SKILLEMALL.ai
GitHub ↗ RU Sign in

AF solo-init

One-time founder onboarding — generates personalized manifest, STREAM calibration, dev principles, and stack selection. Use when user says "set up solo factory", "initialize profile", "configure defaults", "first time setup", or "onboard me". Safe to re-run. Do NOT use for project scaffolding (use /scaffold).

modbender/skill-library-mcp Claude Code author: modbender MIT 3 files body ≈ 1 266 tokens Open the sourcegithub.com↗ analyzed 3 d ago

One-time founder onboarding — generates personalized manifest, STREAM calibration, dev principles, and stack selection.

As a process F 52/100 · Will not run — References files that are not bundled: templates/stacks/

GeneratorAI and agentsWriting 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
A
95/100
safety, quality, tests
Safety 60%
95
Quality 40%
95
Run on models
none yet
Process rating
F
52/100
Will not run
References files that are not bundled: templates/stacks/
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. The text references files that are not there: add them or drop the references.
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
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Grep Bash Glob Write Edit AskUserQuestion

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: templates/stacks/

Process rating: all ten parameters 52/100

Will not run. References files that are not bundled: templates/stacks/
  • 0Tools and files. 1 referenced file(s) missing: templates/stacks/
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1266 tokens
  • 100Running it twice. Mutating operations check current state

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

  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 310: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 17 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.