Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/melodic-software/claude-code-plugins/brainstormnpx skills add melodic-software/claude-code-plugins --skill brainstormgit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/melodic-software/claude-code-plugins/brainstorm)<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/brainstorm"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/brainstorm.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00102 | $0.01091 |
| Opus 5 | $0.00051 | $0.00545 |
| Sonnet 5 | $0.00020 | $0.00218 |
| Haiku 4.5 | $0.00010 | $0.00109 |
Grade A, and why
brainstorm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
The divergence step before any scoping: unknown-knowns (criteria the user only recognizes when seen) surface cheapest at candidate-list time. Finding one mid-implementation costs a re-plan. A brainstorm round also calibrates scope: reacting to a cheapest→most-ambitious spread prevents locking a scope that is too narrow (missed the high-value approach) or too wide (ambition the problem doesn't need).
Opening a fresh session on a rough problem with a brainstorm is a citable practice, not a detour: the cheapest→ambitious spread is the cheapest artifact that surfaces criteria the user only recognizes when seen (rationale and sources: docs/FINDING-YOUR-UNKNOWNS.md in the marketplace repository).
Distinct neighbors: /planning:design Phase 1 decomposes the problem space WITHIN a design task already chosen; a proactive architecture-friction scan (e.g. /architecture:improve, if installed) hunts on its own lanes; a UI-variation prototyper (e.g. /prototype:explore-directions, if installed) builds visual variations of a chosen direction. This skill is the general, problem-shaped entry upstream of all three. Creative-domain ideation owned by a domain skill (e.g. songwriting brainstorms → /songwriting:workflow, if installed) stays with that skill.
Task
Rough problem: $ARGUMENTS (if empty, infer from conversation; if nothing rough is open, say so and stop).
- Intake. Restate the problem in one sentence; if the user's starting point is unknown, ask ONE question to establish where they are. This is divergence, not an interview.
- Ground. Fast breadth pass (
Glob/Grep/targeted Read; survey the file landscape before reading anything in depth) over where the problem lives: entry points, existing mechanisms that already partially address it, prior art in the repo. - Diverge. Generate the candidate list (default ~10; scale to the problem), ordered cheapest → most ambitious. Every candidate is codebase-grounded and names the files/mechanisms it would touch, one line each: what, where, effort tier, expected impact. Do not self-censor the ambitious end; the user calibrates, not you.
- React. The user marks what resonates. A prose numbered list is the default reaction surface; for a large or multi-axis spread, offer a self-contained HTML reaction-capture page (checkable candidates + a copy-out of the selection), rendered to the topic-docs ephemeral tier, never the memory slice, even when the user opts into persisting
brainstorm.mdthere. The conversation record stays authoritative. Placement and rules:${CLAUDE_PLUGIN_ROOT}/reference/topic-docs.md. - Calibrate and hand off. From the resonating candidates, propose a scope and route onward:
/planning:prd(product intent still fuzzy),/planning:interview(engineering contract),/planning:design(type/module decisions), or a feasibility/visual spike (/prototype:pressure-test//prototype:explore-directionsif installed; otherwise a throwaway spike you write and discard). State the recommended route with its basis, marked (RECOMMENDED).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 39 lines · 102 tokens per session scan A e4d7c2fa0ba9
brainstorm is a skill published in the GitHub repository melodic-software/claude-code-plugins (15 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 1,091 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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For complex behavior: build from tiny functions, chain transformations, make code read like a pipeline of operations.
count-combinations
For probability and counting: permutations, combinations, sample spaces, Monte Carlo simulation, brute-force enumeration, card/dice problems.