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 RohiRIK/OpenLtm --skill gitlearngit clone --depth 1 https://github.com/RohiRIK/OpenLtmWrote 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/rohirik/openltm/gitlearn)<a href="https://agentmods.dev/skills/rohirik/openltm/gitlearn"><img src="https://agentmods.dev/badge/skills/rohirik/openltm/gitlearn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rohirik/openltm/gitlearn"><img src="https://agentmods.dev/badge/skills/rohirik/openltm/gitlearn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.00809 |
| Opus 5.5 | $0.00028 | $0.00324 |
| Sonnet 5.5 | $0.00014 | $0.00162 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
GitLearn 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 18d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitLearn — Retroactive Git Commit Learning
Extract durable LTM memories from past git commits by delegating the read-and-extract
work to the dedicated git-learner agent (shipped with this plugin). The agent
reads the diffs and stores memories via the learn MCP tool, so this path needs
no API key and keeps the main thread's context clean (raw diffs stay in the agent).
Scope
| Argument or request | Commits processed |
|---|---|
| (none) | last 10 |
--commits N |
last N |
--since YYYY-MM-DD |
all commits since that date |
When to Use
- Onboarding a new project into LTM — seed memories from existing commit history.
- Backfilling history after enabling
gitLearnEnabledfor the first time. - Harvesting reusable patterns after a productive sprint.
How It Works
The background post-commit hook (GitCommit.ts) calls an LLM API directly and needs
a configured key. This skill runs interactively instead, so it spawns the
git-learner agent via the Agent tool. Spawning an agent is valid here because
skills execute in the live Claude Code session, which has the Agent tool available.
The agent carries its own extraction rubric (signal-to-noise rules, category mapping,
storage fields), so this skill only has to supply the scope.
Instructions for Claude
Step 1 — Resolve scope
Determine the commit range from the arguments or the user's wording (default: last 10). Capture the repo root so the subagent runs git in the right directory:
git rev-parse --show-toplevel
git log --pretty=format:'%H %s' -<N> # or --since="<date>"
Step 2 — Spawn the git-learner agent once
Call the Agent tool once with subagent_type: "openltm:git-learner" (plugin agents are namespaced by plugin name). A single agent processes
the whole batch — do not spawn one per commit. The agent's system prompt already holds
the extraction rubric, so the spawn prompt only supplies a <scope> block:
<scope>
REPO_ROOT: <repo root from git rev-parse --show-toplevel>
PROJECT_NAME: <repo directory basename>
COMMITS:
<one commit hash per line, from Step 1>
</scope>
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.
- 18d ago Changed · +1 lines · +33 tokens per session 72375ff755b6
- 2mo ago First seen · 79 lines · 38 tokens per session scan A b32c09e4d579
GitLearn is a skill published in the GitHub repository RohiRIK/OpenLtm (29 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 809 once invoked, about $0.0003 per session on Opus 5.5. 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
github
Interact with GitHub using the gh CLI. Use gh issue, gh pr, gh run, and gh api for issues, PRs, CI runs, and advanced queries.
swarm-memory
Git-based multi-agent memory synchronization. Activated when managing agent memory files, syncing memories across devices, or organizing long-term knowledge.
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
mem0-remember
Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.
status
Show whether Mem0 memory is working in this repository, covering configuration, capture state, pending flushes, and whether the Mem0 API key is valid. Use when the user asks whether memory is on, why a memory is missing, or anything looks broken.