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 skills/aendrix03/graft/learnnpx skills add AEndrix03/Graft --skill learngit clone --depth 1 https://github.com/AEndrix03/GraftWhat 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 | $0.00124 | $0.03117 |
| Opus 5 | $0.00062 | $0.01558 |
| Sonnet 5 | $0.00025 | $0.00623 |
| Haiku 4.5 | $0.00012 | $0.00312 |
Grade A, and why
learn 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
learn — Batch ingestion of external knowledge into graft
/learn is a plan-first ingestion pipeline. It is NOT "scan and dump everything you see". It is: scout, distill, propose a plan, get the user's approval, then execute. This separation matters because uncontrolled ingestion creates duplicates, garbage summaries, and a keyword vocabulary that fragments into noise. The plan-and-confirm gate is the heart of the skill.
The typical use case: the user points you at a codebase or a docs tree and asks you to "acquire" it — so future-you (or another agent) can /recall it without re-reading the source.
Argument shape
The user invokes you with a free-form prompt that may include:
| Hint | Example | Meaning |
|---|---|---|
| target | "this folder", "src/auth/", "the README + docs/", a glob, a list of paths | What to read. |
| lens / focus | "as a Spring Boot reference", "extract only public APIs", "design decisions only" | The angle of distillation. Without this you'd save trivia. |
| node cap | "max 30 nodes", "atomico per file", "one node per module" | Soft upper bound; default 50 per invocation, hard cap 200. |
| target profile | "in profile=docs-springboot", "nel profilo work" | GRAFT_PROFILE=X for the inserts. |
| excludes | "skip tests/", "ignore generated/", a .gitignore-style list |
Glob/regex skip rules layered on top of the defaults below. |
| rerun mode | "incremental" / "force" / "dry-run" | See "Rerunning on the same source" below. |
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 · 250 lines · 124 tokens per session scan A d4e62f929478
learn is a skill published in the GitHub repository AEndrix03/Graft (11 stars, last pushed 9d ago), licensed Apache-2.0. It adds 124 tokens to every session and 3,117 once invoked, about $0.0006 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.
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