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CC migrate-nanoclaw

Extracts user customizations from a fork, generates a replayable migration guide, and upgrades to upstream by reapplying customizations on a clean base. Replaces merge-based upgrades with intent-based migration.

nanocoai/nanoclaw Agent Skills author: nanocoai MIT 2 files body ≈ 5 887 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Extracts user customizations from a fork, generates a replayable migration guide, and upgrades to upstream by reapplying customizations on a clean base.

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions

GeneratorSoftware developmentWriting and documentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
99
Quality 40%
62
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
30
When it triggers w 12
50
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.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token diagnostics.md:16
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "api_key": "phc_…naP",
    quoted

Files scanned: 2. 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")
  • warning body-long SKILL.md body ≈ 5887 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5887 tokens
  • 85Steps. 80 steps, 1 vague phrases
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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 19 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (11 tags): a typed call is more reliable

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 211: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 80 items
  • +4Has examples (19 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.