SKILLEMALL.ai
GitHub ↗ RU Sign in

AD skill-provenance

Version tracking for Agent Skills bundles and their associated files across sessions, surfaces, and platforms. Use when creating, editing, versioning, validating, packaging, or handing off a skill bundle; when checking or updating MANIFEST.yaml, CHANGELOG.md, hashes, stale evals, or frontmatter mode; and when keeping version identity with the bundle instead of filenames. Compatible with the agentskills.io open standard.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 8 files · 1 script body ≈ 4 671 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Version tracking for Agent Skills bundles and their associated files across sessions, surfaces, and platforms.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerWriting and documentsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 23 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 70Execution cost. Instruction body is 4671 tokens
    • 85Steps. 56 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • low 12 top-level sections: this looks like several domains in one skill
    • low No test case covers injection arriving through data

    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 423: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 56 items
    • +4Has examples (6 code blocks)

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