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BB brainstorm

TRIGGER when: asked to brainstorm, think through, explore, or pressure-test a technical idea or decision through conversation. Interviews adaptively, asks one question at a time, and closes with a chat recap. For technical products, architecture, infrastructure, tools, operations, and engineering workflows. Written design/specification and task-list requests belong to $kk:design; durable domain glossary/reference kits to $kk:model; implementation and fixes to $kk:implement. Not for nontechnical brainstorming.

serpro69/claude-toolbox Agent Skills author: serpro69 NOASSERTION 34 files body ≈ 1 122 tokens Open the sourcegithub.com↗ analyzed 2 h ago

TRIGGER when: asked to brainstorm, think through, explore, or pressure-test a technical idea or decision through conversation.

As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Obfuscation obf-base64-blob SKILL.md:34
      Long base64-looking blob (quoted — discussed, not commanded)
      The prerequisite-aware interview and separation of researched facts from user decisions draw on [Matt Pocock's grilling skill, pinned at `85f83d3`](https://github.com/mattpocock/skills/blob/85f83d3fde
      quoted

    Files scanned: 3. 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 71/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 7 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1122 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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 514: enough signal without eating the budget
    • +4Structure: 3 headings
    • +3Step-by-step instructions: 7 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.