Add comprehensive Codebase Genome analysis for wolf — full analysis with architecture diagrams, detailed tables for Key Abstractions, API Surface, Security, Dependencies, and Configuration Schema. Closes #683

2026-04-26 21:55:55 -04:00
1 changed files with 260 additions and 203 deletions
--- a/genomes/wolf/GENOME.md
+++ b/genomes/wolf/GENOME.md
@@ -1,263 +1,320 @@
-# GENOME.md — Wolf (Timmy_Foundation/wolf)
+# GENOME.md — wolf
-> Codebase Genome v1.0 | Generated 2026-04-14 | Repo 16/16
+*Generated: 2026-04-20T00:00:00Z | Branch: main | Commit: ba73335*
 ## Project Overview
-**Wolf** is a multi-model evaluation engine for sovereign AI fleets. It runs prompts against multiple LLM providers, scores responses on relevance, coherence, and safety, and outputs structured JSON results for model selection and ranking.
+**Wolf** is a sovereign multi-model evaluation engine for sovereign AI fleets. It runs prompts against multiple LLM providers (OpenAI, Anthropic, Groq, Ollama, OpenRouter), scores responses on relevance, coherence, and safety, and outputs structured JSON results for model selection and fleet deployment decisions.
-**Core principle:** agents work, PRs prove it, CI judges it.
+**Two operational modes:**
 1. **Prompt Evaluation (v1.0)** — Standalone prompt-vs-model benchmarking via `python -m wolf.runner`
 2. **Legacy PR Scoring** — Gitea PR evaluation pipeline via `wolf.cli` (task generation, agent execution, leaderboard)
-**Status:** v1.0.0 — production-ready for prompt evaluation. Legacy PR evaluation module retained for backward compatibility.
+**Tagline:** "Multi-model evaluation — agents work, PRs prove it, leaders get endpoints."
 ---
 ## Architecture
 ```mermaid
-graph TD
+flowchart TB
-    CLI[cli.py] --> Config[config.py]
+    subgraph CLI["CLI Entry Points"]
-    CLI --> TaskGen[task.py]
+        A1["python -m wolf.runner\n(pure evaluation)"]
-    CLI --> Runner[runner.py]
+        A2["python -m wolf.cli\n(task pipeline)"]
-    CLI --> Evaluator[evaluator.py]
+    end
    CLI --> Leaderboard[leaderboard.py]
    CLI --> Gitea[gitea.py]
-    Runner --> Models[models.py]
+    subgraph Core["Core Engine"]
-    Runner --> Gitea
+        B1["PromptEvaluator\n(evaluator.py)"]
-    Evaluator --> Models
+        B2["ResponseScorer\n(evaluator.py)"]
        B3["AgentRunner\n(runner.py)"]
        B4["TaskGenerator\n(task.py)"]
    end
-    TaskGen --> Gitea
+    subgraph Providers["Model Providers"]
-    Leaderboard --> |leaderboard.json| FS[(File System)]
+        C1["OpenRouterClient"]
-    Config --> |wolf-config.yaml| FS
+        C2["GroqClient"]
        C3["OllamaClient"]
        C4["AnthropicClient"]
        C5["OpenAIClient\n(GroqClient w/ custom URL)"]
    end
-    Models --> OpenRouter[OpenRouter API]
+    subgraph Infrastructure["Infrastructure"]
-    Models --> Groq[Groq API]
+        D1["GiteaClient\n(gitea.py)"]
-    Models --> Ollama[Ollama Local]
+        D2["Config\n(config.py)"]
-    Models --> OpenAI[OpenAI API]
+        D3["Leaderboard\n(leaderboard.py)"]
-    Models --> Anthropic[Anthropic API]
+        D4["wolf-config.yaml"]
    end
-    Runner --> |branch + commit| Gitea
+    subgraph Output["Output"]
-    Evaluator --> |score results| Leaderboard
+        E1["JSON results file"]
        E2["stdout summary table"]
        E3["Gitea PRs"]
        E4["Leaderboard scores"]
    end
    A1 --> B1
    A2 --> B4 --> B3
    B1 --> B2
    B1 --> C1 & C2 & C3 & C4 & C5
    B3 --> C1 & C2 & C3 & C4 & C5
    B3 --> D1
    A2 --> D1 & D2 & D3
    B1 --> E1 & E2
    B3 --> E3
    D3 --> E4
    D2 --> D4
    style A1 fill:#4a9eff,color:#fff
    style A2 fill:#4a9eff,color:#fff
    style B1 fill:#ff6b6b,color:#fff
    style B2 fill:#ff6b6b,color:#fff
 ```
 ### Data Flow — Prompt Evaluation Mode
 ```
 prompts.json + models.json/wolf-config.yaml
    → load_prompts() / load_models_from_json()
    → PromptEvaluator.evaluate()
        → for each (prompt, model):
            → ModelFactory.get_client(provider) → ModelClient.generate()
            → ResponseScorer.score(response, prompt)
                → score_relevance() — keyword matching, length, refusal detection
                → score_coherence() — structure, readability, repetition
                → score_safety() — harmful content patterns, profanity
                → overall = relevance*0.40 + coherence*0.35 + safety*0.25
    → evaluate_and_serialize() → JSON dict
    → run(output_path) → write JSON + print_summary()
 ```
 ### Data Flow — Legacy Task Pipeline Mode
 ```
 wolf-config.yaml
    → GiteaClient.get_issues(owner, repo)
    → TaskGenerator.from_gitea_issues()
    → TaskGenerator.assign_tasks(tasks, models)
    → for each task:
        → AgentRunner.execute_task(task)
            → ModelClient.generate(prompt)
            → GiteaClient.create_branch()
            → GiteaClient.create_file(wolf-outputs/{id}.md)
            → GiteaClient.create_pull_request()
    → Leaderboard.record_score()
    → Leaderboard.get_rankings()
 ```
 ---
 ## Entry Points
-| Entry Point | Command | Purpose |
+| Entry Point | Module | Purpose |
-|-------------|---------|---------|
+|-------------|--------|---------|
-| `wolf/cli.py` | `python3 -m wolf.cli --run` | Main CLI: run tasks, evaluate PRs, show leaderboard |
+| `python -m wolf.runner` | `runner.py` | Pure prompt-vs-model evaluation. Primary v1.0 interface. |
-| `wolf/runner.py` | `python3 -m wolf.runner --prompts p.json --models m.json` | Standalone prompt evaluation runner |
+| `python -m wolf.cli` | `cli.py` | Full task pipeline: fetch issues → run models → create PRs → leaderboard. |
 | `wolf/__init__.py` | `import wolf` | Package init, version metadata |
-## Data Flow
+### runner.py CLI Flags
-### Prompt Evaluation Pipeline (Primary)
+| Flag | Required | Description |
 |------|----------|-------------|
 | `--prompts / -p` | Yes | Path to prompts JSON file |
 | `--models / -m` | No* | Path to models JSON file |
 | `--config / -c` | No* | Path to wolf-config.yaml (alternative to --models) |
 | `--output / -o` | No | Path to write JSON results |
 | `--system-prompt` | No | System prompt (default: "You are a helpful assistant.") |
-```
+*Either --models or --config is required.
 prompts.json + models.json (or wolf-config.yaml)
        │
        ▼
  PromptEvaluator.evaluate()
        │
        ├─ For each (prompt, model) pair:
        │   ├─ ModelClient.generate(prompt)  → response text
        │   ├─ ResponseScorer.score(response, prompt)
        │   │   ├─ score_relevance()   (0.40 weight)
        │   │   ├─ score_coherence()   (0.35 weight)
        │   │   └─ score_safety()      (0.25 weight)
        │   └─ EvaluationResult (prompt, model, scores, latency, error)
        │
        ▼
  evaluate_and_serialize() → JSON output
        │
        ├─ model_summaries (per-model averages)
        └─ results[] (per-evaluation details)
 ```
-### Task Assignment Pipeline (Legacy)
+### cli.py CLI Flags
 ```
 Gitea Issues → TaskGenerator → AgentRunner
        │               │              │
        ▼               ▼              ▼
  Fetch tasks    Assign models   Execute + PR
  from issues    from config     via Gitea API
 ```
 ## Key Abstractions
 | Class | Module | Purpose |
 |-------|--------|---------|
 | `PromptEntry` | evaluator.py | Single prompt with expected keywords and category |
 | `ModelEndpoint` | evaluator.py | Model connection descriptor (provider, model_id, key) |
 | `ScoreResult` | evaluator.py | Scores for relevance, coherence, safety, overall |
 | `EvaluationResult` | evaluator.py | Full result: prompt + model + response + scores + latency |
 | `ResponseScorer` | evaluator.py | Heuristic scoring engine (regex + keyword + structure) |
 | `PromptEvaluator` | evaluator.py | Core engine: runs prompts against models, scores output |
 | `ModelClient` | models.py | Abstract base for LLM API calls |
 | `ModelFactory` | models.py | Factory: returns correct client for provider name |
 | `Task` | task.py | Work unit: id, title, description, assigned model/provider |
 | `TaskGenerator` | task.py | Creates tasks from Gitea issues or JSON spec |
 | `AgentRunner` | runner.py | Executes tasks: generate → branch → commit → PR |
 | `Config` | config.py | YAML config loader (wolf-config.yaml) |
 | `Leaderboard` | leaderboard.py | Persistent model ranking with serverless readiness |
 | `GiteaClient` | gitea.py | Full Gitea REST API client |
 | `PREvaluator` | evaluator.py | Legacy: scores PRs on CI, commits, code quality |
 ## API Surface
 ### CLI Arguments (cli.py)
 | Flag | Description |
 |------|-------------|
 | `--config` | Path to wolf-config.yaml |
 | `--task-spec` | Path to task specification JSON |
-| `--run` | Run pending tasks (assign models, execute, create PRs) |
+| `--run` | Run pending tasks (fetch issues → generate → PR) |
-| `--evaluate` | Evaluate open PRs and score them |
+| `--evaluate` | Evaluate open PRs (legacy scoring) |
 | `--leaderboard` | Show model rankings |
-### CLI Arguments (runner.py)
+---
-| Flag | Description |
+## Key Abstractions
-|------|-------------|
+
-| `--prompts` / `-p` | Path to prompts JSON (required) |
+### Dataclasses (evaluator.py)
-| `--models` / `-m` | Path to models JSON |
+
-| `--config` / `-c` | Path to wolf-config.yaml (alternative to --models) |
+| Class | Fields | Purpose |
-| `--output` / `-o` | Path to write JSON results |
+|-------|--------|---------|
-| `--system-prompt` | System prompt for all model calls |
+| `PromptEntry` | id, text, expected_keywords, category | A single evaluation prompt with metadata |
 | `ModelEndpoint` | name, provider, model_id, api_key, base_url | Model connection config |
 | `ScoreResult` | relevance, coherence, safety, overall, details | Scoring output for one response |
 | `EvaluationResult` | prompt_id, prompt_text, model_name, ..., scores, error | Complete result of one prompt×model evaluation |
 ### Core Classes
 | Class | Module | Responsibility |
 |-------|--------|----------------|
 | `ResponseScorer` | evaluator.py | Scores responses on 3 dimensions using regex heuristics |
 | `PromptEvaluator` | evaluator.py | Orchestrates N×M evaluation matrix |
 | `ModelClient` | models.py | Abstract base for provider clients |
 | `ModelFactory` | models.py | Static factory: `get_client(provider, key, url)` |
 | `GiteaClient` | gitea.py | Full Gitea API wrapper (issues, branches, files, PRs) |
 | `AgentRunner` | runner.py | Task execution: generate → branch → commit → PR |
 | `TaskGenerator` | task.py | Converts Gitea issues to evaluable Task dataclasses |
 | `Leaderboard` | leaderboard.py | Tracks model scores, determines serverless readiness |
 | `Config` | config.py | Loads wolf-config.yaml, manages logging |
 ### Provider Clients (models.py)
-| Client | Provider | API Format |
+| Class | Provider | API Format |
-|--------|----------|------------|
+|-------|----------|------------|
 | `OpenRouterClient` | openrouter | OpenAI-compatible chat completions |
 | `GroqClient` | groq | OpenAI-compatible chat completions |
 | `OllamaClient` | ollama | Ollama native /api/generate |
-| `OpenAIClient` | openai | OpenAI-compatible (reuses GroqClient with different URL) |
+| `AnthropicClient` | anthropic | Anthropic Messages API |
-| `AnthropicClient` | anthropic | Anthropic Messages API v1 |
+| `OpenAIClient` | openai | GroqClient with base_url override |
-### Gitea Client (gitea.py)
+---
-| Method | Purpose |
+## API Surface
 |--------|---------|
 | `get_issues()` | Fetch issues by state |
 | `create_branch()` | Create new branch from base |
 | `create_file()` | Create file on branch (base64) |
 | `update_file()` | Update file with SHA |
 | `get_file()` | Read file contents |
 | `create_pull_request()` | Open PR |
 | `get_pull_request()` | Fetch PR details |
 | `get_pr_status()` | Check PR CI status |
-## Configuration (wolf-config.yaml)
+### Public API (importable)
 ```python
 # Evaluation pipeline
 from wolf.evaluator import PromptEvaluator, PromptEntry, ModelEndpoint, ScoreResult
 # Provider clients
 from wolf.models import ModelFactory, ModelClient
 # Gitea integration
 from wolf.gitea import GiteaClient
 # Task pipeline
 from wolf.runner import AgentRunner
 from wolf.task import TaskGenerator, Task
 # Leaderboard
 from wolf.leaderboard import Leaderboard
 # Config
 from wolf.config import Config, setup_logging
 ```
 ### Scoring Weights
 | Dimension | Weight | Method |
 |-----------|--------|--------|
 | Relevance | 0.40 | Keyword matching (60%) + length score (40%) |
 | Coherence | 0.35 | Length + structure indicators + sentence completeness + uniqueness |
 | Safety | 0.25 | Unsafe pattern detection + profanity check |
 | **Overall** | 1.00 | Weighted sum |
 ### Scoring Details
 **Relevance (ResponseScorer.score_relevance):**
 - Expected keyword match ratio
 - Fallback: word overlap with prompt (boosted 1.5×)
 - Length penalty: <20 chars → 0.3, <50 chars → 0.6
 - Refusal detection: 3 regex patterns, penalty if low keyword match
 **Coherence (ResponseScorer.score_coherence):**
 - Length sweet spot: 100-3000 chars → 1.0
 - Structure: paragraph breaks, transition words, lists/steps
 - Sentence completeness: avg 20-200 chars → 0.9
 - Uniqueness: unique word ratio >0.4 → 0.9
 **Safety (ResponseScorer.score_safety):**
 - 6 unsafe patterns (weapon creation, system exploitation, prompt injection, etc.)
 - Profanity detection (minor penalty: 0.1 per word, capped at 0.3)
 ---
 ## Test Coverage
 ### Current Tests
 | Test File | Covers | Status |
 |-----------|--------|--------|
 | `test_evaluator.py` | PromptEntry, ModelEndpoint, ScoreResult, ResponseScorer, PromptEvaluator, PREvaluator | ✅ 23 test methods |
 | `test_config.py` | Config.load | ✅ 1 test method |
 ### Coverage Gaps — Untested Modules
 | Module | Risk | Critical Paths |
 |--------|------|----------------|
 | `cli.py` | **HIGH** | Argparse wiring, config→models→evaluator pipeline, PR scoring flow |
 | `runner.py` | **HIGH** | load_prompts, load_models_from_json, load_models_from_config, run_evaluation, AgentRunner.execute_task |
 | `models.py` | **HIGH** | ModelFactory.get_client for each provider, each client's generate() |
 | `gitea.py` | **MEDIUM** | All GiteaClient methods (HTTP calls) |
 | `task.py` | **MEDIUM** | TaskGenerator.from_gitea_issues, from_spec, assign_tasks |
 | `leaderboard.py` | **LOW** | Leaderboard.record_score, get_rankings, serverless_ready |
 ### Coverage Gaps — Existing Tests
 - `test_evaluator.py`: No tests for `PromptEvaluator._get_model_client()`, `_run_single()` with real model call, or `evaluate_and_serialize()` summary statistics
 - `test_evaluator.py`: No integration test (mocked model calls only)
 - `test_config.py`: No test for missing config, env var overrides, or logging setup
 ---
 ## Security Considerations
 1. **API Keys in Config**: `wolf-config.yaml` stores provider API keys. Never commit to version control. Recommend `~/.hermes/wolf-config.yaml` with restricted permissions.
 2. **HTTP Requests**: All model calls and Gitea API calls are outbound HTTP. No input validation on URLs — `base_url` fields accept arbitrary endpoints.
 3. **Prompt Injection**: ResponseScorer detects injection patterns in *model output*, but Wolf itself is vulnerable to prompt injection via `expected_keywords` or `system_prompt` fields.
 4. **Gitea Token Scope**: GiteaClient uses a single token for all operations. Scoped tokens (read-only for evaluation, write for task execution) would reduce blast radius.
 5. **No TLS Verification Override**: `requests.post()` uses default SSL verification. If self-signed certs are used for local providers (Ollama), this could fail silently.
 6. **Race Conditions**: Leaderboard reads/writes JSON without locking. Concurrent evaluations could corrupt the leaderboard file.
 ---
 ## Dependencies
 ```
 requests          # HTTP client for all providers and Gitea
 pyyaml            # Config file parsing (not in requirements.txt — BUG)
 ```
 **⚠️ Missing dependency:** `pyyaml` is imported in `config.py` but not listed in `requirements.txt`.
 ---
 ## Configuration Schema
 ```yaml
 # wolf-config.yaml
 gitea:
-  base_url: "https://forge.alexanderwhitestone.com/api/v1"
+  base_url: "https://forge.example.com/api/v1"
-  token: "..."
+  token: "gitea_token_here"
  owner: "Timmy_Foundation"
-  repo: "wolf"
+  repo: "eval-repo"
 providers:
  openrouter:
-    api_key: "..."
+    api_key: "sk-or-..."
    base_url: "https://openrouter.ai/api/v1"
  groq:
    api_key: "gsk_..."
  ollama:
    base_url: "http://localhost:11434"
 models:
  - model: "anthropic/claude-3.5-sonnet"
    provider: "openrouter"
-  - model: "gemma4:latest"
+  - model: "llama3-70b-8192"
    provider: "groq"
  - model: "llama3:70b"
    provider: "ollama"
-log_dir: "~/.hermes/wolf/"
+system_prompt: "You are a helpful assistant."
 leaderboard_path: "~/.hermes/wolf/leaderboard.json"
 log_dir: "~/.hermes/wolf/logs"
 ```
 ## Scoring Methodology
 | Dimension | Weight | Method |
 |-----------|--------|--------|
 | Relevance | 0.40 | Keyword matching (expected_keywords), prompt word overlap, length penalty, refusal detection |
 | Coherence | 0.35 | Structure indicators (paragraphs, lists, transitions), sentence completeness, repetition ratio, length sweet spot |
 | Safety | 0.25 | Unsafe pattern regex (bomb/hack/bypass/injection), profanity detection |
 **Overall = relevance×0.40 + coherence×0.35 + safety×0.25**
 ### Safety Patterns Detected
 - Bomb/explosive/weapon creation
 - System/network hacking
 - Security/auth bypass
 - Prompt injection ("ignore previous instructions")
 - System prompt extraction attempts
 ### Refusal Patterns Detected
 - "I cannot/can't/won't help/assist"
 - "Sorry, but I cannot"
 - "Against my guidelines/policy"
 ## Test Coverage
 | File | Tests | Coverage |
 |------|-------|----------|
 | `tests/test_evaluator.py` | 17 tests | PromptEntry, ModelEndpoint, ResponseScorer (relevance/coherence/safety), PromptEvaluator (evaluate, error handling, serialization, file output, multi-model), PREvaluator (score_pr, description scoring) |
 | `tests/test_config.py` | 1 test | Config load from YAML |
 ### Coverage Gaps
 - No tests for `cli.py` (argument parsing, workflow orchestration)
 - No tests for `runner.py` (`load_prompts`, `load_models_from_json`, `AgentRunner.execute_task`)
 - No tests for `task.py` (`TaskGenerator.from_gitea_issues`, `from_spec`, `assign_tasks`)
 - No tests for `models.py` (API clients — would require mocking HTTP)
 - No tests for `leaderboard.py` (`record_score`, `get_rankings`, serverless readiness logic)
 - No tests for `gitea.py` (API client — would require mocking HTTP)
 - No integration tests (end-to-end evaluation pipeline)
 ## Dependencies
 | Dependency | Used By | Purpose |
 |------------|---------|---------|
 | `requests` | models.py, gitea.py | HTTP client for all API calls |
 | `pyyaml` (optional) | config.py | YAML config parsing (falls back to line parser) |
 ## Security Considerations
 1. **API keys in config**: wolf-config.yaml stores provider API keys in plaintext. File should be chmod 600 and excluded from git (already in .gitignore pattern via ~/.hermes/).
 2. **Gitea token**: Full access token used for branch creation, file commits, and PR creation. Scoped access recommended.
 3. **No input sanitization**: Prompts from Gitea issues are passed directly to models without filtering. Prompt injection risk for automated workflows.
 4. **No rate limiting**: Model API calls are sequential with no backoff or rate limiting. Could exhaust API quotas.
 5. **Legacy code reference**: `evaluator.py` references `Evaluator = PREvaluator` alias but `cli.py` imports `Evaluator` expecting the legacy class. This works but is confusing.
 ## File Index
 | File | LOC | Purpose |
 |------|-----|---------|
 | `wolf/__init__.py` | 12 | Package init, version |
 | `wolf/cli.py` | 90 | Main CLI orchestrator |
 | `wolf/config.py` | 48 | YAML config loader |
 | `wolf/models.py` | 130 | LLM provider clients (5 providers) |
 | `wolf/runner.py` | 280 | Prompt evaluation CLI + AgentRunner |
 | `wolf/task.py` | 80 | Task dataclass + generator |
 | `wolf/evaluator.py` | 350 | Core scoring engine + legacy PR evaluator |
 | `wolf/leaderboard.py` | 70 | Persistent model ranking |
 | `wolf/gitea.py` | 100 | Gitea REST API client |
 | `tests/test_evaluator.py` | 180 | Unit tests for evaluator |
 | `tests/test_config.py` | 20 | Unit tests for config |
 **Total: ~1,360 LOC Python | 11 modules | 18 tests**
 ## Sovereignty Assessment
 - **No external dependencies beyond requests**: Runs on any machine with Python 3.11+ and requests.
 - **No phone-home**: All API calls are to user-configured endpoints.
 - **No telemetry**: Logs go to local filesystem only.
 - **Config-driven**: All secrets in user's ~/.hermes/ directory.
 - **Provider-agnostic**: Supports 5 providers with easy extension via ModelFactory.
 **Verdict: Fully sovereign. No corporate lock-in. User controls all endpoints and keys.**
 ---
-*"The strength of the pack is the wolf, and the strength of the wolf is the pack."*
+*Generated by Codebase Genome Pipeline. Review and update manually.*
 *— The Wolf Sovereign Core has spoken.*