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SKILL verified MIT Self-run

Inspect Quality

skill-danielvm-git-bigpowers-inspect-quality · by danielvm-git

Interactive QA session where user reports bugs or issues conversationally, and the agent logs them to specs/bugs/registry.yaml with a structured audit schema. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, or mentions \"QA session\".

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Install

$ agentstack add skill-danielvm-git-bigpowers-inspect-quality

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

Inspect Quality

> HARD GATEHARD GATE — Quality metrics (coverage, lint, cyclomatic complexity, security scans) must be monitored. If a metric degrades, surface it as a blocker. Do NOT accept regressions.

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and log each issue to specs/bugs/registry.yaml with a structured, durable format.

For each issue the user raises

1. Listen and lightly clarify

Let the user describe the problem in their own words. Ask at most 2–3 short clarifying questions focused on:

  • What they expected vs what actually happened
  • Steps to reproduce (if not obvious)
  • Whether it's consistent or intermittent

Do NOT over-interview. If the description is clear enough to log, move on.

2. Explore the codebase in the background

Kick off an Agent (subagent_type=Explore) to understand the relevant area. The goal is NOT to find a fix — it's to:

  • Learn the domain language used in that area (check specs/UBIQUITOUS_LANGUAGE_LATEST.md if present)
  • Understand what the feature is supposed to do
  • Identify the user-facing behavior boundary

3. Assess scope: single issue or breakdown?

Break down when:

  • The fix spans multiple independent areas
  • There are clearly separable concerns that could be worked on in parallel
  • The user describes something with multiple distinct failure modes

Keep as a single issue when:

  • It's one behavior that's wrong in one place
  • The symptoms are all caused by the same root behavior

4. Log to specs/bugs/registry.yaml

Append the issue to specs/bugs/registry.yaml. Create the specs/bugs/ directory if it doesn't exist.

registry.yaml format

The file maintains a Markdown table with the following columns (derived from structured audit practice):

| Field | Description | |-------|-------------| | bug_id | BUG-YYYY-MM-DDTHHMMSS | | date | YYYY-MM-DD | | severity | critical / high / medium / low | | priority | p0 / p1 / p2 / p3 | | scope | kebab-case area (e.g. auth, checkout) | | what_happened | actual behavior (user-facing terms) | | what_expected | expected behavior | | steps_to_reproduce | numbered steps | | root_cause | one-line hypothesis | | files_changed | filled in after fix | | approach | filled in after fix | | risk_level | low / medium / high | | new_tests | count (filled in after fix) | | type_check | pass / fail (filled in after fix) | | lint | pass / fail (filled in after fix) | | commit_type | fix / fix! / feat (filled in after fix) | | release_type | patch / minor / major (filled in after fix) | | commit_message | Conventional Commits message (filled in after fix) | | follow_ups | semicolon-separated follow-up items | | file | path to detailed specs/bugs/BUG-*.md (filled in by investigate-bug) | | status | open / in-progress / fixed / wont-fix |

When a bug is fixed (via validate-fix), update the relevant row with the resolution fields.

Issue body (for context below the table)

For each bug, also append a detail section:

### BUG-YYYY-MM-DDTHHMMSS: [short title]

**What happened:** [actual behavior, plain language]
**What I expected:** [expected behavior]
**Steps to reproduce:**
1. [Step 1]
2. [Step 2]

**Additional context:** [domain-language observations, no file paths]
Rules for all entries
  • bug_id uses full timestamp: BUG-YYYY-MM-DDTHHMMSS — matches the individual bug file name in specs/bugs/
  • No file paths or line numbers — these go stale
  • Use the project's domain language (check specs/UBIQUITOUS_LANGUAGE_LATEST.md if it exists)
  • Describe behaviors, not code — "the sync service fails to apply the patch" not "applyPatch() throws"
  • Reproduction steps are mandatory — if you can't determine them, ask the user

5. Continue the session

After logging, ask: "Next issue, or are we done?" Keep going until the user says done. Each issue is independent — don't batch them.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.