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A errantry-framework

Mythological framework mapping Diane Duane's Young Wizards magic system to AI architecture patterns. Use when designing agent workflows or multi-agent systems, explaining AI concepts through accessible metaphor, debugging agent behavior ("what went wrong in the spell"), establishing alignment principles for new projects, or framing constraints for autonomous systems. Provides shared vocabulary, ethical guardrails as binding commitments, and heuristics for recognizing entropic patterns (drift, collapse, misalignment).

ClawHub Agent Skills author: stusatwork-oss v1.0.0 4 files body ≈ 1 428 tokens open source ↗ analyzed 31 h ago
ProcedureAI 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
A
94/100
Overall score
Safety 60%
100
Quality 40%
84
Tests bonus
0

How to improve

  1. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  2. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

Guard findings · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Lint

✓ Lint: no remarks

Process maturity 61/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 25 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1428 tokens

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 522: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 25 items
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: clean
This is a text-only conceptual framework that can shape how an agent discusses AI workflows, but it does not run code or request access to data, credentials, or systems.
LLM: benign (high) · VirusTotal: benign · 28 May 2026