refresh-eo-jobs

name	refresh-eo-jobs
description	Use when refreshing or updating the EO-jobs company directory — re-verifies each company's links, locations, and remote-work policy against their live sites/careers pages, discovers new Earth Observation companies, and opens a PR with the changes. Run on demand.

Refresh EO-jobs

Automates upkeep of the Earth Observation company directory. The CSV earth-observation-jobs - Companies.csv is the source of truth (plain LF, so ordinary pandas edits give clean diffs); generate_readme.py renders it into README.md. A run re-verifies existing companies, discovers new ones, and opens one PR for review.

Design rationale and the validated fetch strategy live in docs/superpowers/specs/2026-06-14-eo-jobs-refresh-agent-design.md.

Core conventions

• Conservative edits. Only change a field when the page clearly contradicts the current value. When unsure, leave it and note "could not verify".
• Evidence for every Remote change. Record a short quote/observation and which page it came from. This is the column users care about most.
• Regenerate, don't hand-edit, the README. After editing the CSV, run generate_readme.py. Never edit the table in README.md directly.

Setup

1. Per the repo's worktree rule, work on a branch off master in a worktree (e.g. refresh/YYYY-MM-DD).
2. Ensure the venv exists with deps: python3 -m venv .venv && .venv/bin/pip install -q pandas tabulate Use .venv/bin/python for the script runs below.

Stage 1 — Refresh existing companies (parallel WebFetch)

1. Read the CSV columns Name, Description, Satellites, Website, Jobs site, Jobs 2, Locations, Remote for all rows.
2. Fan out subagents in parallel, ~10 concurrent (Agent tool, multiple calls per message), one per company, using the prompt template in references/company-prompt.md. Each returns a structured report (link health, field suggestions, and Remote with evidence + confidence).
3. Collect the reports. Track every company whose Website / Jobs site / ATS URL returned HTTP 403 or an empty body — these go to Stage 1.5.

Stage 1.5 — Browser fallback (sequential)

Hosted ATS boards (BambooHR, Lever, Greenhouse, Darwinbox, …) routinely return 403 to WebFetch, and that is exactly where per-role remote tags live. For each company flagged in Stage 1:

1. Load the Chrome browser tools (one ToolSearch call — see the MCP guidance in the system prompt). There is one shared Chrome instance, so process these one at a time.
2. navigate to the blocked URL, then get_page_text.
   • Real content → re-derive the company's findings (especially Remote), raising confidence.
   • 404 / "not found" → the URL is genuinely dead, not blocked; propose a replacement (the company's own embedded careers board) or flag it.
3. Only after both passes fail is a page declared truly unreadable; leave that row unchanged with a "could not verify" note.

Stage 2 — Discover new companies

Sweep these sources for EO companies not already in the CSV (match on Name + Website, case-insensitive; when unsure if it's a duplicate, skip):

• GitHub chrieke/awesome-geospatial-companies.
• Google Sheet (fetch the CSV export form, not the editor URL): https://docs.google.com/spreadsheets/d/1YieNiDHVTC5CfX13n72fXuceDaubSuyrghgAnxipsbY/export?format=csv&gid=0
• The geospatial / climate job newsletters cited in README.md.

For each genuinely new EO company, gather what can be verified (at minimum Name, Website, Description, Locations) and add it as a new row. Flag every addition as [NEW] in the PR body — these need extra scrutiny.

Stage 3 — Apply, regenerate, open PR

Apply all collected changes to the CSV with a few lines of pandas, then regenerate. New companies are just Names not yet present:

import pandas as pd
f = "earth-observation-jobs - Companies.csv"
df = pd.read_csv(f, dtype=str, keep_default_na=False).set_index("Name")
for name, fields in CHANGES.items():       # {"Company": {"Column": "value"}}
    for col, val in fields.items():
        df.loc[name, col] = val            # a new Name creates a new row
df.reset_index().to_csv(f, index=False)    # LF in, LF out — diffs stay row-local

(Use the internal Notes column for provenance like acquisitions — it is not rendered in the README.)

Then:

1. .venv/bin/python generate_readme.py
2. Sanity-check: git diff --stat — both diffs should be row-local (only changed companies). A full-table reflow means something regressed; stop and investigate.
3. Commit (CSV + README together), push the branch.
4. Open one PR. Body = the changelog template in references/pr-template.md, nothing else. The Remote changes section (with evidence) is what the reviewer checks closely.

Note: opening the PR is the final step; if PR creation is gated, push the branch and hand over the PR-create link.

Scaling notes

• Stage 1 parallelizes well (185 companies in batches of ~10). Stage 1.5 is the sequential bottleneck — expect ~30–50 companies to need the browser.
• To keep PRs reviewable at full scale, running a slice per PR (e.g. the companies most likely stale) is fine rather than all 185 at once.