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 moonlight-lupin/agent-skills --skill skill-maintainergit clone --depth 1 https://github.com/moonlight-lupin/agent-skillsWrote 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/moonlight-lupin/agent-skills/skill-maintainer)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/skill-maintainer"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/skill-maintainer/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/moonlight-lupin/agent-skills/skill-maintainer"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/skill-maintainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 291 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00012 | $0.04665 |
| Opus 5 | $0.00006 | $0.02332 |
| Sonnet 5 | $0.00002 | $0.00933 |
| Haiku 4.5 | $0.00001 | $0.00466 |
Grade A, and why
skill-maintainer scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
# windows: cron + curl available via WSL/MSYS2, but untested on native Win32 How it starts
The opening of the file, as written. The whole thing — 515 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Maintainer
Overview
A meta-skill for agents who maintain a library of skills — their own, adapted from external repos, or published to a shared repo. Covers the full lifecycle: authoring → curating → tracking → syncing → publishing.
This skill encodes a workflow built and battle-tested across dozens of skills.
It is agent-agnostic: replace tool names (e.g. write_file, terminal,
delegate_task) with your platform's equivalents. The patterns — manifest
tracking, layered sync, parallel diff dispatch, cron-based drift detection —
transfer to any agent runtime.
When to Use
- Creating a new skill from scratch
- Importing/adapting skills from an external repo or collection
- Checking if local skills have drifted from upstream
- Scanning for untracked external tool dependencies
- Publishing a local skill to a shared repo
- Setting up automated monthly sync checks
- Tightening/pruning an accumulated skill library
Don't use for: one-off skill creation with no upstream tracking need (just use your platform's skill-authoring tool directly). This skill adds value when you have ≥5 skills or ≥1 external dependency to track.
Core Concepts
The three sync layers
| Layer | Direction | What drifts | Example |
|---|---|---|---|
| 1. External → Local | Upstream repo → your skill | Upstream adds features, fixes bugs, changes API | baoyu-skills v1.56 → v1.117 |
| 2. Local → Published repo | Your skill → your GitHub repo | You improve locally; repo copy goes stale | skill gets new flags locally; repo doesn't |
| 3. Local → Standalone repo | Your skill → a code project repo | Skill documents a workflow; the code evolves | skill describes CLI v1; CLI is now v3 |
All three need tracking. Layer 1 is the most common concern. Layer 2 is the most commonly forgotten — publish once, keep improving, repo goes stale.
The manifest
A single UPSTREAM_MANIFEST.md file at your skills root tracks every skill
with external provenance. One row per skill, covering all three layers. See
templates/upstream-manifest.md for the file format.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 515 lines · 12 tokens per session scan A 9a733ef88108
skill-maintainer is a skill published in the GitHub repository moonlight-lupin/agent-skills (60 stars, last pushed 3d ago), licensed MIT. It adds 12 tokens to every session and 4,665 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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