memory-onboard

memory-onboard is a skill for Claude Code, Codex from tmj-90/gaffer. It costs 196 tokens per session (3,603 once invoked), scanned A, original, Apache-2.0.

A repository onboarding process for shared project memory, called lore. It reads project materials and proposes draft records about conventions, decisions, migrations, and other facts for a person to review.

In plain words
What is it for?
Use it with `/memory-onboard` or when setting up memory for a repository. It can inspect the README, architecture decisions, recent commits, and deprecation markers.
Why use it?
It creates repo-specific guidance from evidence instead of filling memory with generic or unverified notes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tmj-90/gaffer/memory-onboard
Any agent
npx skills add tmj-90/gaffer --skill memory-onboard
Clone the repo
git clone --depth 1 https://github.com/tmj-90/gaffer

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memory-onboard

README.md
[![agentmods](https://agentmods.dev/badge/skills/tmj-90/gaffer/memory-onboard.svg)](https://agentmods.dev/skills/tmj-90/gaffer/memory-onboard)
Your own site
<a href="https://agentmods.dev/skills/tmj-90/gaffer/memory-onboard"><img src="https://agentmods.dev/badge/skills/tmj-90/gaffer/memory-onboard.svg" alt="Measured on agentmods" height="20"></a>
Per session 196 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,603 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00196 $0.03603
Opus 5 $0.00098 $0.01801
Sonnet 5 $0.00039 $0.00721
Haiku 4.5 $0.00020 $0.00360

Measured 4d ago against content hash 6851503caf34, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memory-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 4d 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.

packages/memory/skills/memory-onboard/SKILL.md · 300 lines

How it starts

The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/memory-onboard — repo-aware onboarding for memory

This skill is the way to seed a repo with lore. It earns its keep by reading the repo first (README, ADRs, recent commits, deprecation markers, in-flight migrations) so the proposed drafts are specific to this codebase, with source citations — rather than inventing memory or mechanically chunking every bullet in the docs (which produces mostly noise and floods the review queue).

Output: DRAFT records with source citations, plus optional absence markers and boundary edges. Everything lands in memory review for a human to ratify — the skill never promotes its own records.

Hard rules

  1. Every lore record this skill produces is a DRAFT. Call suggest_lore (or report_conflict for counter-claims — also drafts), never any approval path. The human reviews with memory review. Exception: record_absence does NOT create a draft — it records a self-expiring "we checked, no policy" marker. Different primitive, different gate (see rule 6).
  2. Every draft carries the tag induction so it can be filtered later. Layer topic-specific extras as appropriate (security, conventions, migrations, incident-lessons, invariants, etc.). report_conflict counter-drafts are auto-tagged conflict-report by the server.
  3. Cite sources in the body. When a draft comes from a file or commit, include a one-line reference at the top of the body — e.g. Source: README.md L42–55 or Source: commit abc1234 "migrate accounts → organisations". This is the trust signal a reviewer needs.
  4. Never put secrets, credentials, personal data, regulated data, or anything that looks like a token into any lore record. If a candidate item contains something like that, skip it and tell the user why.
  5. Skip the obvious. "We use TypeScript", "run the tests before committing", and other things a model already knows about a typical codebase belong in CLAUDE.md (or nowhere), not in lore. Aim for non-obvious, high-consequence knowledge.
  6. Use record_absence sparingly. If the user explicitly confirms "we don't have a policy on X yet, and that's intentional", that's a legitimate absence marker — call record_absence({ query: "<topic>", reason: "<why no policy>", repo: <repo> }). Don't infer absence from "I didn't find anything in the README"; the user has to confirm it. Markers self-expire (14 days default) so they fade automatically if the team takes a stance later.
  7. Use report_conflict when you find a contradiction, not when you find a duplicate. If search_lore returns an existing record and the source-of-truth you just read disagrees with it (e.g. README says "Argon2id", existing lore says "scrypt"), that's a report_conflict moment. If the existing record just covers the same topic without contradicting, leave it alone — possibleConflicts in search results handles benign overlap.

Read the full file on GitHub · 300 lines

Changes

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.

  1. 4d ago First seen · 300 lines · 196 tokens per session scan A 6851503caf34

Subscribe to this mod's changes

memory-onboard is a skill published in the GitHub repository tmj-90/gaffer (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 196 tokens to every session and 3,603 once invoked, about $0.0010 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 2026-08-31.

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