Borrowing it
Nothing to install: this file belongs to AIOSAI/AIPass. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AIOSAI/AIPass/main/.codex/skills/memo/SKILL.mdgit clone --depth 1 https://github.com/AIOSAI/AIPassWrote 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/skills/aiosai/aipass/memo)<a href="https://agentmods.dev/skills/aiosai/aipass/memo"><img src="https://agentmods.dev/badge/skills/aiosai/aipass/memo.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 | $0.00028 | $0.00553 |
| Opus 5 | $0.00014 | $0.00277 |
| Sonnet 5 | $0.00006 | $0.00111 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
memo 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 7d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Update
Purpose: Update branch memory files after completing work this session.
Execution
- Read
.trinity/passport.jsonfirst — re-absorb your identity, role, and principles before writing memories - Review what was done this session (context, recent changes, key decisions)
- Update each file below as needed
- Confirm completion — list files updated
What to Update
Always
- .trinity/local.json — Add a session entry to
sessionsif significant work was done; addkey_learningsfor facts you'd need next time. Todos: add what you parked, and DELETE every todo you finished this session — the proof goes in the session entry, not the todo. Rollover never trims todos (they're operational), so done ones you leave behind resurface as "open" next load and you waste time re-confirming them. - .trinity/observations.json — Add notable collaboration insights: breakthrough moments, pattern corrections, flow states, friction points, preference discoveries. Skip if nothing notable this session.
Entry shape — one rule for all four types
key_learnings, sessions, todos (local.json) and observations (observations.json) all share ONE shape: a list of objects, newest at the top (index 0). Every entry carries a number (monotonic int per type — highest = newest, never reused; new = current max + 1) and a date (ISO), plus its text field + extras: key_learnings {number, date, key, value} · sessions {number, date, summary, status, tags} · todos {number, date, task, priority, status} · observations {number, date, note, tags}.
When adding: stamp number + date, then prepend (newest on top). Don't hand-trim sessions/key_learnings/observations — rollover archives the oldest by number to @memory automatically. Todos are the exception — rollover never touches them, so you prune done ones by hand (delete finished todos, see above).
If Relevant
- .trinity/passport.json — Evolve identity when the branch's role, capabilities, or principles have genuinely changed. Don't update just to update — but don't leave placeholders forever either.
- README.md — Does it reflect current state? Update if stale.
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.
- 7d ago First seen · 34 lines · 28 tokens per session scan A 3a5d2cc8d057
memo is a skill published in the GitHub repository AIOSAI/AIPass (273 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 553 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 skills, from other repositories
learning-capture
Log corrections, errors, and discoveries to orbital/LESSONS.md so future sessions avoid repeating mistakes.
aura
Give this agent a real email address, a webhook URL, durable memory across runs, and the ability to wait for an event without burning tokens. Use when a task needs a verification code sent by email, needs to wait for a webhook or an approval, needs to remember something after the session ends, or needs to survive a…
squad-bootstrap
Skill de inicialização da squad. Faz a varredura do projeto e popula a memória técnica e de negócios em memories/. ATENÇÃO - SÓ DEVE ser executada se houver solicitação explícita do usuário ("instalar a squad", "fazer setup inicial", "bootstrap do projeto") ou logo após o instalador (install.sh) ter configurado a…
compound
Persistência de aprendizados nas memórias vivas ao final de cada ciclo. Use para "atualizar memória", "guardar aprendizados" ou "sincronizar contexto do projeto". Executada pelo Tech Lead após o PO definir Status entregue.
agentic-loop
Run long-lived autonomous agent loops with persistent memory, context-window compaction, self-healing retries, and resumable state. Use when an AI coding agent must retain progress across sessions, recover from failures, or avoid context exhaustion.
Context Doctor
Identify and repair degradation in system prompt, external memory, and skills preventing you from following instructions or remembering information as well as you should.