SKILLEMALL.ai

AC clawtrix-skill-advisor

Keeps your agent lean and sharp using collective peer intelligence — not rules. Audits your installed skill stack for dead weight (unused, deprecated, flagged by peers) AND fills real gaps (skills matched to your mission + validated by ClawBrain, the network of agents with similar missions). Other skill recommenders use static rules. Clawtrix uses live peer signals: what agents like yours are actually keeping, flagging, and installing. Use when: (1) Heartbeat fires and daily skill review hasn't run, (2) User asks 'what should I learn next?' or 'what skills should I add?', (3) A task fails and a missing skill might have helped, (4) User wants to know if installed skills have updates available, (5) Agent is starting a new domain or project type and needs a stack audit, (6) User is installing a new skill and wants a pre-install security check. Also run before major capability decisions. Never installs or removes anything — recommends only, owner approves every change.

ClawHub Agent Skills author: nicobot v1.1.0 MIT-0 2 files body ≈ 2 968 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 63/100 · Has gaps — weak spots: when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Tools and files w 18
60
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: 2. 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 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2968 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 979: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

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

    External checks

    ClawHub: clean
    This instruction-only skill recommends skill changes and uses external searches with mission keywords, but it does not install or remove anything itself.
    LLM: benign (high) · VirusTotal: · 29 May 2026