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Git history intelligence powered by AI. Use when the user asks about git history, commit messages, branch names, repo insights, code archaeology, or wants to understand what happened in a codebase. Triggers on: "git history", "commit message", "branch name", "who changed", "what happened", "repo summary", "code archaeology", "semantic search git", "generate commit", "smart commit", "git log analysis", "contributor insights".

ClawHub Agent Skills author: Alfonso Harding Jr v1.0.1 3 files · 1 script body ≈ 864 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Git history intelligence powered by AI.

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationSoftware developmenttype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 9 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
    • 85Steps. 24 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 864 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +3Description length 428: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 24 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    Gitrama does what it claims, but it sends git repository context to Gitrama's servers, so users should only use it with repositories they are comfortable sharing.
    LLM: benign (high) · VirusTotal: suspicious · 28 May 2026