+        """Returns error when trafilatura can't extract text."""
+        import sys
+
+        mock_trafilatura = sys.modules["trafilatura"]
+        mock_requests = sys.modules["requests"]
+
+        mock_resp = MagicMock()
+        mock_resp.text = "<html></html>"
+        mock_requests.get.return_value = mock_resp
+        mock_requests.exceptions = _make_exceptions()
+        mock_trafilatura.extract.return_value = None
+
+        result = web_fetch("https://example.com")
+        assert "Error: could not extract" in result
+
+    @patch.dict("sys.modules", {"trafilatura": MagicMock()})
+    def test_timeout(self):
+        """Timeout errors are handled gracefully."""
+
+        mock_requests = MagicMock()
+        exc_mod = _make_exceptions()
+        mock_requests.exceptions = exc_mod
+        mock_requests.get.side_effect = exc_mod.Timeout("timed out")
+
+        with patch.dict("sys.modules", {"requests": mock_requests}):
+            result = web_fetch("https://example.com")
+
+        assert "timed out" in result
+
+    @patch.dict("sys.modules", {"trafilatura": MagicMock()})
+    def test_http_error(self):
+        """HTTP errors (404, 500, etc.) are handled gracefully."""
+
+        mock_requests = MagicMock()
+        exc_mod = _make_exceptions()
+        mock_requests.exceptions = exc_mod
+
+        mock_response = MagicMock()
+        mock_response.status_code = 404
+        mock_requests.get.return_value.raise_for_status.side_effect = exc_mod.HTTPError(
+            response=mock_response
+        )
+
+        with patch.dict("sys.modules", {"requests": mock_requests}):
+            result = web_fetch("https://example.com/nope")
+
+        assert "404" in result
+
+    def test_missing_requests(self):
+        """Graceful error when requests not installed."""
+        with patch.dict("sys.modules", {"requests": None}):
+            result = web_fetch("https://example.com")
+        assert "requests" in result and "not installed" in result
+
+    def test_missing_trafilatura(self):
+        """Graceful error when trafilatura not installed."""
+        mock_requests = MagicMock()
+        with patch.dict("sys.modules", {"requests": mock_requests, "trafilatura": None}):
+            result = web_fetch("https://example.com")
+        assert "trafilatura" in result and "not installed" in result
+
+    def test_catalog_entry_exists(self):
+        """web_fetch should appear in the tool catalog."""
+        from timmy.tools import get_all_available_tools
+
+        catalog = get_all_available_tools()
+        assert "web_fetch" in catalog
+        assert "orchestrator" in catalog["web_fetch"]["available_in"]
+
+
+def _make_exceptions():
+    """Create a mock exceptions module with real exception classes."""
+
+    class Timeout(Exception):
+        pass
+
+    class HTTPError(Exception):
+        def __init__(self, *args, response=None, **kwargs):
+            super().__init__(*args, **kwargs)
+            self.response = response
+
+    class RequestException(Exception):
+        pass
+
+    mod = MagicMock()
+    mod.Timeout = Timeout
+    mod.HTTPError = HTTPError
+    mod.RequestException = RequestException
+    return mod

tests/unit/test_research.py

@@ -1,497 +0,0 @@
-"""Unit tests for timmy.research — ResearchOrchestrator pipeline."""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from timmy.research import (
-    DEFAULT_QUERIES_PER_TOPIC,
-    MemoryInterface,
-    ResearchOrchestrator,
-    ResearchResult,
-    ResearchTools,
-    SearchSnippet,
-    _extract_action_items,
-)
-
-# ── Data structures ──────────────────────────────────────────────────────────
-
-
-class TestResearchResult:
-    def test_defaults(self):
-        r = ResearchResult(topic="test", report="content")
-        assert r.topic == "test"
-        assert r.report == "content"
-        assert r.cache_hit is False
-        assert r.queries_generated == []
-        assert r.sources == []
-        assert r.action_items == []
-        assert r.duration_ms == 0.0
-        assert r.timestamp  # non-empty
-
-    def test_with_data(self):
-        r = ResearchResult(
-            topic="AI",
-            report="report text",
-            queries_generated=["q1", "q2"],
-            sources=[{"url": "http://example.com", "title": "Test"}],
-            action_items=["Do X"],
-            cache_hit=True,
-            duration_ms=42.5,
-        )
-        assert r.cache_hit is True
-        assert len(r.sources) == 1
-        assert r.duration_ms == 42.5
-
-
-class TestSearchSnippet:
-    def test_fields(self):
-        s = SearchSnippet(title="T", url="http://x.com", snippet="text")
-        assert s.relevance == 0.0
-
-
-# ── _extract_action_items ────────────────────────────────────────────────────
-
-
-class TestExtractActionItems:
-    def test_action_prefix(self):
-        report = "Some text\nACTION: Do the thing\nMore text"
-        items = _extract_action_items(report)
-        assert items == ["Do the thing"]
-
-    def test_todo_prefix(self):
-        report = "TODO: Fix the bug\nTodo: Also this"
-        items = _extract_action_items(report)
-        assert items == ["Fix the bug", "Also this"]
-
-    def test_checkbox(self):
-        report = "- [ ] Implement feature\n- [x] Already done"
-        items = _extract_action_items(report)
-        assert items == ["Implement feature"]
-
-    def test_mixed(self):
-        report = "ACTION: First\n- [ ] Second\nTODO: Third"
-        items = _extract_action_items(report)
-        assert items == ["First", "Second", "Third"]
-
-    def test_empty(self):
-        assert _extract_action_items("No actions here") == []
-        assert _extract_action_items("") == []
-
-
-# ── MemoryInterface ──────────────────────────────────────────────────────────
-
-
-class TestMemoryInterface:
-    def test_custom_fns(self):
-        search = MagicMock(return_value=[])
-        store = MagicMock()
-        mi = MemoryInterface(search_fn=search, store_fn=store)
-        assert mi.search_fn is search
-        assert mi.store_fn is store
-
-    def test_defaults_when_import_fails(self):
-        with patch.dict("sys.modules", {"timmy.memory_system": None}):
-            mi = MemoryInterface()
-            # Should have fallback callables
-            assert callable(mi.search_fn)
-            assert callable(mi.store_fn)
-            # Fallback search returns empty
-            assert mi.search_fn("test") == []
-
-
-# ── ResearchOrchestrator ─────────────────────────────────────────────────────
-
-
-def _make_cascade(**overrides):
-    """Create a mock cascade router."""
-    cascade = AsyncMock()
-    cascade.complete = AsyncMock(
-        return_value={"content": overrides.get("content", "query1\nquery2\nquery3")}
-    )
-    return cascade
-
-
-def _make_memory(search_results=None, score=0.0):
-    """Create a mock memory interface."""
-    if search_results is None:
-        search_results = []
-    search_fn = MagicMock(return_value=search_results)
-    store_fn = MagicMock()
-    return MemoryInterface(search_fn=search_fn, store_fn=store_fn)
-
-
-def _make_tools(search_results=None, fetch_content="Page content"):
-    """Create mock research tools."""
-    web_search = MagicMock(
-        return_value=search_results
-        or [
-            {"title": "Result 1", "url": "http://a.com", "snippet": "Snippet 1"},
-            {"title": "Result 2", "url": "http://b.com", "snippet": "Snippet 2"},
-        ]
-    )
-    web_fetch = MagicMock(return_value=fetch_content)
-    return ResearchTools(web_search=web_search, web_fetch=web_fetch)
-
-
-class TestResearchOrchestratorInit:
-    def test_basic_init(self):
-        cascade = _make_cascade()
-        memory = _make_memory()
-        tools = _make_tools()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory, tools=tools)
-        assert orch.cascade is cascade
-        assert orch.memory is memory
-        assert orch.tools is tools
-        assert orch._metrics["research_cache_hit"] == 0
-        assert orch._metrics["research_api_call"] == 0
-
-
-class TestCheckLocalKnowledge:
-    @pytest.mark.asyncio
-    async def test_cache_hit(self):
-        """High-confidence memory result returns cached ResearchResult."""
-        entry = MagicMock()
-        entry.relevance_score = 0.90
-        entry.content = "Cached report"
-
-        memory = _make_memory(search_results=[entry])
-        cascade = _make_cascade()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory)
-
-        result = await orch._check_local_knowledge("test topic")
-        assert result is not None
-        assert result.cache_hit is True
-        assert result.report == "Cached report"
-
-    @pytest.mark.asyncio
-    async def test_cache_miss_low_score(self):
-        """Low-confidence result returns None."""
-        entry = MagicMock()
-        entry.relevance_score = 0.5
-        entry.content = "Weak match"
-
-        memory = _make_memory(search_results=[entry])
-        cascade = _make_cascade()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory)
-
-        result = await orch._check_local_knowledge("test topic")
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_cache_miss_empty(self):
-        """No memory results returns None."""
-        memory = _make_memory(search_results=[])
-        cascade = _make_cascade()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory)
-
-        result = await orch._check_local_knowledge("test topic")
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_exception_returns_none(self):
-        """Memory search exception returns None gracefully."""
-        memory = MemoryInterface(
-            search_fn=MagicMock(side_effect=RuntimeError("db error")),
-            store_fn=MagicMock(),
-        )
-        cascade = _make_cascade()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory)
-
-        result = await orch._check_local_knowledge("test topic")
-        assert result is None
-
-
-class TestGenerateQueries:
-    @pytest.mark.asyncio
-    async def test_parses_queries(self):
-        cascade = _make_cascade(content="query one\nquery two\nquery three")
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        queries = await orch._generate_queries("AI safety", None, None)
-        assert queries == ["query one", "query two", "query three"]
-
-    @pytest.mark.asyncio
-    async def test_strips_numbering(self):
-        cascade = _make_cascade(content="1. First query\n2. Second query\n3) Third")
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        queries = await orch._generate_queries("topic", None, None)
-        assert "First query" in queries
-        assert "Second query" in queries
-        assert "Third" in queries
-
-    @pytest.mark.asyncio
-    async def test_fallback_on_error(self):
-        cascade = AsyncMock()
-        cascade.complete = AsyncMock(side_effect=RuntimeError("LLM down"))
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        queries = await orch._generate_queries("fallback topic", None, None)
-        assert queries == ["fallback topic"]
-
-    @pytest.mark.asyncio
-    async def test_passes_cascade_tier(self):
-        cascade = _make_cascade(content="q1\nq2")
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        await orch._generate_queries("topic", None, "gpt-4")
-        call_kwargs = cascade.complete.call_args.kwargs
-        assert call_kwargs.get("model") == "gpt-4"
-
-
-class TestSearch:
-    @pytest.mark.asyncio
-    async def test_collects_snippets(self):
-        tools = _make_tools()
-        orch = ResearchOrchestrator(
-            cascade=_make_cascade(), memory=_make_memory(), tools=tools
-        )
-
-        snippets = await orch._search(["q1", "q2"])
-        # 2 results per query, 2 queries, but deduplicated by URL
-        assert len(snippets) == 2  # same URLs returned for both queries
-
-    @pytest.mark.asyncio
-    async def test_no_search_tool(self):
-        tools = ResearchTools(web_search=None)
-        orch = ResearchOrchestrator(
-            cascade=_make_cascade(), memory=_make_memory(), tools=tools
-        )
-
-        snippets = await orch._search(["q1"])
-        assert snippets == []
-
-    @pytest.mark.asyncio
-    async def test_search_error_handled(self):
-        tools = ResearchTools(
-            web_search=MagicMock(side_effect=RuntimeError("network error"))
-        )
-        orch = ResearchOrchestrator(
-            cascade=_make_cascade(), memory=_make_memory(), tools=tools
-        )
-
-        snippets = await orch._search(["q1"])
-        assert snippets == []
-
-
-class TestFetch:
-    @pytest.mark.asyncio
-    async def test_fetches_pages(self):
-        tools = _make_tools(fetch_content="Page body here")
-        orch = ResearchOrchestrator(
-            cascade=_make_cascade(), memory=_make_memory(), tools=tools
-        )
-
-        snippets = [
-            SearchSnippet(title="P1", url="http://a.com", snippet="s1"),
-            SearchSnippet(title="P2", url="http://b.com", snippet="s2"),
-        ]
-        pages = await orch._fetch(snippets)
-        assert len(pages) == 2
-        assert pages[0].content == "Page body here"
-
-    @pytest.mark.asyncio
-    async def test_no_fetch_tool(self):
-        tools = ResearchTools(web_fetch=None)
-        orch = ResearchOrchestrator(
-            cascade=_make_cascade(), memory=_make_memory(), tools=tools
-        )
-
-        pages = await orch._fetch([SearchSnippet("T", "http://x.com", "s")])
-        assert pages == []
-
-
-class TestSynthesize:
-    @pytest.mark.asyncio
-    async def test_produces_report(self):
-        cascade = _make_cascade(content="# Report\nKey findings here")
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        from timmy.research import FetchedPage
-
-        pages = [FetchedPage(url="http://x.com", title="X", content="content")]
-        report = await orch._synthesize("topic", None, pages, None)
-        assert "Report" in report
-
-    @pytest.mark.asyncio
-    async def test_fallback_on_error(self):
-        cascade = AsyncMock()
-        cascade.complete = AsyncMock(side_effect=RuntimeError("LLM error"))
-        orch = ResearchOrchestrator(cascade=cascade, memory=_make_memory())
-
-        from timmy.research import FetchedPage
-
-        pages = [FetchedPage(url="http://x.com", title="X", content="content")]
-        report = await orch._synthesize("topic", None, pages, None)
-        assert "Synthesis failed" in report
-        assert "topic" in report
-
-
-class TestCrystallize:
-    @pytest.mark.asyncio
-    async def test_stores_in_memory(self):
-        memory = _make_memory()
-        orch = ResearchOrchestrator(cascade=_make_cascade(), memory=memory)
-
-        result = ResearchResult(topic="test", report="report text")
-        await orch._crystallize("test", result)
-
-        memory.store_fn.assert_called_once()
-        call_kwargs = memory.store_fn.call_args
-        assert call_kwargs.kwargs.get("context_type") == "research"
-        assert call_kwargs.kwargs.get("source") == "research_orchestrator"
-
-    @pytest.mark.asyncio
-    async def test_store_error_handled(self):
-        memory = MemoryInterface(
-            search_fn=MagicMock(return_value=[]),
-            store_fn=MagicMock(side_effect=RuntimeError("db error")),
-        )
-        orch = ResearchOrchestrator(cascade=_make_cascade(), memory=memory)
-
-        result = ResearchResult(topic="test", report="report")
-        # Should not raise
-        await orch._crystallize("test", result)
-
-
-class TestWriteArtifact:
-    @pytest.mark.asyncio
-    async def test_no_action_items_skips(self):
-        orch = ResearchOrchestrator(cascade=_make_cascade(), memory=_make_memory())
-
-        result = ResearchResult(topic="test", report="r", action_items=[])
-        # Should complete without any calls
-        await orch._write_artifact(result)
-
-    @pytest.mark.asyncio
-    async def test_creates_issues(self):
-        orch = ResearchOrchestrator(cascade=_make_cascade(), memory=_make_memory())
-
-        result = ResearchResult(
-            topic="test", report="r", action_items=["Fix the thing"]
-        )
-        with patch("timmy.research._create_gitea_issues") as mock_create:
-            await orch._write_artifact(result)
-            mock_create.assert_called_once_with(result)
-
-
-class TestFullPipeline:
-    @pytest.mark.asyncio
-    async def test_cache_hit_short_circuits(self):
-        """When memory has a high-confidence match, skip web search."""
-        entry = MagicMock()
-        entry.relevance_score = 0.95
-        entry.content = "Previously researched content"
-
-        memory = _make_memory(search_results=[entry])
-        cascade = _make_cascade()
-        tools = _make_tools()
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory, tools=tools)
-
-        result = await orch.run("cached topic")
-        assert result.cache_hit is True
-        assert result.report == "Previously researched content"
-        # Cascade should NOT have been called (no query generation or synthesis)
-        cascade.complete.assert_not_called()
-        assert orch._metrics["research_cache_hit"] == 1
-
-    @pytest.mark.asyncio
-    async def test_full_pipeline_no_tools(self):
-        """Pipeline completes even without web tools (graceful degradation)."""
-        memory = _make_memory()
-        cascade = AsyncMock()
-        # First call: generate queries, second: synthesize
-        cascade.complete = AsyncMock(
-            side_effect=[
-                {"content": "query 1\nquery 2"},
-                {"content": "# Report\nACTION: Do something"},
-            ]
-        )
-        tools = ResearchTools()  # No web tools
-
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory, tools=tools)
-
-        with patch("timmy.research._create_gitea_issues"):
-            result = await orch.run("test topic")
-
-        assert result.topic == "test topic"
-        assert result.cache_hit is False
-        assert "Report" in result.report
-        assert result.action_items == ["Do something"]
-        assert result.duration_ms > 0
-        assert orch._metrics["research_api_call"] == 1
-        memory.store_fn.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_full_pipeline_with_tools(self):
-        """Full pipeline with search and fetch tools."""
-        memory = _make_memory()
-        cascade = AsyncMock()
-        cascade.complete = AsyncMock(
-            side_effect=[
-                {"content": "search query 1\nsearch query 2"},
-                {"content": "# Full Report\nTODO: Review findings"},
-            ]
-        )
-        tools = _make_tools()
-
-        orch = ResearchOrchestrator(cascade=cascade, memory=memory, tools=tools)
-
-        with patch("timmy.research._create_gitea_issues"):
-            result = await orch.run("test topic")
-
-        assert result.topic == "test topic"
-        assert result.cache_hit is False
-        assert len(result.queries_generated) == 2
-        assert len(result.sources) > 0
-        assert result.action_items == ["Review findings"]
-
-    @pytest.mark.asyncio
-    async def test_get_metrics(self):
-        orch = ResearchOrchestrator(cascade=_make_cascade(), memory=_make_memory())
-        metrics = orch.get_metrics()
-        assert "research_cache_hit" in metrics
-        assert "research_api_call" in metrics
-
-
-class TestCreateGiteaIssues:
-    def test_no_token_skips(self):
-        """No Gitea token configured — silently skips."""
-        from timmy.research import _create_gitea_issues
-
-        result = ResearchResult(
-            topic="t", report="r", action_items=["item"]
-        )
-        mock_settings = MagicMock()
-        mock_settings.gitea_token = ""
-        mock_settings.gitea_url = ""
-        with patch("timmy.research.settings", mock_settings):
-            # Should not raise
-            _create_gitea_issues(result)
-
-    def test_creates_issue_on_success(self):
-        from timmy.research import _create_gitea_issues
-
-        result = ResearchResult(
-            topic="AI", report="r", action_items=["Deploy model"]
-        )
-        mock_settings = MagicMock()
-        mock_settings.gitea_token = "tok"
-        mock_settings.gitea_url = "http://localhost:3000"
-        mock_settings.gitea_repo = "owner/repo"
-
-        mock_resp = MagicMock()
-        mock_resp.status_code = 201
-
-        mock_requests_mod = MagicMock()
-        mock_requests_mod.post.return_value = mock_resp
-
-        with (
-            patch("timmy.research.settings", mock_settings),
-            patch.dict("sys.modules", {"requests": mock_requests_mod}),
-        ):
-            _create_gitea_issues(result)
-            mock_requests_mod.post.assert_called_once()
-            call_kwargs = mock_requests_mod.post.call_args
-            assert "[research]" in call_kwargs.kwargs["json"]["title"]