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BC propagate-design-change

Finds architecture decisions (ADRs) made stale by a changed GDD and walks through resolving each. Use when the user changed a number or rule in a GDD and asks which ADRs or architecture decisions are now out of date.

Donchitos/Claude-Code-Game-Studios Claude Code author: Donchitos MIT 1 file body ≈ 2 664 tokens Open the sourcegithub.com↗ analyzed 31 h ago

Finds architecture decisions (ADRs) made stale by a changed GDD and walks through resolving each.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticsWriting 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
B
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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 Broad scope meta-dynamic-shell SKILL.md:10
      Shell command executed automatically when the skill loads (Claude Code !`cmd` preamble)
      !`bash "${CLAUDE_SKILL_DIR}/../../hooks/yaml-helper.sh" resolve_config --keys review_mode,review_gate,automation,automation_always_ask,workflow,system_overrides`

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 5 mutating operations with no state check
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 26 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2664 tokens
    • 100Progress reporting. Reports progress
    • low 10 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 216: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (6 code blocks)

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