MARKET RESEARCH

SEC Rule Draft Diff and Material-Change Flagger

Scrapes a watched SEC rule-draft page on a schedule, diffs the new text against the last captured version.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule: re-check rule page every few hours
  • ActionScrape rule draft to markdownFirecrawl
  • LogicDiff vs. last stored version; stop if unchanged
  • ActionClassify edits as material vs. cosmeticOpenAI
  • ActionSave new version as baselinePostgreSQLPostgres
  • OutputPost severity-tagged alert with diffed passagesSlack

What it does

Keeps a running snapshot of a specific SEC proposed-rule page. On each run it re-scrapes the page, compares the fresh text to the previously stored version, and surfaces exactly what changed. An LLM judges whether the edits are substantive (new obligations, changed thresholds, altered effective dates) or cosmetic (formatting, typos), then sends a Slack message with the verdict and the diffed passages.

When to use it

Use it when your team tracks a handful of in-flight SEC rulemakings and cannot afford to manually re-read 80-page documents to find the two sentences that moved. Best for compliance, legal, and policy teams who need a same-day heads-up the moment a draft is revised.

How it works

  1. 1A scheduled trigger fires every few hours.
  2. 2Firecrawl scrapes the watched rule page to clean markdown.
  3. 3The pipeline pulls the prior captured version from Postgres and computes a text diff; if nothing changed it stops.
  4. 4OpenAI reads the diff and classifies it as material or cosmetic with a one-line rationale.
  5. 5The new version is written back to Postgres as the baseline.
  6. 6Slack receives an alert tagged by severity with the changed passages inline.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FirecrawlCrawl, scrape, structured extract.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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