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/zhangfengcdt/memoirWrote 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/zhangfengcdt/memoir/onboard)<a href="https://agentmods.dev/commands/zhangfengcdt/memoir/onboard"><img src="https://agentmods.dev/badge/commands/zhangfengcdt/memoir/onboard.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.00248 |
| Opus 5 | $0.00032 | $0.00124 |
| Sonnet 5 | $0.00013 | $0.00050 |
| Haiku 4.5 | $0.00006 | $0.00025 |
Grade A, and why
onboard 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 8d 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
Run the memoir-onboard skill. The skill picks the right procedure for the current folder:
- git repo →
codebase:onboard(modules, goals, rules, lessons). Cold path on a fresh clone, warm path after meaningful diffs, meta-only when code HEAD hasn't moved. - non-git folder →
project:onboard(per-file structured blobs from deterministic stdlib extractors, plus a project-shape summary). Tuned for writing, video editing, bookkeeping, and other mixed-media projects. Cold/warm/meta paths keyed off a filesystem snapshot hash.
Pass --force to rewrite even when nothing has changed since the last onboarding pass.
Arguments: $ARGUMENTS
Invoke the memoir-onboard skill now, forwarding any arguments above.
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.
- 8d ago First seen · 17 lines · 64 tokens per session scan A 3725fcf002cf
onboard is a command published in the GitHub repository zhangfengcdt/memoir (610 stars, last pushed 4d ago), licensed Apache-2.0. It adds 64 tokens to every session and 248 once invoked, about $0.0003 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
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.
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.
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.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.