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AC opc-case-research

Systematic public-information research for OPC, super-individual, creator IP, and one-person business cases, with Chinese outputs focused on content strategy, IP positioning, channel strategy, business models, timelines, and replicable lessons. Use when Codex needs to research a named person, creator brand, or one-person company case; produce a case brief or structured report; map sources and evidence; compare facts versus inferences; or turn scattered public materials into a reusable research deliverable.

ClawHub Agent Skills author: cellinlab v0.1.0 MIT-0 13 files body ≈ 1 889 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

GeneratorMarketingData and analyticsInfrastructuretype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 0

    ✓ No critical or high findings

    Files scanned: 13. 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 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (opc-case-research) differs from the folder (cell-opc-case-research)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Failures and branches. 5 branches
    • 100Steps. 119 steps
    • 100Execution cost. Instruction body is 1889 tokens

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 511: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 119 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a public-source research workflow with templates and no code that would access private data, make purchases, or persist in the environment.
    LLM: benign (high) · VirusTotal: · 29 May 2026