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gooseworks-ai / capabilities-job-posting-intent

Job Posting Intent Detection

Detect buying intent from job postings. When a company posts a job in your problem area, they've allocated budget and are actively thinking about the problem. This skill finds those companies, qualifies them, extracts personali

agent codexmodel gpt-5.5snapshot python312-uveval programmatic8 stepsv1.0.0

Deploy Job Posting Intent Detection to your jetty.io

One-click installs this runbook into a collection on your Jetty account. You can run it from the Spot dashboard, schedule it, or pipe inputs in via the API.

Deploy on jetty.io →View source
The shape of the run

8 steps · start to finish.

  1. 1
    Step 1

    Environment Setup

    ▶

    Create the output directory, install runtime dependencies, and verify required secrets before running any paid search.

    mkdir -p /app/results
python -m pip install --quiet requests apify-client pandas pydantic

if [ -z "${APIFY_API_TOKEN:-}" ]; then
  echo "ERROR: APIFY_API_TOKEN is required" >&2
  exit 1
fi

    Source setup notes:

  2. 2
    Step 2

    Define the Search Intent

    ▶

    Convert the operator's ICP into explicit title, problem-area, and location filters. Treat job postings as budget signals: the hiring company has allocated headcount, acknowledged the problem, and is actively trying to solve it.

  3. 3
    Step 3

    Estimate Cost

    ▶

    Estimate Apify spend before execution and stop if the estimate exceeds `max_cost`. Write the estimate into the validation report so reviewers can audit paid API usage.

  4. 4
    Step 4

    Run the Search

    ▶

    Run the search only after setup and cost validation pass. Save both qualified results and raw payloads when requested.

  5. 5
    Step 5

    Qualify and Export Leads

    ▶

    For each candidate company, record signal strength, reasoning, likely decision makers, outreach angles, and personalization context. If Google Sheets export is enabled, create or update the sheet and write its URL to `/app/results/google_sheet_url.txt`; otherwise write `Google Sh

  6. 6
    Step 6

    Validate Outputs

    ▶

    Validate that every mandatory artifact exists, that JSON outputs parse, and that lead records include company, job title, source URL, qualification reasoning, and signal strength.

  7. 7
    Step 7

    Iterate on Errors (max 3 rounds)

    ▶

    If setup, execution, export, or validation fails, inspect the specific failure, apply one targeted fix, and rerun validation. Stop after max 3 rounds and write the unresolved issue to `/app/results/summary.md` and `/app/results/validation_report.json`.

  8. 8
    Step 8

    Write Summary

    ▶

    Write `/app/results/summary.md` with the query parameters, cost estimate, number of postings inspected, number of qualified leads, Google Sheet URL or skip reason, and any manual follow-up needed.