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 skills add n24q02m/claude-plugins --skill passport-bootstrapgit clone --depth 1 https://github.com/n24q02m/claude-pluginsWrote 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/n24q02m/claude-plugins/passport-bootstrap)<a href="https://agentmods.dev/skills/n24q02m/claude-plugins/passport-bootstrap"><img src="https://agentmods.dev/badge/skills/n24q02m/claude-plugins/passport-bootstrap.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 | $0.00083 | $0.01040 |
| Opus 5 | $0.00042 | $0.00520 |
| Sonnet 5 | $0.00017 | $0.00208 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
passport-bootstrap 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.
This is a copy
100% identical to passport-bootstrap — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Passport Bootstrap
Restore an encrypted memory passport from a configured backend so a fresh mnemo-mcp install picks up the user's full memory history.
When to Use
Trigger on explicit signals:
- "set up mnemo on this machine"
- "restore my passport" / "import my memory passport"
- "bootstrap mnemo on this laptop / VM"
- New-machine context: "I just installed mnemo on a new device"
- Recovery context: "I lost my memories, can you restore from S3?"
Do NOT trigger when the user is on an already-configured machine
(check config(action="status") first - if total_memories > 0,
ask before importing because import LWW-merges and may NOT overwrite
local rows newer than the bundle).
Workflow
Step 1: Detect configured backend
Call config(action="status") and inspect the response. The default
backend is the first entry of SYNC_BACKEND env (defaults to
gdrive). If both s3 and gdrive are configured, ask the user
which one to import from. If neither is configured, stop and tell the
user they need to run the relay form first
(config(action="setup_start") in HTTP mode, or set
SYNC_S3_BUCKET + GOOGLE_DRIVE_CLIENT_ID env vars in stdio mode).
Step 2: Confirm passphrase
The bundle is AES-256-GCM-encrypted with an Argon2id-derived key from
the user's passphrase. The MCP server needs the RAW passphrase to
decrypt - the Argon2id hash stored in config.enc is verification-
only.
In stdio mode: instruct the user to set SYNC_PASSPHRASE env var
before running mnemo-mcp (or to relaunch with the env exported).
In HTTP mode: prompt the user to submit the relay form's passphrase field again (the raw value is held in process memory only and is cleared on restart - it is NEVER persisted).
Step 3: Import the bundle
Call config(action="import_passport", key="<backend>") where
<backend> is s3 or gdrive. The server pulls the latest bundle
from the chosen backend, decrypts with the supplied passphrase, and
applies each row via last-write-wins per row (local rows newer than
the bundle row are preserved + an audit row is written to
sync_overrides).
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 · 97 lines · 83 tokens per session scan A c357dcb930da
passport-bootstrap is a skill published in the GitHub repository n24q02m/claude-plugins (4 stars, last pushed today), licensed Apache-2.0. It adds 83 tokens to every session and 1,040 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to passport-bootstrap, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
catchup
Restore context after /clear by summarizing recent work and project state.
start
Load session context — git state, backlog, last handoff.
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
long-context
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases…
vellum-memory-v3-migration
One-time migration of an existing memory-v2 concept corpus into the memory-v3 section-grain "wiki" — topical articles with a stand-alone lead and queryable sections — with loss-proof staging, assistant-reviewed authoring, and a retrieval-eval gate before cutover.