SKILLEMALL.ai

AC skill-launch-doctor

Audit, score, and improve agent skills before publishing, sharing, or installing them. Use when reviewing a SKILL.md file or skill folder for trigger quality, progressive disclosure, resource references, cross-agent compatibility, safety risks, install friction, marketplace positioning, launch readiness, or when rewriting a skill description to increase installs without locking it to one agent runtime.

ClawHub Agent Skills author: haidong v0.1.0 MIT-0 7 files body ≈ 926 tokens Open the sourceclawhub.ai analyzed 2 d ago

Audit, score, and improve agent skills before publishing, sharing, or installing them.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 4. 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 57/100

    • 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. 4 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 35 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 926 tokens

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 405: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 35 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    The skill bundle contains disclosed ClawHub, Convex, documentation, and review workflows with some powerful admin and production operations, but the artifacts consistently gate those actions behind user intent, authentication, dry runs, confirmation, or explicit signoff.
    LLM: benign (high) · VirusTotal: · 20 Jun 2026