SKILLEMALL.ai

BC auto-skill-evolver

A meta-skill that continuously improves other skills through trace+feedback-driven evolution, with the goal of making skill training, status checking, and approval natural in conversation; optimized for mobile chat routing, it recognizes Chinese/English intents such as 训练技能, 技能迭代, 技能进化, 查看训练状态, train skill, evolve skill, check training status, and approve/apply proposal, then auto-runs propose/status/approve workflows safely.

ClawHub Agent Skills author: YSSHI-FPGA v1.5.1 MIT-0 9 files body ≈ 2 755 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
69
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Dangerous commands cmd-privilege SKILL.md:147
    Privilege escalation / world-writable permissions (detector / deny-list definition; security demo / example)
    - Absolute high-risk blocklist scan (e.g., `curl`, `rm -rf`, `chmod 777`, disk destructive patterns)
    detectordemo

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 59 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2755 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

  • +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
  • -32 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 429: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 59 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This skill can run user-chosen commands and propose changes to other skills, but that behavior is clearly disclosed, purpose-aligned, and gated before applying edits.
LLM: benign (medium) · VirusTotal: · 29 May 2026