version: "1.0.1" name: coarse-review description: > Produce a rigorous academic peer review of a research paper, manuscript, or preprint (PDF, markdown, TeX, DOCX, HTML, or EPUB) using the full coarse pipeline with the user's local Codex CLI (ChatGPT subscription) doing all the LLM reasoning. Every pipeline stage — structure analysis, overview synthesis, per-section review, proof verification, editorial pass — is served by a headless codex exec subprocess instead of a paid API. Use when the user asks to review, critique, referee, or provide feedback on an academic paper. Takes 10-25 minutes.

coarse-review (Codex)

Runs the full coarse review pipeline on a paper using the local codex exec CLI as the LLM backend. Every LLM call is served by a headless Codex subprocess using the user's ChatGPT Plus/Pro/Team plan. The only per-paper cost is the ~$0.05-0.15 Mistral OCR extraction for PDF sources, which uses the user's OpenRouter key locally — non-PDF sources (.tex, .md, .txt, .docx, .html, .epub) extract locally with no OpenRouter key at all.

Prerequisites

• uvx preferred, uv acceptable. First run:

command -v uvx || command -v uv

• If neither exists, install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

• Then refresh PATH for the current shell:

export PATH="$HOME/.local/bin:$PATH"

• coarse requires Python 3.12+. If needed, install it with:

uv python install 3.12

• If uv exists but uvx does not, replace uvx --python 3.12 --from ... below with

uv tool run --python 3.12 --from ....

• Refresh the bundled coarse-review skill with an ephemeral install:

uvx --python 3.12 --from 'coarse-ink==1.9.0' coarse install-skills --all --force (If that fails with No such command 'install-skills', you're on a PyPI release that predates the command — upgrade or ignore; the skill bundle is also loadable directly via uvx --from without install.)

• OpenRouter API key required for PDF papers or an explicitly requested deep literature search — Mistral OCR extraction (~$0.10 per paper) runs on PDFs alone. Standard non-PDF reviews extract locally and need no OpenRouter key; --deep-literature-search uses Perplexity Sonar Deep Research for any format and normally adds about $0.30. Skip the probes below only when both conditions are absent. Prefer checking for the key with presence-only probes so you don't needlessly echo its value into the transcript, but if the user hands you the key directly just save it — don't lecture them.

For PDF papers, or whenever deep literature search is requested, check whether OPENROUTER_API_KEY is already configured before running:

• In the environment: test -n "$OPENROUTER_API_KEY" && echo "env: set" || echo "env: missing"
• In a .env file in the current directory: test -f .env && grep -q '^OPENROUTER_API_KEY=' .env && echo ".env: set" || echo ".env: missing"

If the paper is a PDF or deep literature was requested and neither probe reports "set", ask the user:

> I need an OpenRouter API key for PDF OCR (~$0.10) and/or the requested deep literature search (~$0.30). A few options: > > 1. Paste the key here and I'll save it to ~/.coarse/config.toml via uvx --python 3.12 --from 'coarse-ink==1.9.0' coarse setup. Note the key passes through the LLM provider (OpenAI) on its way to me, so treat it as slightly less private than one you typed into a local terminal — rotate at https://openrouter.ai/settings/keys if that worries you. > 2. Set it yourself in a separate terminal: export OPENROUTER_API_KEY=sk-or-v1-... or add it to .env in your current directory, then re-ask me. > 3. Run uvx --python 3.12 --from 'coarse-ink==1.9.0' coarse setup in a separate terminal yourself and paste the key into its interactive prompt — the key never touches this chat. > > Which do you want?

If the user pastes a key here, save it via uvx --python 3.12 --from 'coarse-ink==1.9.0' coarse setup with the pasted value and confirm it's stored. Their chat, their choice.

• codex CLI logged in: codex login.

How to run

Two-step launch-and-wait, not foreground. A full review takes 10-25 minutes, which exceeds Codex's default 5-minute tool timeout. Foreground runs will be killed mid-review and reported as crashed when they're actually still working.

Step 1 detaches the worker (~2 seconds) and writes <log>.pid. Step 2 uses --attach to block on that pidfile and stream the log until the worker exits, emitting a heartbeat every 30 seconds of log idleness so the Codex shell doesn't flag the command as hung. Use a per-review unique log file so parallel runs don't clobber each other's output:

LOG=/tmp/coarse-review-$(basename <paper_path> .pdf).log
 
# STEP 2a — launch (returns in ~2s)
uvx --python 3.12 --from 'coarse-ink==1.9.0' \
  coarse-review --detach --log-file "$LOG" \
  <paper_path> --host codex [--model gpt-5.6-sol] [--effort high] [--deep-literature-search]
 
# STEP 2b — wait (one blocking call, ~10-25 min, emits heartbeats)
uvx --python 3.12 --from 'coarse-ink==1.9.0' \
  coarse-review --attach "$LOG"

Run the attach call with a long tool timeout — at least 45 minutes (--timeout 2700) — so Codex doesn't kill the blocking command prematurely. The 45-minute recommendation leaves ~20 minutes of margin on top of the 10-25 minute review runtime for cold starts, slow models, long papers, and --effort max runs; 30 minutes is too tight because the tool timeout is a wall clock, not an idle-stream cap. Bump to 60 minutes (--timeout 3600) for book-length papers or the largest models. Do NOT re-run the --detach command from STEP 2a if the attach call returns early (that would spawn a second worker). Safe to Ctrl+C the attach: the watcher detaches but the worker keeps running, and re-attaching with the same command is idempotent. Attach exit codes: 0 complete, 1 failure marker, 2 silent crash, 3 missing pidfile, 124 attach's own 30-min timeout, 130 user interrupt.

When attach exits cleanly, use the final log lines as the authoritative artifact locations:

rg '^  view:|^  local:' "$LOG"

If local: is present, read that exact file. If view: is present, use that URL (it already includes the signed access token — use it as-is). Do not run broad filesystem searches trying to rediscover the review file. If view: says unavailable, report the callback failure and use only the local: path.

Available models: gpt-5.6-sol (default), gpt-5.6-terra, gpt-5.6-luna, gpt-5.5, gpt-5.4. Available effort levels: low, medium, high (default), max.

These map to Codex's internal reasoning effort:

• low → low
• medium → medium
• high → high
• max → high

Handoff mode (when the user came from the coarse web form): the paper is a REMOTE resource at the handoff URL. Do NOT search for a local PDF and do NOT ask the user for a file path — the --handoff URL IS the paper source. Same two-step launch+attach pattern:

LOG=/tmp/coarse-review-$(date +%s).log
 
# STEP 2a — launch
uvx --python 3.12 --from 'coarse-ink==1.9.0' \
  coarse-review --detach --log-file "$LOG" \
  --handoff https://coarse.ink/h/<token> --host codex
 
# STEP 2b — wait
uvx --python 3.12 --from 'coarse-ink==1.9.0' \
  coarse-review --attach "$LOG"

When complete, show the user the output path, web URL (if present), recommendation, top issues, and comment count.

Notes

• uvx --python 3.12 --from ... coarse-review ... runs coarse from a temporary environment, so the agent does not mutate the user's global tool install.
• The review process runs locally using the user's own Codex login; coarse.ink only receives the finished markdown callback.
• coarse-review monkey-patches coarse.llm.LLMClient → coarse.headless_clients.CodexClient, which spawns codex exec -c model_reasoning_effort='<level>' - for every pipeline LLM call, feeding the prompt via stdin.
• Codex session env vars are stripped so nested sessions don't conflict.