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AA skill-sentinel

Auditoria e evolucao do ecossistema de skills. Qualidade de codigo, seguranca, custos, gaps, duplicacoes, dependencias e relatorios de saude.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 22 files body ≈ 1 913 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Auditoria e evolucao do ecossistema de skills.

As a process A 81/100 · Runs to the end — weak spots: result and completion, running it twice

AnalyzerQuality controltype 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
A
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
A
81/100
Runs to the end
Running it twice w 4
30
Result and completion w 14
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: skill-sentinel (sickn33/agentic-awesome-skills)

How to improve

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-password-literal references/security_patterns.md:44
      Hard-coded password / key literal (may be an example)
      API_KEY = "sk-a…456"
    • low Secrets in code secret-password-literal references/security_patterns.md:45
      Hard-coded password / key literal (may be an example)
      PASSWORD = "minh…123"

    Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 81/100

    • 30Running it twice. 1 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 68 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1913 tokens
    • 100Progress reporting. Reports progress
    • low 25 top-level sections: this looks like several domains in one skill

    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
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 141: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 68 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 8 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.