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/brainos-hq/brain-os/patternsgit clone --depth 1 https://github.com/brainOS-HQ/brain-osWhat 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 | $0.00000 | $0.00803 |
| Opus 5 | $0.00000 | $0.00402 |
| Sonnet 5 | $0.00000 | $0.00161 |
| Haiku 4.5 | $0.00000 | $0.00080 |
Grade B, and why
patterns scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Do not moralize. State the pattern, the risk, and the action. No lectures. How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain OS : Pattern Engine
"What patterns are showing up across my work?"
Not analytics. Recognition.
REQUIRED FIRST READ
Before any tool call, read ~/.claude/brain-os/PROTOCOL.md. Tool routing is non-negotiable.
Input
Arguments: $ARGUMENTS (optional : entity/project name, "weekly" for recent patterns, "deep" for full analysis, or a specific theme). If a project name is given, focus on that project's patterns first.
Primary tool sequence
mcp__brain-os__pattern_detect(): current pattern report- If empty:
mcp__brain-os__semantic_recall("recent patterns", source_kind="pattern") - Supporting evidence:
mcp__brain-os__entity_read()for relevant entities
What to look for
1. Momentum patterns
- Which entities are moving? Which are stalled?
- Is momentum concentrated or scattered?
- Anything losing momentum that was recently high?
2. Blocker recurrence
- Same blocker across multiple entities
- Same blocker in the same entity across sessions
- Blockers noted but never resolved
3. Decision repetition
- Strategic questions that keep being reopened
- Decisions marked active but not acted on
- Similar decisions made independently across entities
4. Theme emergence
- Concepts appearing across multiple entities
- What kind of work the user keeps gravitating toward
- Shared architectural needs
5. Execution vs discussion gap
- Entities with lots of decisions but little evidence of progress
- High-status descriptions that don't match actual output
next_movefields unchanged across updates
6. Avoidance signals
- "Active" entities not updated in 3+ weeks
- Entities where
next_movekeeps changing without anything shipping - Open questions piling up without answers
7. Convergence opportunities
- Entities that could share architecture, components, or patterns
- Work in one entity that directly benefits another
Output
========================================
PATTERN REPORT
========================================
## Pattern 1: [name]
Evidence:
- [specific data point from entity_read or pattern_detect]
- [specific data point]
Interpretation:
[one paragraph max]
Risk:
[what happens if this pattern continues]
Recommendation:
[one concrete action]
Entities affected:
[list]
----------------------------------------
## Pattern 2: [name]
...
========================================
EXISTING PATTERNS : STATUS CHECK
========================================
[for each pattern already logged]
[still active? resolved? false positive?]
========================================
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 · 112 lines · 0 tokens per session scan B 9a2ac24131e8
patterns is a command published in the GitHub repository brainOS-HQ/brain-os (6 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 803 tokens. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
tree-ring-update
Check for or install a verified Tree Ring Memory CLI update without changing installation scope.
tree-ring-certify
Generate Tree Ring harness or recall-quality evidence without confusing it with the full framework release suite.
tree-ring-recall
Recall durable Tree Ring Memory context before starting or resuming work.
tree-ring-status
Check receipt-backed Tree Ring harness readiness without claiming configuration is activation.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.
release
Command "release" from melagiri/code-insights, covering /release — automated release workflow, step 1: pre-flight checks, must be on master, must have clean working tree and pull latest.