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 commands/spillwavesolutions/agent-brain/agent-brain-cachegit clone --depth 1 https://github.com/SpillwaveSolutions/agent-brainWhat 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 | $0.01576 |
| Opus 5 | $0.00006 | $0.00788 |
| Sonnet 5 | $0.00003 | $0.00315 |
| Haiku 4.5 | $0.00001 | $0.00158 |
Grade A, and why
agent-brain-cache 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.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Brain Cache Management
Purpose
Manage the embedding cache used by Agent Brain to avoid redundant OpenAI API calls:
- status — View hit rate, entry counts, and cache size to understand cache health.
- clear — Flush all cached embeddings to force fresh computation on the next reindex.
The embedding cache is automatic — it requires no setup. Use this command to monitor it and clear it when changing embedding providers or models.
Usage
/agent-brain:agent-brain-cache status [--json] [--url <url>]
/agent-brain:agent-brain-cache clear [--yes] [--url <url>]
Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
| subcommand | Yes | - | Operation: status or clear |
| --yes | No | false | Skip confirmation prompt (clear only) |
| --json | No | false | Output in JSON format (status only) |
| --url | No | AGENT_BRAIN_URL or http://127.0.0.1:8000 | Server URL override |
Examples
/agent-brain:agent-brain-cache status # Show cache metrics (human-readable)
/agent-brain:agent-brain-cache status --json # Show metrics as JSON
/agent-brain:agent-brain-cache clear # Clear cache (prompts for confirmation)
/agent-brain:agent-brain-cache clear --yes # Clear cache (skips confirmation)
Execution: Status Path
Step 1: Run Cache Status
agent-brain cache status
For JSON output (useful for scripting):
agent-brain cache status --json
Expected Output
Metric Value
──────────────── ──────
Entries (disk) 1,234
Entries (memory) 500
Hit Rate 87.3%
Hits 5,432
Misses 800
Size 14.81 MB
Interpreting Metrics
| Metric | Description |
|---|---|
| Entries (disk) | Total embeddings persisted in the SQLite cache database |
| Entries (memory) | Embeddings currently held in the in-memory LRU (fastest tier) |
| Hit Rate | Percentage of embedding lookups served from cache (higher is better) |
| Hits | Total successful cache lookups this session |
| Misses | Total cache misses (embedding had to be computed via API) |
| Size | Total disk space used by the cache database |
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 · 221 lines · 13 tokens per session scan A 412753887eae
agent-brain-cache is a command published in the GitHub repository SpillwaveSolutions/agent-brain (117 stars, last pushed 2d ago), licensed MIT. It adds 13 tokens to every session and 1,576 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 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.