SKILLEMALL.ai
GitHub ↗ RU Sign in

AC industry-research

Multi-agent collaborative industry research for OpenClaw. Dynamically assigns research roles, runs parallel research via sessions_spawn with codex/gemini/claude CLI augmentation, iterative quality review, merge & converge, outputs structured Markdown report. All parameters are configurable via environment variables or interactive setup. Trigger: /research, 行业调研, industry research, 调研报告

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 3 536 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Multi-agent collaborative industry research for OpenClaw.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationAI and agentstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 4. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 40Consistency. Frontmatter name (industry-research) differs from the folder (multi-agent-industry-research)
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 75 steps
    • 100Execution cost. Instruction body is 3536 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 390: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 75 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.