ENGINEERING

Sync HuggingFace watchlist from GitHub model references

On every push to your main repo, scans the code for HuggingFace model ids actually referenced.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPush to default branchGitHubGitHub
  • ActionExtract HuggingFace model ids from repo treeGitHubGitHub
  • ActionLoad current watchlist from PostgresPostgreSQLPostgres
  • LogicReconcile adds and removals
  • ActionWrite updated watchlist to PostgresPostgreSQLPostgres
  • OutputPost watchlist changes to SlackSlack

What it does

Keeps the model watchlist honest. Instead of maintaining a manual list, it triggers when your repository changes, greps the codebase for HuggingFace model identifiers (in configs, model loaders, env defaults), and reconciles those against the stored watchlist. Models newly referenced get added; models no longer referenced anywhere get retired. The result is a watchlist that always reflects what your code really loads.

When to use it

Use it when model usage shifts often and a stale watchlist means you either alert on models you dropped or miss models you quietly adopted. This is the upstream feeder for the other drift monitors in this catalog.

How it works

  1. 1A push to the default branch triggers the workflow via GitHub.
  2. 2It fetches the changed repository tree and extracts every HuggingFace model id pattern.
  3. 3It loads the current watchlist from Postgres.
  4. 4A reconcile step computes additions and removals.
  5. 5It writes the updated watchlist back to Postgres and posts a short summary of adds/removes to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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