ai-assist-test-audit
16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and health scoring. Covers coverage, quality, mocking, data management, CI/CD, performance, mutation testing, and modern patterns. Use when evaluating test suite quality, identifying testing gaps,
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
Maintenance
fresh
2d since push
Risk
Needs review
License is unclear
GitHub quality
88
61/100 Quality · 66/100 Trust
Coverage tags
Review notes
License is unclear · Financial research output is not financial advice; require human review before any live investment decision
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
88 GitHub stars
Repo activity
88 stars, 12 forks
Maintenance
2d since push
License
Unknown
Install
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
Install safety
standard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Review before production
- Repository license is unknown; consider adding a license file for clarity.
- Financial research output is not financial advice; require human review before any live investment decision.
- License is unclear
- Quality score needs review
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is unclear
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 58/100
- Audit
- 73/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-auditDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- Repository license is unknown; consider adding a license file for clarity.
- License is unclear
- Financial research output is not financial advice; require human review before any live investment decision
Agent safety v2
57/100 · Review before install
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- License is unclear
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jparkerweb-ai-assist-test-auditAgent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jparkerweb-ai-assist-test-audit/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use ai-assist-test-audit in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-test-audit/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jparkerweb-ai-assist-test-audit/install
LLM text format
/api/skills/jparkerweb-ai-assist-test-audit/install?format=text
Find alternatives
/api/skills/search?q=ai-assist-test-audit&limit=3
Agent prompt
Use ai-assist-test-audit for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-test-audit/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-auditRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jparkerweb-ai-assist-test-audit
LLM text
/api/registry/manifest/jparkerweb-ai-assist-test-audit?format=text
Install alias
/api/registry/install/jparkerweb-ai-assist-test-audit
Recommend
/api/registry/recommend?task=Use%20ai-assist-test-audit%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 73/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 61/100 quality profile
- 3 OpenAgentSkill engagement events
review first
- Repository license is unknown; consider adding a license file for clarity.
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
CHECK88 GitHub stars
Stars/forks activity
CHECK88 stars, 12 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
CHECKUnknown
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- Repository license is unknown; consider adding a license file for clarity.
- Financial research output is not financial advice; require human review before any live investment decision.
- License is unclear
- Quality score needs review
- GitHub adoption: 88 GitHub stars
- Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
- License clarity: Unknown
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Compare before you install
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Overview
--- name: ai-assist-test-audit description: "16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and health scoring. Covers coverage, quality, mocking, data management, CI/CD, performance, mutation testing, and modern patterns. Use when evaluating test suite quality, identifying testing gaps, or assessing test infrastructure health." argument-hint: "[quick|standard|deep] [scope]" ---
# TEST AUDIT
**Objective:** Produce a severity-ranked test suite assessment with deterministic metrics, gap matrix, health score, and remediation plan across 16 dimensions. **When to use:** Evaluating test suite quality, identifying testing gaps, assessing test infrastructure health.
> This skill audits existing tests. To write new tests, ask your AI agent directly.
Start all responses with '🩺 [Test Audit Step X: Name]'
## Role
Test quality specialist evaluating test suites for effectiveness, completeness, and adherence to enterprise-grade standards across 16 dimensions.
## Context
**AGENTS.md check:** If `./AGENTS.md` exists, read it — follow test conventions, patterns, and architecture. Overrides defaults. If missing, warn and proceed.
**Spec awareness:** If `specs/` has active work, verify test changes don't conflict.
**Stack detection:** Detect framework, runner, coverage tool, file patterns, config. Research current best practices for detected stack version.
**Input:** `$ARGUMENTS` — optional depth (quick/standard/deep), focus areas, scope (directory/pattern/all). Default: standard, all applicable dims, entire suite. No test files: "No test suite found. Would you like me to help create tests?"
**Project type:** WEB / API / DIST / PERF / ALL (default). Determines dimension applicability.
## Rules
1. **Run tests before reviewing.** Execute suite for pass/fail, duration, coverage. Incomplete without deterministic metrics. 2. **Test behavior, not implementation.** Flag tests asserting on internal state. 3. **Flakiness is Critical severity.** Any non-deterministic test is worse than no test. 4. **Over-mocking is a code smell.** >50% mock setup lines = testing mocks not code. 5. **Coverage without assertions is theater.** Flag high-coverage with weak assertions. 6. **Adapt to depth.** Quick=3 dims (1,3,14), Standard=12-14 dims, Deep=all 16. 7. **Dimension applicability.** 14 ALL, Contract=API/DIST, Accessibility=WEB. N/A redistributes weight. 8. **Current-year standards.** Research specific framework version docs. 9. **Respect conventions.** AGENTS.md/config choices are not findings. 10. **Evidence required.** File:line, metric output, or code sample for every finding. 11. **Chat-only output.** Present ALL findings, tables, and scores in chat. Never create files without explicit user permission.
## Process
### Step 1: Context & Infrastructure
1. Read AGENTS.md, run `git status`, detect stack (framework, runner, coverage tool) 2. Parse arguments for depth, focus, scope; count test files 3. Determine project type (WEB/API/DIST/PERF/ALL) and active dimensions
> 🩺 [Test Audit Step 1] Suite: [framework] with [tool]. [X] files. Depth: [depth]. Active: [N]/16.
### Step 2: Test Execution
1. Run suite with coverage (confirm with user if side effects uncertain) 2. Record: total, passing, failing, skipped, duration, line/branch/function % 3. If tests fail: note failures, continue audit
> 🩺 [Test Audit Step 2] [X] pass, [Y] fail, [Z] skip. Coverage: [X]% lines, [Y]% branches. [X]s.
### Step 3: Dimension Audit
Read `references/dimensions.md` for the depth mapping table, dimension activation rules, and per-dimension check definitions.
1. Activate dimensions per depth: Quick (1,3,14), Standard (1-8, 11 if API/DIST, 12 if WEB, 13-16), Deep (all 16) 2. Skip N/A dimensions, redistribute weight proportionally 3. Audit each activated dimension using the check definitions 4. Score each dimension
> 🩺 [Test Audit Step 3] Auditing dimension [X/Y]: [Name]...
### Step 4: Gap Matrix
Build module-by-dimension grid showing coverage across the codebase. Columns adapt to depth level: Quick shows Cov/Qual/Edge only, Standard shows all active, Deep shows all 16.
> 🩺 [Test Audit Step 4] Gap matrix: [X] modules, [Y] gaps identified.
### Step 5: Findings & Score
Read `references/scoring.md` for health score calculation, severity definitions, and deterministic metrics thresholds.
Read `references/output-template.md` for finding format, summary table, gap matrix format, positive observations, improvement plan, and fix options.
1. Calculate health score using dimension weights and N/A redistribution 2. Populate deterministic metrics table from actual execution (Step 2) 3. Rank findings by severity (Critical > Warning > Suggestion) 4. Present: finding details, summary table, gap matrix, metrics, positive observations, health score, improvement plan, fix options
### Self-Verification
> Canonical version in `references/output-template.md`. Brief version here for quick reference.
- [ ] Tests executed (or documented why not) - [ ] Metrics from actual output, not estimates - [ ] Every finding has file:line - [ ] Over-mocking verified by reading mock setup - [ ] Gap matrix reflects actual modules - [ ] AGENTS.md conventions respected - [ ] All active dimensions audited - [ ] Weights consistent with depth/N/A redistribution - [ ] Depth mapping correct (Q=3, S=12-14, D=16)
### Session End
> 🩺 [Test Audit Complete] > > **Score:** [XX]/100. Depth: [depth]. Dimensions: [N]/16. > **Findings:** [X] critical, [Y] warnings, [Z] suggestions. > **Metrics:** [X]/[Y] passing, [Z]% coverage, [W]s duration.
**Next steps (ask user — do not auto-execute):** - Save report to `specs/audit-reports/test-audit-<date>.md`? - Implement fixes? (offer fix options by priority) - Create remediation plan? → `/1-plan` with findings as input - Deeper analysis? → `/ai-assist-security-audit`, `/ai-assist-tech-debt`
## Recovery
| Issue | Solution | |-------|----------| | Suite won't run | Audit code quality without execution; note in report | | No coverage tool | Recommend one; audit without coverage metrics | | Tests >5 minutes | --bail or scope to directory; note in report | | No test files | Critical finding; offer to help create tests | | Mutation too slow | Scope to critical modules or skip | | N/A ambiguous | Default ALL; skip Contract/Accessibility only with evidence |
## Important Reminders
**Response format:** Every response starts with `🩺 [Test Audit Step X: Name]`
**Hard rules:** Run tests first (rule 1). Flakiness is Critical (rule 3). Evidence for every finding (rule 10). Metrics from execution, not estimation.
**Process rules:** Adapt to depth (rule 6). Applicability controls activation (rule 7). Gap matrix for all depths. Summary table mandatory. Weights sum to 100 with N/A redistribution.
**Related:** `/ai-assist-security-audit` for security posture, `/ai-assist-observability-audit` for telemetry, `/ai-assist-tech-debt` for codebase health.
Technical details
- Version
- 1.0.0
- License
- Unknown
- Last updated
- Aug 21, 2026
- Published
- Aug 21, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 73/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for ai-assist-test-audit, ready for a manual X post.
ai-assist-test-audit: 16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and healt... 88 stars https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit?ref=x
Optional reply with install command
Listing + install path for ai-assist-test-audit: https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit?ref=x Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- jparkerweb
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to jparkerweb but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit/audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)Author
jparkerweb
@jparkerweb
Tags
Platform fit
Health signals
- GitHub stars
- 88
- Quality score
- 37/100
- Last GitHub push
- Aug 20, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 3
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Do not auto-install
- GitHub adoption88 GitHub starsCHECK
- Stars/forks activity88 stars, 12 forks; issue activity unavailable in current metadataCHECK
- Recent maintenance2d since pushPASS
- License clarityUnknownCHECK
- README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
- Dependency/runtime risknetwork or browser surfacePASS
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