SKILLEMALL.ai
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Scans OpenClaw logs, config files, chat history, cursor history, behavior, desires, tastes, and drafts to take comprehensive daily and weekly notes with summaries.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 1 876 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Scans OpenClaw logs, config files, chat history, cursor history, behavior, desires, tastes, and drafts to take comprehensive daily and weekly notes with…

As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

GeneratorWriting and documentstype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
73
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-cron-mention SKILL.md:233
    Mentions editing / listing crontab
    # Add to crontab (crontab -e)

Files scanned: 3. 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")
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 62/100

  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1876 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill

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)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 163: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 30 items
  • +3Output format is stated explicitly
  • +4Has examples (11 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.