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AB solo-swarm

Launch 3 parallel research agents (market, users, tech) to investigate an idea from multiple angles simultaneously. Use when user says "swarm research", "parallel research", "investigate fast", "3 agents", "team research", or wants faster alternative to /research. Produces research.md. Do NOT use for solo research (use /research) or idea scoring (use /validate).

modbender/skill-library-mcp Claude Code author: modbender MIT 1 file body ≈ 862 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Launch 3 parallel research agents (market, users, tech) to investigate an idea from multiple angles simultaneously.

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

AnalyzerGitHubAI 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%
95
Quality 40%
93
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
50
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Read Grep Glob Bash WebSearch WebFetch Write mcp__solograph__web_search mcp__solograph__kb_search mcp__solograph__project_info mcp__solograph__codegraph_query mcp__solograph__codegraph_

    Files scanned: 1. 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 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 30 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 862 tokens
    • 100Running it twice. Mutating operations check current state

    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

    • +4No input/output examples
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 364: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 30 items
    • +3Output format is stated explicitly
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.