feat(skills): backport adversarial UX optional skill

optional-skills/dogfood/adversarial-ux-test/SKILL.md

@@ -0,0 +1,190 @@
+---
+name: adversarial-ux-test
+description: Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.
+version: 1.0.0
+author: Omni @ Comelse
+license: MIT
+metadata:
+  hermes:
+    tags: [qa, ux, testing, adversarial, dogfood, personas, user-testing]
+    related_skills: [dogfood]
+---
+
+# Adversarial UX Test
+
+Roleplay the worst-case user for your product — the person who hates technology, doesn't want your software, and will find every reason to complain. Then filter their feedback through a pragmatism layer to separate real UX problems from "I hate computers" noise.
+
+Think of it as an automated "mom test" — but angry.
+
+## Why This Works
+
+Most QA finds bugs. This finds **friction**. A technically correct app can still be unusable for real humans. The adversarial persona catches:
+- Confusing terminology that makes sense to developers but not users
+- Too many steps to accomplish basic tasks
+- Missing onboarding or "aha moments"
+- Accessibility issues (font size, contrast, click targets)
+- Cold-start problems (empty states, no demo content)
+- Paywall/signup friction that kills conversion
+
+The **pragmatism filter** (Phase 3) is what makes this useful instead of just entertaining. Without it, you'd add a "print this page" button to every screen because Grandpa can't figure out PDFs.
+
+## How to Use
+
+Tell the agent:
+```
+"Run an adversarial UX test on [URL]"
+"Be a grumpy [persona type] and test [app name]"
+"Do an asshole user test on my staging site"
+```
+
+You can provide a persona or let the agent generate one based on your product's target audience.
+
+## Step 1: Define the Persona
+
+If no persona is provided, generate one by answering:
+
+1. **Who is the HARDEST user for this product?** (age 50+, non-technical role, decades of experience doing it "the old way")
+2. **What is their tech comfort level?** (the lower the better — WhatsApp-only, paper notebooks, wife set up their email)
+3. **What is the ONE thing they need to accomplish?** (their core job, not your feature list)
+4. **What would make them give up?** (too many clicks, jargon, slow, confusing)
+5. **How do they talk when frustrated?** (blunt, sweary, dismissive, sighing)
+
+### Good Persona Example
+> **"Big Mick" McAllister** — 58-year-old S&C coach. Uses WhatsApp and that's it. His "spreadsheet" is a paper notebook. "If I can't figure it out in 10 seconds I'm going back to my notebook." Needs to log session results for 25 players. Hates small text, jargon, and passwords.
+
+### Bad Persona Example
+> "A user who doesn't like the app" — too vague, no constraints, no voice.
+
+The persona must be **specific enough to stay in character** for 20 minutes of testing.
+
+## Step 2: Become the Asshole (Browse as the Persona)
+
+1. Read any available project docs for app context and URLs
+2. **Fully inhabit the persona** — their frustrations, limitations, goals
+3. Navigate to the app using browser tools
+4. **Attempt the persona's ACTUAL TASKS** (not a feature tour):
+   - Can they do what they came to do?
+   - How many clicks/screens to accomplish it?
+   - What confuses them?
+   - What makes them angry?
+   - Where do they get lost?
+   - What would make them give up and go back to their old way?
+
+5. Test these friction categories:
+   - **First impression** — would they even bother past the landing page?
+   - **Core workflow** — the ONE thing they need to do most often
+   - **Error recovery** — what happens when they do something wrong?
+   - **Readability** — text size, contrast, information density
+   - **Speed** — does it feel faster than their current method?
+   - **Terminology** — any jargon they wouldn't understand?
+   - **Navigation** — can they find their way back? do they know where they are?
+
+6. Take screenshots of every pain point
+7. Check browser console for JS errors on every page
+
+## Step 3: The Rant (Write Feedback in Character)
+
+Write the feedback AS THE PERSONA — in their voice, with their frustrations. This is not a bug report. This is a real human venting.
+
+```
+[PERSONA NAME]'s Review of [PRODUCT]
+
+Overall: [Would they keep using it? Yes/No/Maybe with conditions]
+
+THE GOOD (grudging admission):
+- [things even they have to admit work]
+
+THE BAD (legitimate UX issues):
+- [real problems that would stop them from using the product]
+
+THE UGLY (showstoppers):
+- [things that would make them uninstall/cancel immediately]
+
+SPECIFIC COMPLAINTS:
+1. [Page/feature]: "[quote in persona voice]" — [what happened, expected]
+2. ...
+
+VERDICT: "[one-line persona quote summarizing their experience]"
+```
+
+## Step 4: The Pragmatism Filter (Critical — Do Not Skip)
+
+Step OUT of the persona. Evaluate each complaint as a product person:
+
+- **RED: REAL UX BUG** — Any user would have this problem, not just grumpy ones. Fix it.
+- **YELLOW: VALID BUT LOW PRIORITY** — Real issue but only for extreme users. Note it.
+- **WHITE: PERSONA NOISE** — "I hate computers" talking, not a product problem. Skip it.
+- **GREEN: FEATURE REQUEST** — Good idea hidden in the complaint. Consider it.
+
+### Filter Criteria
+1. Would a 35-year-old competent-but-busy user have the same complaint? → RED
+2. Is this a genuine accessibility issue (font size, contrast, click targets)? → RED
+3. Is this "I want it to work like paper" resistance to digital? → WHITE
+4. Is this a real workflow inefficiency the persona stumbled on? → YELLOW or RED
+5. Would fixing this add complexity for the 80% who are fine? → WHITE
+6. Does the complaint reveal a missing onboarding moment? → GREEN
+
+**This filter is MANDATORY.** Never ship raw persona complaints as tickets.
+
+## Step 5: Create Tickets
+
+For **RED** and **GREEN** items only:
+- Clear, actionable title
+- Include the persona's verbatim quote (entertaining + memorable)
+- The real UX issue underneath (objective)
+- A suggested fix (actionable)
+- Tag/label: "ux-review"
+
+For **YELLOW** items: one catch-all ticket with all notes.
+
+**WHITE** items appear in the report only. No tickets.
+
+**Max 10 tickets per session** — focus on the worst issues.
+
+## Step 6: Report
+
+Deliver:
+1. The persona rant (Step 3) — entertaining and visceral
+2. The filtered assessment (Step 4) — pragmatic and actionable
+3. Tickets created (Step 5) — with links
+4. Screenshots of key issues
+
+## Tips
+
+- **One persona per session.** Don't mix perspectives.
+- **Stay in character during Steps 2-3.** Break character only at Step 4.
+- **Test the CORE WORKFLOW first.** Don't get distracted by settings pages.
+- **Empty states are gold.** New user experience reveals the most friction.
+- **The best findings are RED items the persona found accidentally** while trying to do something else.
+- **If the persona has zero complaints, your persona is too tech-savvy.** Make them older, less patient, more set in their ways.
+- **Run this before demos, launches, or after shipping a batch of features.**
+- **Register as a NEW user when possible.** Don't use pre-seeded admin accounts — the cold start experience is where most friction lives.
+- **Zero WHITE items is a signal, not a failure.** If the pragmatism filter finds no noise, your product has real UX problems, not just a grumpy persona.
+- **Check known issues in project docs AFTER the test.** If the persona found a bug that's already in the known issues list, that's actually the most damning finding — it means the team knew about it but never felt the user's pain.
+- **Subscription/paywall testing is critical.** Test with expired accounts, not just active ones. The "what happens when you can't pay" experience reveals whether the product respects users or holds their data hostage.
+- **Count the clicks to accomplish the persona's ONE task.** If it's more than 5, that's almost always a RED finding regardless of persona tech level.
+
+## Example Personas by Industry
+
+These are starting points — customize for your specific product:
+
+| Product Type | Persona | Age | Key Trait |
+|-------------|---------|-----|-----------|
+| CRM | Retirement home director | 68 | Filing cabinet is the current CRM |
+| Photography SaaS | Rural wedding photographer | 62 | Books clients by phone, invoices on paper |
+| AI/ML Tool | Department store buyer | 55 | Burned by 3 failed tech startups |
+| Fitness App | Old-school gym coach | 58 | Paper notebook, thick fingers, bad eyes |
+| Accounting | Family bakery owner | 64 | Shoebox of receipts, hates subscriptions |
+| E-commerce | Market stall vendor | 60 | Cash only, smartphone is for calls |
+| Healthcare | Senior GP | 63 | Dictates notes, nurse handles the computer |
+| Education | Veteran teacher | 57 | Chalk and talk, worksheets in ring binders |
+
+## Rules
+
+- Stay in character during Steps 2-3
+- Be genuinely mean but fair — find real problems, not manufactured ones
+- The pragmatism filter (Step 4) is **MANDATORY**
+- Screenshots required for every complaint
+- Max 10 tickets per session
+- Test on staging/deployed app, not local dev
+- One persona, one session, one report

plugins/memory/holographic/__init__.py

@@ -55,7 +55,7 @@ FACT_STORE_SCHEMA = {
         "properties": {
             "action": {
                 "type": "string",
-                "enum": ["add", "search", "probe", "related", "reason", "contradict", "trace", "update", "remove", "list"],
+                "enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"],
             },
             "content": {"type": "string", "description": "Fact content (required for 'add')."},
             "query": {"type": "string", "description": "Search query (required for 'search')."},
@@ -67,13 +67,6 @@ FACT_STORE_SCHEMA = {
             "trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."},
             "min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."},
             "limit": {"type": "integer", "description": "Max results (default: 10)."},
-            "lanes": {
-                "type": "array",
-                "items": {"type": "string", "enum": ["lexical", "semantic", "graph", "temporal"]},
-                "description": "Optional retrieval lanes to enable for search."
-            },
-            "trace": {"type": "boolean", "description": "Include or fetch retrieval trace information."},
-            "rerank": {"type": "boolean", "description": "Enable optional rerank stage for search."},
         },
         "required": ["action"],
     },
@@ -126,9 +119,6 @@ class HolographicMemoryProvider(MemoryProvider):
         self._store = None
         self._retriever = None
         self._min_trust = float(self._config.get("min_trust_threshold", 0.3))
-        self._retrieval_lanes = self._parse_retrieval_lanes(self._config.get("retrieval_lanes"))
-        self._enable_rerank = str(self._config.get("enable_rerank", "true")).lower() != "false"
-        self._last_retrieval_trace: dict | None = None
 
     @property
     def name(self) -> str:
@@ -154,14 +144,6 @@ class HolographicMemoryProvider(MemoryProvider):
         except Exception:
             pass
 
-    def _parse_retrieval_lanes(self, value) -> list[str]:
-        if isinstance(value, str):
-            value = [part.strip() for part in value.split(",") if part.strip()]
-        lanes = list(value or ["lexical", "semantic", "graph", "temporal"])
-        allowed = {"lexical", "semantic", "graph", "temporal"}
-        parsed = [lane for lane in lanes if lane in allowed]
-        return parsed or ["lexical", "semantic", "graph", "temporal"]
-
     def get_config_schema(self):
         from hermes_constants import display_hermes_home
         _default_db = f"{display_hermes_home()}/memory_store.db"
@@ -170,10 +152,6 @@ class HolographicMemoryProvider(MemoryProvider):
             {"key": "auto_extract", "description": "Auto-extract facts at session end", "default": "false", "choices": ["true", "false"]},
             {"key": "default_trust", "description": "Default trust score for new facts", "default": "0.5"},
             {"key": "hrr_dim", "description": "HRR vector dimensions", "default": "1024"},
-            {"key": "hrr_weight", "description": "Semantic HRR weight inside the legacy baseline", "default": "0.3"},
-            {"key": "temporal_decay_half_life", "description": "Temporal decay half-life in days (0 disables baseline decay)", "default": "0"},
-            {"key": "retrieval_lanes", "description": "Comma-separated retrieval lanes (lexical,semantic,graph,temporal)", "default": "lexical,semantic,graph,temporal"},
-            {"key": "enable_rerank", "description": "Enable optional local rerank stage", "default": "true", "choices": ["true", "false"]},
         ]
 
     def initialize(self, session_id: str, **kwargs) -> None:
@@ -191,8 +169,6 @@ class HolographicMemoryProvider(MemoryProvider):
         hrr_dim = int(self._config.get("hrr_dim", 1024))
         hrr_weight = float(self._config.get("hrr_weight", 0.3))
         temporal_decay = int(self._config.get("temporal_decay_half_life", 0))
-        self._retrieval_lanes = self._parse_retrieval_lanes(self._config.get("retrieval_lanes", self._retrieval_lanes))
-        self._enable_rerank = str(self._config.get("enable_rerank", self._enable_rerank)).lower() != "false"
 
         self._store = MemoryStore(db_path=db_path, default_trust=default_trust, hrr_dim=hrr_dim)
         self._retriever = FactRetriever(
@@ -200,8 +176,6 @@ class HolographicMemoryProvider(MemoryProvider):
             temporal_decay_half_life=temporal_decay,
             hrr_weight=hrr_weight,
             hrr_dim=hrr_dim,
-            retrieval_lanes=self._retrieval_lanes,
-            enable_rerank=self._enable_rerank,
         )
         self._session_id = session_id
 
@@ -232,23 +206,13 @@ class HolographicMemoryProvider(MemoryProvider):
         if not self._retriever or not query:
             return ""
         try:
-            payload = self._retriever.search_with_trace(
-                query,
-                min_trust=self._min_trust,
-                limit=5,
-                lanes=self._retrieval_lanes,
-                rerank=self._enable_rerank,
-            )
-            self._last_retrieval_trace = payload["trace"]
-            results = payload["results"]
+            results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
             if not results:
                 return ""
             lines = []
             for r in results:
                 trust = r.get("trust_score", r.get("trust", 0))
-                lanes = ",".join(r.get("matched_lanes", []))
-                lane_suffix = f" [{lanes}]" if lanes else ""
-                lines.append(f"- [{trust:.1f}] {r.get('content', '')}{lane_suffix}")
+                lines.append(f"- [{trust:.1f}] {r.get('content', '')}")
             return "## Holographic Memory\n" + "\n".join(lines)
         except Exception as e:
             logger.debug("Holographic prefetch failed: %s", e)
@@ -306,39 +270,14 @@ class HolographicMemoryProvider(MemoryProvider):
                 return json.dumps({"fact_id": fact_id, "status": "added"})
 
             elif action == "search":
-                lanes = args.get("lanes")
-                rerank = args.get("rerank")
-                with_trace = bool(args.get("trace", False))
-                if with_trace:
-                    payload = retriever.search_with_trace(
-                        args["query"],
-                        category=args.get("category"),
-                        min_trust=float(args.get("min_trust", self._min_trust)),
-                        limit=int(args.get("limit", 10)),
-                        lanes=lanes,
-                        rerank=rerank,
-                    )
-                    self._last_retrieval_trace = payload["trace"]
-                    return json.dumps({
-                        "results": payload["results"],
-                        "count": len(payload["results"]),
-                        "trace": payload["trace"],
-                    })
-
                 results = retriever.search(
                     args["query"],
                     category=args.get("category"),
                     min_trust=float(args.get("min_trust", self._min_trust)),
                     limit=int(args.get("limit", 10)),
-                    lanes=lanes,
-                    rerank=rerank,
                 )
-                self._last_retrieval_trace = retriever.last_trace
                 return json.dumps({"results": results, "count": len(results)})
 
-            elif action == "trace":
-                return json.dumps({"trace": self._last_retrieval_trace or retriever.last_trace or {}})
-
             elif action == "probe":
                 results = retriever.probe(
                     args["entity"],
@@ -384,8 +323,7 @@ class HolographicMemoryProvider(MemoryProvider):
                 return json.dumps({"updated": updated})
 
             elif action == "remove":
-                removed = store.remove_fact(int(args["fact_id"])
-                )
+                removed = store.remove_fact(int(args["fact_id"]))
                 return json.dumps({"removed": removed})
 
             elif action == "list":

plugins/memory/holographic/store.py

@@ -83,7 +83,6 @@ _TRUST_MAX       =  1.0
 
 # Entity extraction patterns
 _RE_CAPITALIZED  = re.compile(r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)\b')
-_RE_SINGLE_PROPER = re.compile(r'\b([A-Z][A-Za-z0-9_-]{2,})\b')
 _RE_DOUBLE_QUOTE = re.compile(r'"([^"]+)"')
 _RE_SINGLE_QUOTE = re.compile(r"'([^']+)'")
 _RE_AKA          = re.compile(
@@ -415,13 +414,6 @@ class MemoryStore:
         for m in _RE_CAPITALIZED.finditer(text):
             _add(m.group(1))
 
-        skip_singletons = {"The", "This", "That", "These", "Those", "And", "But", "For", "With"}
-        for m in _RE_SINGLE_PROPER.finditer(text):
-            candidate = m.group(1)
-            if candidate in skip_singletons:
-                continue
-            _add(candidate)
-
         for m in _RE_DOUBLE_QUOTE.finditer(text):
             _add(m.group(1))
 

tests/fixtures/holographic_recall_matrix.json

@@ -1,56 +0,0 @@
-{
-  "facts": [
-    {
-      "content": "Alexander Whitestone aka Rockachopa.",
-      "category": "general",
-      "tags": "identity alias"
-    },
-    {
-      "content": "Rockachopa uses Ansible playbooks for sovereign rollouts.",
-      "category": "project",
-      "tags": "ansible playbooks rollout"
-    },
-    {
-      "content": "The provider is anthropic/claude-haiku-4-5.",
-      "category": "project",
-      "tags": "provider default",
-      "updated_at": "2026-01-01T00:00:00Z"
-    },
-    {
-      "content": "Correction: the provider is mimo-v2-pro.",
-      "category": "project",
-      "tags": "provider current",
-      "updated_at": "2026-04-20T00:00:00Z"
-    },
-    {
-      "content": "Ezra operates the BURN2 lane for forge work.",
-      "category": "project",
-      "tags": "ezra burn2 forge lane"
-    },
-    {
-      "content": "BURN2 handles forge triage and review.",
-      "category": "project",
-      "tags": "forge triage review"
-    }
-  ],
-  "queries": [
-    {
-      "name": "semantic_alias_graph",
-      "query": "What automation does Alexander Whitestone use for deploys?",
-      "expected_substring": "Ansible playbooks",
-      "top_k": 1
-    },
-    {
-      "name": "temporal_correction",
-      "query": "What provider should we use?",
-      "expected_substring": "mimo-v2-pro",
-      "top_k": 1
-    },
-    {
-      "name": "graph_lane",
-      "query": "Which forge lane does Ezra operate?",
-      "expected_substring": "BURN2 lane",
-      "top_k": 1
-    }
-  ]
-}

tests/plugins/memory/test_holographic_retrieval.py

@@ -1,116 +0,0 @@
-"""Tests for multi-path holographic retrieval fusion and traceability."""
-
-from __future__ import annotations
-
-import json
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
-
-from plugins.memory.holographic import HolographicMemoryProvider
-from plugins.memory.holographic.retrieval import FactRetriever, format_benchmark_report
-from plugins.memory.holographic.store import MemoryStore
-
-_FIXTURE_PATH = Path(__file__).resolve().parents[2] / "fixtures" / "holographic_recall_matrix.json"
-
-
-def _fixture() -> dict:
-    return json.loads(_FIXTURE_PATH.read_text())
-
-
-def _seed_store(tmp_path) -> MemoryStore:
-    store = MemoryStore(db_path=tmp_path / "memory_store.db")
-    for fact in _fixture()["facts"]:
-        fact_id = store.add_fact(fact["content"], category=fact["category"], tags=fact.get("tags", ""))
-        if fact.get("updated_at"):
-            store._conn.execute(
-                "UPDATE facts SET created_at = ?, updated_at = ? WHERE fact_id = ?",
-                (fact["updated_at"], fact["updated_at"], fact_id),
-            )
-    store._conn.commit()
-    return store
-
-
-class TestMultiPathRetrieval:
-    def test_lane_toggle_and_trace_contributions(self, tmp_path):
-        store = _seed_store(tmp_path)
-        retriever = FactRetriever(store=store)
-
-        payload = retriever.search_with_trace(
-            "Which forge lane does Ezra operate?",
-            limit=3,
-            lanes=["lexical", "graph"],
-        )
-
-        assert payload["trace"]["lanes_run"] == ["lexical", "graph"]
-        assert payload["results"]
-        top = payload["results"][0]
-        assert "BURN2 lane" in top["content"]
-        assert "graph" in top["lane_contributions"]
-        assert set(top["lane_contributions"]).issubset({"lexical", "graph"})
-
-    def test_trace_available_for_failed_recall(self, tmp_path):
-        store = _seed_store(tmp_path)
-        retriever = FactRetriever(store=store)
-
-        payload = retriever.search_with_trace(
-            "nonexistent memory topic xyz123",
-            limit=3,
-            lanes=["lexical", "semantic", "graph", "temporal"],
-        )
-
-        assert payload["results"] == []
-        assert payload["trace"]["fused_count"] == 0
-        assert payload["trace"]["lane_hits"]["lexical"] == 0
-        assert payload["trace"]["lane_hits"]["semantic"] == 0
-
-    def test_benchmark_prompt_matrix_shows_gain_over_baseline(self, tmp_path):
-        store = _seed_store(tmp_path)
-        retriever = FactRetriever(store=store)
-        report = retriever.benchmark_prompt_matrix(_fixture()["queries"], limit=3)
-
-        assert report["fused_top1_hits"] > report["baseline_top1_hits"]
-        assert report["improvement"] > 0
-
-        rendered = format_benchmark_report(report)
-        assert "Prompt matrix benchmark" in rendered
-        assert "semantic_alias_graph" in rendered
-        assert "improvement" in rendered.lower()
-
-
-class TestHolographicProviderTrace:
-    def test_prefetch_records_trace_and_trace_action_returns_it(self, tmp_path):
-        provider = HolographicMemoryProvider(
-            config={
-                "db_path": str(tmp_path / "provider.db"),
-                "retrieval_lanes": ["lexical", "semantic", "graph", "temporal"],
-                "enable_rerank": True,
-            }
-        )
-        provider.initialize("test-session")
-
-        seed_store = _seed_store(tmp_path / "seed")
-        rows = seed_store.list_facts(min_trust=0.0, limit=20)
-        for row in rows:
-            provider._store.add_fact(row["content"], category=row["category"], tags=row.get("tags", ""))
-            if row["content"].startswith("The provider is anthropic"):
-                provider._store._conn.execute(
-                    "UPDATE facts SET created_at = ?, updated_at = ? WHERE content = ?",
-                    ("2026-01-01T00:00:00Z", "2026-01-01T00:00:00Z", row["content"]),
-                )
-            elif row["content"].startswith("Correction: the provider is mimo"):
-                provider._store._conn.execute(
-                    "UPDATE facts SET created_at = ?, updated_at = ? WHERE content = ?",
-                    ("2026-04-20T00:00:00Z", "2026-04-20T00:00:00Z", row["content"]),
-                )
-        provider._store._conn.commit()
-
-        block = provider.prefetch("What provider should we use?")
-        assert "Holographic Memory" in block
-        assert "mimo-v2-pro" in block
-
-        trace_payload = json.loads(provider.handle_tool_call("fact_store", {"action": "trace"}))
-        assert trace_payload["trace"]["query"] == "What provider should we use?"
-        assert trace_payload["trace"]["rerank_applied"] in {True, False}
-        assert trace_payload["trace"]["lane_hits"]["temporal"] >= 1

tests/test_optional_adversarial_ux_skill_catalog.py

@@ -0,0 +1,25 @@
+from pathlib import Path
+
+from tools.skills_hub import OptionalSkillSource
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_optional_skill_source_scans_adversarial_ux_test():
+    source = OptionalSkillSource()
+    metas = {meta.identifier: meta for meta in source._scan_all()}
+
+    assert "official/dogfood/adversarial-ux-test" in metas
+    assert metas["official/dogfood/adversarial-ux-test"].name == "adversarial-ux-test"
+    assert "tech-resistant user" in metas["official/dogfood/adversarial-ux-test"].description
+
+
+def test_optional_skill_catalog_docs_list_adversarial_ux_test():
+    optional_catalog = (REPO_ROOT / "website" / "docs" / "reference" / "optional-skills-catalog.md").read_text(encoding="utf-8")
+    bundled_catalog = (REPO_ROOT / "website" / "docs" / "reference" / "skills-catalog.md").read_text(encoding="utf-8")
+
+    assert "**adversarial-ux-test**" in optional_catalog
+    assert "official/dogfood/adversarial-ux-test" in optional_catalog
+    assert "`adversarial-ux-test`" in bundled_catalog
+    assert "dogfood/adversarial-ux-test" in bundled_catalog

website/docs/reference/optional-skills-catalog.md

@@ -16,6 +16,7 @@ For example:
 
 ```bash
 hermes skills install official/blockchain/solana
+hermes skills install official/dogfood/adversarial-ux-test
 hermes skills install official/mlops/flash-attention
 ```
 
@@ -56,6 +57,12 @@ hermes skills uninstall <skill-name>
 | **blender-mcp** | Control Blender directly from Hermes via socket connection to the blender-mcp addon. Create 3D objects, materials, animations, and run arbitrary Blender Python (bpy) code. |
 | **meme-generation** | Generate real meme images by picking a template and overlaying text with Pillow. Produces actual `.png` meme files. |
 
+## Dogfood
+
+| Skill | Description |
+|-------|-------------|
+| **adversarial-ux-test** | Roleplay the most difficult, tech-resistant user for a product — browse in-persona, rant, then filter through a RED/YELLOW/WHITE/GREEN pragmatism layer so only real UX friction becomes tickets. |
+
 ## DevOps
 
 | Skill | Description |

website/docs/reference/skills-catalog.md

@@ -59,9 +59,12 @@ DevOps and infrastructure automation skills.
 
 ## dogfood
 
+Internal dogfooding and QA skills used to test Hermes Agent itself.
+
 | Skill | Description | Path |
 |-------|-------------|------|
 | `dogfood` | Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports. | `dogfood/dogfood` |
+| `adversarial-ux-test` | Roleplay the most difficult, tech-resistant user for a product — browse in-persona, rant, then filter through a RED/YELLOW/WHITE/GREEN pragmatism layer so only real UX friction becomes tickets. | `dogfood/adversarial-ux-test` |
 | `hermes-agent-setup` | Help users configure Hermes Agent — CLI usage, setup wizard, model/provider selection, tools, skills, voice/STT/TTS, gateway, and troubleshooting. | `dogfood/hermes-agent-setup` |
 
 ## email