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CD verify

Drive a controller/admin-UI change end-to-end from a worktree without touching the live station — isolated controller on a spare port + temp STATE_DIR, worktree Next dev server, Playwright against /admin.

The skillemall take

The skill promises end-to-end testing of controller and admin-UI changes: spin up an isolated controller on a spare port with a temp STATE_DIR, run the dev server from a worktree, and verify /admin via Playwright. Reality: one file with 391 tokens, no scripts. Quality score 70, process score 44—logic exists, but execution is rough. No critical findings reported, but no data on medium or low ones either.

You'll need to figure out the launch sequence yourself. If your worktree setup is already solid and you know where ports go, it might save time. Otherwise, expect to fill in gaps. Install only if you're ready to read code and patch what's missing.

perminder-klair/subwave Agent Skills author: perminder-klair 1 file body ≈ 391 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Drive a controller/admin-UI change end-to-end from a worktree without touching the live station — isolated controller on a spare port + temp STATEDIR…

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerPlaywrightInfrastructureAI and agentstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
44/100
Unfinished process
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

  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: 1. 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")

Process rating: all ten parameters 44/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 85Steps. 8 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 391 tokens
  • 100Running it twice. No mutating operations

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 204: enough signal without eating the budget
  • +4Structure: 3 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (2 code blocks)

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