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AC aidlc

Strict human-gated AIDLC planning for non-trivial software work, with per-gate deconfliction review.

ClawHub Agent Skills author: Michael v1.2.0 MIT-0 13 files body ≈ 2 156 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Strict human-gated AIDLC planning for non-trivial software work, with per-gate deconfliction review.

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerGitHubSoftware developmentData and analyticsWriting 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
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 · 0

✓ No critical or high findings

Files scanned: 13. 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 "homepage"

Process rating: all ten parameters 54/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 6 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (aidlc) differs from the folder (everwood-aidlc)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 85Steps. 44 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 2156 tokens
  • 100Progress reporting. Reports progress
  • low 13 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)
  • +3Description length 100: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This skill is a disclosed local planning workflow that writes approval/session notes in the workspace and uses reviewer subagents only for gated review.
LLM: benign (high) · VirusTotal: · 6 Aug 2026