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/james-traina/compound-science/workflows-brainstormnpx skills add James-Traina/compound-science --skill workflows-brainstormgit clone --depth 1 https://github.com/James-Traina/compound-scienceWrote 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/james-traina/compound-science/workflows-brainstorm)<a href="https://agentmods.dev/skills/james-traina/compound-science/workflows-brainstorm"><img src="https://agentmods.dev/badge/skills/james-traina/compound-science/workflows-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 | $0.00015 | $0.01684 |
| Opus 5 | $0.00008 | $0.00842 |
| Sonnet 5 | $0.00003 | $0.00337 |
| Haiku 4.5 | $0.00002 | $0.00168 |
Grade A, and why
workflows:brainstorm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorm a Research Approach or Methodological Decision
Pipeline mode: This command operates fully autonomously. All decisions are made automatically.
Brainstorming helps answer WHAT approach to take through structured analysis. It precedes /workflows:plan, which answers HOW to implement it.
Process knowledge: See references/brainstorming-techniques.md for detailed question techniques, approach exploration patterns, and parsimony principles.
Research Question
<feature_description> #$ARGUMENTS </feature_description>
If the research question above is empty: Infer the question from recent context — open files, recent conversation, or the project's estimation code. If no context is available, state "No research question provided" and stop.
Execution Flow
Phase 0: Assess Scope
Evaluate whether brainstorming is needed based on the research question.
Clear requirements indicators:
- Specific estimator or method already chosen
- Referenced existing implementation to follow
- Described exact identification strategy
- Constrained, well-defined methodological scope
If requirements are already clear:
Skip brainstorming and note: "Requirements are detailed enough to proceed directly to planning. Run /workflows:plan to continue." Then stop.
If requirements need exploration: Proceed to Phase 1.
Phase 1: Understand the Problem
1.1 Codebase and Literature Research
Run a targeted scan to understand existing patterns and related methods:
- Task methods-explorer("Understand existing methodological patterns and approaches related to: <research_question>")
Focus on: existing estimation code, identification strategies used in this project, methodology documented in papers or notes.
1.2 Problem Decomposition
Analyze the research question systematically without user interaction:
- Core question: What is the fundamental methodological decision being made?
- Constraints: What data limitations, computational budgets, or identification requirements constrain the choice?
- Prior art: What has this project already done that's similar? What methods are established in the literature?
- Success criteria: What would a good solution look like? (e.g., consistent estimation, reasonable computational cost, testable identification)
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.
- 4d ago First seen · 215 lines · 15 tokens per session scan A ce4ae46e33c2
workflows:brainstorm is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 1,684 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…