Agent update watcher

Skill jajupmochi/agent-harness/skills/agent-update-watcher

Linlin's curated AI agent harness configuration: workflow rules, skills, hooks, plugin recommendations, tooling preferences, and project templates. Loadable into any new project so a fresh /init can pick the relevant subset.

Install

npx -y skills add jajupmochi/agent-harness --skill agent-update-watcher

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

Copied from the file, not written here

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.

SKILL.md

2.2 KB, as published. Nobody here has run it

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.