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AC EvoClaw — Self-Evolving Identity Framework

Every "you" in this document refers to you, the AI agent reading this — not to the human. The human is referred to as "the human" throughout. When this document says "your SOUL.md," it means YOUR s...

modbender/skill-library-mcp Agent Skills author: modbender MIT 17 files body ≈ 12 080 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Every "you" in this document refers to you, the AI agent reading this — not to the human.

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

ProcedureInfrastructureAI and agentstype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning body-long SKILL.md body ≈ 12080 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (EvoClaw — Self-Evolving Identity Framework) differs from the folder (evoclaw)
  • 40Execution cost. Instruction body is 12080 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, write, web, python) that frontmatter does not declare
  • 65Failures and branches. 3 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 126 steps, 1 vague phrases
  • 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
  • low 15 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -218 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 126 items
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.