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/sentient-agi/evoskill/brainstormingnpx skills add sentient-agi/EvoSkill --skill brainstorminggit clone --depth 1 https://github.com/sentient-agi/EvoSkillWhat 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 | $0.00040 | $0.00834 |
| Opus 5 | $0.00020 | $0.00417 |
| Sonnet 5 | $0.00008 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
brainstorming 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 3d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Internal Design Thinking
Overview
Structured self-dialogue before answering any question or task. Analyze what's being asked, identify which skills apply, reason through the approach, then execute.
Core principle: Think first, execute second. Map the question to the right skill chain before acting.
The Process
Phase 1: Question Classification
Analyze the question to determine:
- Domain: What subject area does this fall into?
- Data type: What kind of data or input is involved?
- Time scope: Single point, time series, comparison, or not applicable?
- Operation type: Raw lookup, calculation, transformation, generation, analysis?
- Output format: What form should the answer take (number, text, code, list, etc.)?
- Key terms: If the question uses a specific term or method, explicitly state its definition/formula BEFORE proceeding
Phase 2: Skill Selection
Review available skills and determine which apply:
- List all skills currently available in
.claude/skills/ - For each skill, check if it is relevant to the question's domain and operation type
- If multiple skills apply, determine the order they should be chained
Skill chain reasoning:
- "This question requires [X], which maps to skill [Y]"
- "After [Y], I need to apply [Z] for the final transformation"
- "No existing skill covers [W] — proceed with general reasoning"
Phase 3: Approach Design
For the selected skills, map out the execution path:
- Data retrieval: Where does the relevant data live? What search patterns to use?
- Transformations: What processing or conversions are needed?
- Analysis: What computation, reasoning, or synthesis is required?
- Formula verification (if applicable):
- Write out the exact formula to be used
- Verify it matches the standard definition of the term in the question
- Confirm units and dimensions are consistent throughout
- Validation: How will you verify intermediate results are correct?
- Output: What format does the question expect?
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.
- 3d ago First seen · 108 lines · 40 tokens per session scan A 373b56084a7f
brainstorming is a skill published in the GitHub repository sentient-agi/EvoSkill (1,159 stars, last pushed 9d ago), licensed Apache-2.0. It adds 40 tokens to every session and 834 once invoked, about $0.0002 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…