# Refresh Eo Jobs > 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. - Skill: `tomevault-io/refresh-eo-jobs` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/refresh-eo-jobs` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/refresh-eo-jobs/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/refresh-eo-jobs --- # 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: ```python 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. --- > Source: [DahnJ/EO-jobs](https://github.com/DahnJ/EO-jobs) — distributed by [TomeVault](https://tomevault.io).