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BC rea-ci-tests-audit

Audit REA CI and test suites for correctness, fixture drift, false passes, and measured performance; use for suite or workflow investigations and efficiency changes, rather than ordinary feature testing or a single test failure.

The skillemall take

Promises to audit CI pipelines and test suites: catches fixture drift, false passes, and performance issues. Designed for suite investigations and optimization, not for debugging a single test failure.

Three files, 1628 tokens, no scripts. No critical findings, no medium-low issues either. Linter is clean. Never run on models, no sandbox testing. Quality scored 75, process 53. Has broken references or missing files.

Install if you need CI audits specifically. Keep in mind it's untested in practice, and process metrics raise questions.

morluto/rea Agent Skills author: morluto MIT 3 files body ≈ 1 628 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Audit REA CI and test suites for correctness, fixture drift, false passes, and measured performance; use for suite or workflow investigations and efficiency…

As a process C 53/100 · Has gaps — References files that are not bundled: ../../../docs/testing.md, ../../../CONTRIBUTING.md

AnalyzerSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
References files that are not bundled: ../../../docs/testing.md, ../../../CONTRIBUTING.md
Tools and files w 18
0
Result and completion w 14
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../../../docs/testing.md
  • warning missing-ref reference to a missing file: ../../../CONTRIBUTING.md

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: ../../../docs/testing.md, ../../../CONTRIBUTING.md
  • 0Tools and files. 2 referenced file(s) missing: ../../../docs/testing.md, ../../../CONTRIBUTING.md
  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 5 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1628 tokens
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 228: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 4 items
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.