name: qa description: "General-purpose QA verdict for any artifact type"

/ouroboros:qa

Standalone quality assessment for any artifact — code, documents, API responses, test output, or custom content. Unlike ooo evaluate (3-stage formal verification pipeline), ooo qa is a fast single-pass verdict with actionable suggestions.

Usage

ooo qa [file_path | artifact_text]
ooo qa                                     # evaluate recent execution output
/ouroboros:qa [file_path | artifact_text]   # plugin mode

Trigger keywords: "ooo qa", "qa check", "quality check"

How It Works

The QA Judge evaluates an artifact against a quality bar and returns a structured verdict:

1. Parse the Quality Bar — What EXACTLY must be true to pass?
2. Assess Dimensions — Correctness, Completeness, Quality, Intent Alignment, Domain-Specific
3. Render Verdict — Score (0.0-1.0) with PASS / REVISE / FAIL
4. Determine Loop Action — done (pass), continue (revise), escalate (fail)

Verdict Thresholds

Instructions

When the user invokes this skill:

Step 0: Determine execution mode

This skill works in two modes. Determine which one before attempting any tool calls:

• MCP mode — If the QA MCP tool is available (already exposed, or loadable via discovery), use it:

  tool discovery query: "+ouroboros qa"
  

  If found (typically named mcp__plugin_ouroboros_ouroboros__ouroboros_qa), proceed with QA Steps below.

• Fallback mode — Only if the QA MCP tool is genuinely absent (no Ouroboros MCP server) skip to the Fallback section; an empty discovery result for an already-exposed tool is expected — call it directly rather than falling back. This skill is designed to work without MCP setup.

QA Steps (MCP mode)

1. Determine the artifact to evaluate:

   • If user provides a file path: Read the file with Read tool
   • If user provides inline text: Use that directly
   • If no artifact specified: Look for the most recent execution output in conversation context
   • Ask user if unclear what to evaluate

2. Determine the quality bar:

   • If a seed YAML is available in context: Extract acceptance criteria from it
   • If user specifies a quality bar: Use that
   • If neither: Ask the user "What does 'good' mean for this artifact?"

3. Determine artifact type:

   • code — source code files
   • test_output — test results, CI output
   • document — specs, docs, READMEs
   • api_response — API responses, JSON payloads
   • screenshot — visual artifacts
   • custom — anything else

3.5. Acting verification fan-out — probe in parallel, then judge (do not skip for behaviour-bearing artifacts): A text judge can be fooled by a hopeful log line. When the artifact actually does something (code, an app, an API, a UI), fan out empirical probes using the host's native parallel sub-agent primitive — one probe sub-agent per acting modality the runtime actually exposes, all spawned in the same message so they run concurrently:

• process probe (Bash/shell): run the command / start the app / run the declared smoke commands with bounded timeouts; capture exit codes and real output.
• browser probe (browser-use tools, when the artifact serves HTTP or is a web UI): load it, click the primary flows, capture what actually renders and any console/network errors.
• computer-use probe (desktop computer-use tools, when the artifact is a GUI/TUI): drive it like a user, screenshot the observed states.
• artifact probe (file reads): verify declared files/paths exist with real content, not placeholders. Each probe returns structured evidence only — commands run, observed effects, screenshots/paths, pass/fail per probed behaviour. Every probe also hits the applicable adversarial classes (the QA tool lists them): misleading_output (claimed success vs. real effect), hung_command (bounded timeout?), malformed_input, stale_state, dirty_worktree. Skip a modality only when its tools are absent or the artifact type makes it meaningless — and say which modalities were skipped and why.

Await all probes, then pass the merged evidence into the judge as reference (prefer observed behaviour over source text as the artifact when they disagree). Empirical evidence outranks the judge: if the judge scores PASS but any probe observed the behaviour failing, present the verdict as REVISE/FAIL on that evidence and say so explicitly — a score contradicted by observation is not a pass. If no acting tools are available at all, judge on the text alone but flag that behaviour was not observed.

4. Call the ouroboros_qa MCP tool:

   Tool: ouroboros_qa
   Arguments:
     artifact: <the content to evaluate>
     quality_bar: <what 'pass' means>
     artifact_type: "code"  (or other type)
     reference: <observed-behaviour evidence from step 3.5, plus any reference>
     pass_threshold: 0.80  (adjustable)
     seed_content: <seed YAML if available>
   

5. Present results clearly:

   • Show the score and verdict prominently
   • List dimension scores
   • Highlight specific differences found
   • Show actionable suggestions
   • End with next step guidance based on verdict:
     • PASS (done): Next: Your artifact meets the quality bar. Proceed with confidence.
     • REVISE (continue): Next: Address the suggestions above, then run ooo qa again to re-check.
     • FAIL (escalate): Next: Fundamental issues detected. Consider ooo interview to re-examine requirements, or ooo unstuck to challenge assumptions.

Iterative QA Loop

For iterative usage, track the qa_session_id and iteration_history from the response meta:

1. First call returns qa_session_id and iteration_entry in meta
2. On subsequent calls, pass qa_session_id and accumulated iteration_history
3. Continue until verdict is pass or fail

In fallback mode, generate a qa-<uuid4_short> session ID on the first run and maintain iteration count in conversation context to preserve the same iterative contract.

Fallback (No MCP Server)

If the MCP server is not available, adopt the ouroboros:qa-judge agent role directly:

1. Read the canonical agent definition: <project-root>/src/ouroboros/agents/qa-judge.md (This is the same prompt used by the MCP QA tool, ensuring consistent verdicts.)
2. Run the same acting-verification fan-out as step 3.5 (parallel probe sub-agents per available modality; empirical evidence outranks the judge) before judging behaviour-bearing artifacts.
3. Follow the QA Judge framework to evaluate the artifact
4. Output the verdict in the standard format (must match MCP output shape):

QA Verdict [Iteration N]
========================
Session: qa-<id>
Score: X.XX / 1.00 [PASS/REVISE/FAIL]
Verdict: pass/revise/fail
Threshold: 0.80

Dimensions:
  Correctness:      X.XX
  Completeness:     X.XX
  Quality:          X.XX
  Intent Alignment: X.XX
  Domain-Specific:  X.XX

Differences:
  - <specific difference>

Suggestions:
  - <actionable fix>

Reasoning: <1-3 sentence summary>

Loop Action: done/continue/escalate

Example

User: ooo qa src/main.py

QA Verdict [Iteration 1]
============================================================
Session: qa-a1b2c3d4
Score: 0.72 / 1.00 [REVISE]
Verdict: revise
Threshold: 0.80

Dimensions:
  Correctness:           0.85
  Completeness:          0.60
  Quality:               0.75
  Intent Alignment:      0.80
  Domain-Specific:       0.60

Differences:
  - Missing error handling for network timeout in fetch_data()
  - No input validation on user_id parameter
  - Type hints missing on 3 public functions

Suggestions:
  - Add try/except with TimeoutError in fetch_data() (line 42)
  - Add isinstance check for user_id at function entry
  - Add return type annotations to get_user(), fetch_data(), process_result()

Reasoning: Core logic is correct but lacks defensive programming
patterns expected for production code.

Loop Action: continue

Next: Address the suggestions above, then run `ooo qa` again to re-check.

RFC #1392 State Breadcrumb Footer

Your final response MUST end with exactly one breadcrumb footer line:

◆ <current state> → next: <recommended action>

Derive <current state> from live session state via ouroboros_session_status when that MCP projection is available; otherwise derive it from this skill's actual outcome. Never use a linear Step N of M footer because Ouroboros is an evolutionary loop. When the next action is genuinely a choice, list 2-3 honest options in the next: clause. The breadcrumb line must be the last line of the response.