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CC induction-audit

Audit research-corpus induction quality — depth bands (stub/compact/good/full/deep), structural integrity (analysis doc + sidecar + PDF, honoring audit-exclude conventions), and per-source-type required sections (a blog isn't flagged for missing Benchmark Results). Plus additive frontmatter backfill for legacy docs. Runs via `aiwg corpus induction-audit` / `aiwg corpus frontmatter-backfill`.

jmagly/aiwg Agent Skills author: jmagly 1 file body ≈ 667 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Audit research-corpus induction quality — depth bands (stub/compact/good/full/deep), structural integrity (analysis doc + sidecar + PDF, honoring…

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerData and analyticsFinancetype 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
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: induction-audit (jmagly/aiwg)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "namespace"
  • note frontmatter-key unknown frontmatter key "commandHint"
  • note edit-residue the text marks something as outdated (lines 5, 22, 30): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 50/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 10 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 667 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 394: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (1 code blocks)

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