SKILLEMALL.ai

AC ainative-git-workflow

Git commit, PR, and branching standards with ZERO TOLERANCE for AI attribution. Use when (1) Creating commits, (2) Writing commit messages, (3) Creating pull requests, (4) Writing PR descriptions, (5) Branching or merging code. ABSOLUTE RULE - NEVER include "Claude", "Anthropic", "AI-generated", emojis with "Generated with", or any AI tool attribution in commits, PRs, issues, or documentation.

ClawHub Agent Skills author: Toby Morning v1.0.0 MIT-0 5 files body ≈ 860 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ReferenceSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
55/100
Has gaps
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
    • 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: 5. 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 55/100

    • 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
    • 30Running it twice. 12 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 85Steps. 43 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 860 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 396: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This skill is transparent and instruction-only, but it broadly directs agents to remove AI attribution and may encourage history rewriting without clear user approval.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026