How do I install the Agent Update Watcher skill?

Run npx skillmds@latest add jajupmochi/agent-update-watcher in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Update Watcher safe to use?

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Which AI agents work with Agent Update Watcher?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Update Watcher free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Update Watcher?

jajupmochi (@jajupmochi) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Update Watcher by jajupmochi — Agent Skill

Name: Agent Update Watcher
Author: jajupmochi

Agent Update Watcher

Use to keep up with the fast-moving agent ecosystem (new/updated CLIs, plugins, skills) WITHOUT burning tokens on constant checks. Declares the sources to watch in a config, checks them only when a minimum interval has elapsed, and reports ONLY what actually changed vs the recorded version — you then decide whether to adopt.

jajupmochi c05b01a 4 files · 9.4 KB Updated Sep 17, 2026

File contents

agent-update-watcher

Overhaul task 6. New agent plugins/skills/CLIs appear constantly; checking them every turn wastes tokens and network. This balances availability / recency against cost with a deterministic checker + a frequency guard, and surfaces only real changes.

Use

Copy scripts/sources.example.json to a local sources.json; list what to watch (name, kind, current_version, url) and set real current versions.
Provide latest versions as a --snapshot {name: latest} JSON (the thin fetch that turns each url into a latest version is caller-supplied — kept out of the deterministic checker so it stays testable/offline):
```
python3 scripts/check_updates.py --config sources.json --state state.json --snapshot latest.json --min-interval-days 7
```
It prints one UPDATE\t<kind>\t<name>\t<cur> -> <latest>\t<url> line per changed source, records the check time in state.json, and skips cheaply if re-run within the interval (override with --force).

Why deterministic + interval-guarded

Checking and diffing versions is pure code (no LLM). The interval guard is the token/network balance: a weekly (default) check is enough for an ecosystem that moves in days, not seconds. The model only decides whether to adopt an update — it never does the polling.

Relation to autoresearch-toolfinder

autoresearch-toolfinder tracks ML-RESEARCH tools (awesome-lists). This watches the AGENT ecosystem itself (the CLIs/plugins/skills you build on). Different domains, same "track + diff + report, don't reload" idea.

Status: v0.1 (config + interval-guarded diff, --snapshot-driven, tested test_check_updates.py 6/6). The real release-feed fetch (url -> latest version) + a scheduled runner are the thin wrappers left to add.

---name: agent-update-watcherdescription: Use to keep up with the fast-moving agent ecosystem (new/updated CLIs, plugins, skills) WITHOUT burning tokens on constant checks. Declares the sources to watch in a config, checks them only when a minimum interval has elapsed, and reports ONLY what actually changed vs the recorded version — you then decide whether to adopt.---# agent-update-watcherOverhaul task 6. New agent plugins/skills/CLIs appear constantly; checking them every turn wastes tokens andnetwork. This balances **availability / recency** against **cost** with a deterministic checker + a frequencyguard, and surfaces only real changes.## Use1. Copy `scripts/sources.example.json` to a local `sources.json`; list what to watch (`name`, `kind`, `current_version`, `url`) and set real current versions.2. Provide latest versions as a `--snapshot {name: latest}` JSON (the thin fetch that turns each `url` into a latest version is caller-supplied — kept out of the deterministic checker so it stays testable/offline): ``` python3 scripts/check_updates.py --config sources.json --state state.json --snapshot latest.json --min-interval-days 7 ```3. It prints one `UPDATE\t<kind>\t<name>\t<cur> -> <latest>\t<url>` line per changed source, records the check time in `state.json`, and **skips cheaply** if re-run within the interval (override with `--force`).## Why deterministic + interval-guardedChecking and diffing versions is pure code (no LLM). The interval guard is the token/network balance: aweekly (default) check is enough for an ecosystem that moves in days, not seconds. The model only *decideswhether to adopt* an update — it never does the polling.## Relation to autoresearch-toolfinder`autoresearch-toolfinder` tracks ML-RESEARCH tools (awesome-lists). This watches the AGENT ecosystem itself(the CLIs/plugins/skills you build on). Different domains, same "track + diff + report, don't reload" idea.Status: v0.1 (config + interval-guarded diff, `--snapshot`-driven, tested `test_check_updates.py` 6/6). Thereal release-feed fetch (url -> latest version) + a scheduled runner are the thin wrappers left to add.

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