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.
git clone --depth 1 https://github.com/SpillwaveSolutions/agent-brainWrote 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/commands/spillwavesolutions/agent-brain/agent-brain-semantic)<a href="https://agentmods.dev/commands/spillwavesolutions/agent-brain/agent-brain-semantic"><img src="https://agentmods.dev/badge/commands/spillwavesolutions/agent-brain/agent-brain-semantic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/spillwavesolutions/agent-brain/agent-brain-semantic"><img src="https://agentmods.dev/badge/commands/spillwavesolutions/agent-brain/agent-brain-semantic.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00014 | $0.01297 |
| Opus 5.5 | $0.00006 | $0.00519 |
| Sonnet 5.5 | $0.00003 | $0.00259 |
| Haiku 4.5 | $0.00001 | $0.00130 |
Grade A, and why
agent-brain-semantic 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 1mo 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Brain Semantic Search
Purpose
Performs pure semantic vector search using OpenAI embeddings. This mode finds documents based on meaning and conceptual similarity rather than exact keyword matching.
Semantic search is ideal for:
- Conceptual questions ("how does X work?")
- Finding related documentation even without exact term matches
- Natural language queries
- Discovering documents about similar concepts
Usage
/agent-brain:agent-brain-semantic <query> [--top-k <n>] [--threshold <t>]
Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
| query | Yes | - | The conceptual search query |
| --top-k, -k | No | 5 | Number of results (1-20) |
| --threshold, -t | No | 0.3 | Minimum similarity score (0.0-1.0) |
| --source-types | No | - | Filter by source type (doc,code,test) |
| --languages | No | - | Filter by programming language |
| --file-paths | No | - | Filter by file path patterns (wildcards) |
| --scores | No | false | Show individual vector/BM25 scores |
| --full | No | false | Show full text content |
| --json | No | false | Output as JSON |
| --url | No | from config | Server URL (env: AGENT_BRAIN_URL) |
When to Use Semantic Search
| Use Semantic Search | Use BM25/Keyword Instead |
|---|---|
| "how does authentication work" | "AuthenticationError" |
| "best practices for caching" | "cache_ttl_seconds" |
| "explain the data model" | "UserSchema" |
| "what is the purpose of..." | exact function names |
Execution
Pre-flight Check
Verify the server is running and has indexed documents:
agent-brain status
Expected output shows:
- Server status: healthy
- Document count: > 0
- Mode: project or shared
Search Command
agent-brain query "<query>" --mode vector --top-k <top-k> --threshold <threshold>
Examples
# Conceptual query
agent-brain query "how does the authentication system work" --mode vector
# More results for broader exploration
agent-brain query "best practices for error handling" --mode vector --top-k 10
# Higher threshold for more precise matches
agent-brain query "explain caching strategy" --mode vector --threshold 0.5
# Lower threshold to find tangentially related docs
agent-brain query "security considerations" --mode vector --threshold 0.2
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.
- 1mo ago First seen · 207 lines · 14 tokens per session scan A e145fe828191
agent-brain-semantic is a command published in the GitHub repository SpillwaveSolutions/agent-brain (119 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 1,297 once invoked, about $0.0001 per session on Opus 5.5. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.