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/abilityai/abilities/search-brainnpx skills add Abilityai/abilities --skill search-braingit clone --depth 1 https://github.com/Abilityai/abilitiesWhat 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 | $0.00013 | $0.00379 |
| Opus 5 | $0.00006 | $0.00189 |
| Sonnet 5 | $0.00003 | $0.00076 |
| Haiku 4.5 | $0.00001 | $0.00038 |
Grade A, and why
search-brain 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 2d 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.
What it actually says
Search Brain
Search the Brain vault for notes matching a query.
Arguments
$ARGUMENTS - The search query (keywords, tags, or phrases)
Step 1: Determine Search Type
Analyze the query:
- Tag search (starts with #): Search frontmatter tags
- Title search (quoted): Search note titles/filenames
- Content search (default): Full-text search
Step 2: Execute Search
For content search:
Grep pattern="$ARGUMENTS" path="Brain/"
For tag search:
Grep pattern="tags:.*$ARGUMENTS" path="Brain/"
For title/filename:
Glob pattern="Brain/**/*$ARGUMENTS*.md"
Step 3: Rank Results
Order results by relevance:
- Exact title matches
- Title contains query
- Content matches with context
- Tag matches
Step 4: Present Results
Format output:
## Search Results for "$ARGUMENTS"
Found X notes:
### 1. Note Title
**Path**: Brain/02-Permanent/note-title.md
**Created**: 2025-01-15
**Preview**: ...matching context with **highlighted** terms...
### 2. Another Note
...
Step 5: Suggest Actions
Based on results:
- "No results? Try
/create-note $ARGUMENTS" - "Want to see connections? Run
/find-connections <note-name>"
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.
- 2d ago First seen · 73 lines · 13 tokens per session scan A d1d3d3616cdb
search-brain is a skill published in the GitHub repository Abilityai/abilities (11 stars, last pushed 15d ago), licensed MIT. It adds 13 tokens to every session and 379 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
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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…