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Use for market sizing (TAM, SAM, SOM both top-down and bottom-up), survey sample sizes or segment scoring. Runs market_sizer.py, sample_size_planner.py and segmentation_scorer.py and never returns one unsourced number.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI 11 files · 6 scripts body ≈ 2 316 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use for market sizing (TAM, SAM, SOM both top-down and bottom-up), survey sample sizes or segment scoring.

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureMarketingtype 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
D
47/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: market-research (alirezarezvani/claude-skills)

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 · 0

    ✓ No critical or high findings

    Files scanned: 11. 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 47/100

    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 29 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2316 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 218: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 29 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 6 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.