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/joaquinbejar/ai-crew-syncWrote 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/joaquinbejar/ai-crew-sync/note)<a href="https://agentmods.dev/commands/joaquinbejar/ai-crew-sync/note"><img src="https://agentmods.dev/badge/commands/joaquinbejar/ai-crew-sync/note/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/joaquinbejar/ai-crew-sync/note"><img src="https://agentmods.dev/badge/commands/joaquinbejar/ai-crew-sync/note.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 | $0.00019 | $0.00252 |
| Opus 5.5 | $0.00008 | $0.00101 |
| Sonnet 5.5 | $0.00004 | $0.00050 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
note 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 27d 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
Use the crew bus shared notes as the team's durable memory.
Input: $ARGUMENTS
Steps:
- Parse the input:
find <query>: callsearch_noteswith the query and list the matches asscope/keywith the first line of each value.<key>alone (optionallyscope/key; scope defaults toglobal): callget_noteand show the value verbatim, with who updated it and when.<key> <text>: a write. Callget_notefirst; if the note exists, show me the current value next to the new one and ask before overwriting. Thenset_notewith the text asvalueand the tags I mention, if any.
- Values are read cold by teammates' agents: when writing, keep it self-contained (what, why, where) and do not paste secrets.
Confirm with scope/key and whether it was created or updated.
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.
- 27d ago First seen · 18 lines · 19 tokens per session scan A 25f48a69e548
note is a command published in the GitHub repository joaquinbejar/ai-crew-sync (8 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 252 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 2026-09-23.
Other commands, from other repositories
twining:export
Export Twining state as markdown for context handoff or archival.
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.