MARKET RESEARCH

Tracked Competitor Model Rank-Drop Watch

Watches a watchlist of specific competitor models on the HuggingFace leaderboard and raises a ticket when any of them drops past a rank threshold or loses ground on key metrics.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule
  • ActionLoad tracked-model watchlistPostgreSQLPostgres
  • ActionFetch current rank and metricsHugging FaceHugging Face
  • LogicFlag threshold breaches
  • ActionWrite change rationaleOpenAI
  • OutputOpen Linear issue per flagged modelLinearLinear

What it does

Instead of monitoring the whole leaderboard, this workflow tracks a defined watchlist of competitor models you care about. On each run it checks where those models sit and how their scores have changed, and if any crosses a configured rank or metric-decline threshold, it opens a Linear issue so the team formally evaluates whether the shift is an opportunity or a risk.

When to use it

Use it when you have named competitors or partner models to keep tabs on and want signal, not noise. Ideal for product and partnerships teams who only need to act when a tracked model meaningfully moves.

How it works

  1. 1A daily schedule triggers the check.
  2. 2Postgres loads the watchlist of tracked model IDs and their last-known stats.
  3. 3HuggingFace fetches current rank and metrics for each tracked model.
  4. 4A logic step flags any model breaching the rank-drop or metric-decline thresholds.
  5. 5OpenAI writes a short rationale explaining what changed and why it matters.
  6. 6Linear opens an issue for each flagged model.
  7. 7Updated stats are written back to Postgres.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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