SKILLEMALL.ai
GitHub ↗ RU Sign in

AA kano-model-strategist

Classify product features into Kano categories. Cut waste. Build MDP over MVP. You have 30 features and 6 weeks. Sort them into Kano's six categories (Must-be, Performance, Attractive, Indifferent, Reverse, Questionable) so you ship the ones that earn their place and cut the rest.

ClawHub Agent Skills author: Monika Zapisek v0.1.1 MIT-0 10 files body ≈ 2 179 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process A 84/100 · Runs to the end — weak spots: when it triggers, running it twice

GeneratorInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
A
84/100
Runs to the end
When it triggers w 12
20
Running it twice w 4
30
Inputs and preconditions w 11
70
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

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: 10. 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 "triggers"

Process rating: all ten parameters 84/100

  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 8 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2179 tokens
  • 100Progress reporting. Reports progress

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)
  • +2Single-language instructions
  • +3Description length 282: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 26 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This appears to be a product-planning guidance skill with no evidence of malware, exfiltration, persistence, or privileged system access.
LLM: benign (medium) · VirusTotal: · 3 Jul 2026