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/aiosai/aipass/prepgit clone --depth 1 https://github.com/AIOSAI/AIPassWhat 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 | $0.01395 |
| Opus 5 | $0.00000 | $0.00698 |
| Sonnet 5 | $0.00000 | $0.00279 |
| Haiku 4.5 | $0.00000 | $0.00139 |
Grade A, and why
prep 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Wrap-Up
Purpose: Button up everything at the end of a session — or before a /compact. Memories, plans, git — all tidy. Works for both closing out a chat and preparing for compaction.
Workflow: /prep → review output → close chat or /compact
Execution
- Pin the interrupted thread FIRST — before any wrap-up work, write one sentence naming what the conversation was doing when /prep fired (the live task, question, or build mid-flight — "nothing in flight" counts). Prep often auto-triggers mid-task and the wrap-up steps flush the thread from attention; this line is what gets it back. It becomes the Resuming line at the end.
- Read
.trinity/passport.json— re-absorb your identity before writing anything - Do ALL of the following, then confirm what was updated
1. Memories
Each memory file plays a distinct role. Update based on what actually changed this session.
.trinity/passport.json— IDENTITY. Who you are: role, capabilities, principles. Only update if identity genuinely evolved this session..trinity/local.json— YOUR MEMORY. Add/update session entry with a summary of work done. Add key_learnings for anything learned. Update todos[] with current in-flight items..trinity/observations.json— YOUR MEMORY OF THE USER. Collaboration insights, preferences, friction points. Skip if nothing new about the user 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:
number— a monotonic int per type (highest = newest, never reused). New entry's number = current max for that type + 1.date— ISO date/datetime.- 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}.
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 · 88 lines · 0 tokens per session scan A 2f00ebc45a9f
prep is a command published in the GitHub repository AIOSAI/AIPass (264 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,395 tokens. 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.
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