presubmit

name	presubmit
description	Run the standalone presubmit CLI. Adversarial 30+ stage peer-review pipeline.
argument-hint	[path to your draft to review, or describe the setup task]

Presubmit Activator

What this skill is

A Claude-Code-native launcher and setup wizard for the presubmit Python CLI — the standalone, API-driven adversarial peer-review pipeline that produces a consolidated review report on disk.

This skill does not itself perform the review. It:

1. Verifies presubmit is installed (and walks you through install if not).
2. Verifies your Anthropic API key is set (and walks you through obtaining and configuring one if not).
3. Asks where you want review outputs to live (the first time only — saves to a config file).
4. Receives a paper path, derives a sensible slug, confirms it with you, and invokes presubmit with the right --work-dir so outputs land in the conventional location.
5. Monitors the run, reports cost / wall-time / where the consolidated report landed, and points you at the file to read first.

The actual review work is done by the presubmit package (Anthropic API calls, ~30 stages, ~$5–10 per full run on a typical manuscript).

What this skill is NOT

• Not a re-implementation of presubmit. It calls the existing CLI; the CLI must be installed.
• Not a replacement for paper-review-lite. That skill performs the review itself using parallel Claude Code sub-agents (no API key, no per-token cost). This skill activates the heavier standalone tool. Both have a place — see "When to use which" below.
• Not for peer-reviewing other people's manuscripts. For that, the user maintains a separate reviews/ workflow with a different agents-based CLAUDE.md. This skill is for self-audit of your own drafts pre-submission.

Setup phase (run once per machine)

Before any per-paper invocation, the skill must verify the install + config. Use this checklist; only run the steps whose check fails.

Step 1 — Is presubmit installed?

command -v presubmit && presubmit --help | head -3

If the command is found and --help returns the usage banner, presubmit is installed — skip to Step 2.

If not, ask the user where they keep cloned repos (set PRESUBMIT_DIR to that choice; the examples below use it throughout), then walk them through:

PRESUBMIT_DIR=~/repos/presubmit   # wherever the user keeps clones

# Clone (or update) the repo
git clone https://github.com/scdenney/presubmit "$PRESUBMIT_DIR" \
  || git -C "$PRESUBMIT_DIR" pull

cd "$PRESUBMIT_DIR"

# Create a venv
python3 -m venv .venv
source .venv/bin/activate

# Install — first time pulls marker-pdf + PyTorch, ~5–10 min.
# pyproject.toml pins anthropic>=0.60 directly; verify the resolver honored it:
pip install -e .
pip show anthropic | head -2     # must be >= 0.60; if not: pip install -U 'anthropic>=0.60'

Confirm install with:

"$PRESUBMIT_DIR/.venv/bin/presubmit" --help | head -3

The CLI lives in the venv. Either source the venv each session (source "$PRESUBMIT_DIR/.venv/bin/activate") or invoke the absolute binary path.

On first conversion, marker-pdf will download ~3.3 GB of OCR / layout / table-recognition models into its local Hugging Face cache (macOS: ~/Library/Caches/datalab/models/; Linux: ~/.cache/datalab/models/). Subsequent runs reuse this cache. The download is bandwidth-limited; warn the user.

Step 2 — Is ANTHROPIC_API_KEY set?

[ -n "$ANTHROPIC_API_KEY" ] && case "$ANTHROPIC_API_KEY" in sk-ant-*) echo "key OK";; *) echo "key set but unexpected prefix: ${ANTHROPIC_API_KEY:0:8}…";; esac

If empty, also check whether it's defined in ~/.zshrc but the current shell hasn't sourced it:

eval "$(grep -E '^export ANTHROPIC_API_KEY=' ~/.zshrc | head -1)" 2>/dev/null && [ -n "$ANTHROPIC_API_KEY" ] && case "$ANTHROPIC_API_KEY" in sk-ant-*) echo "found in .zshrc";; esac

If still missing, walk the user through:

1. Generate a key at https://console.anthropic.com/ → Settings → API Keys → Create Key.

2. Add to ~/.zshrc (or equivalent shell rc), placed above any wrapper functions that re-set ANTHROPIC_API_KEY to an empty string for routing the claude CLI to local Ollama models — those would shadow the real key:

   export ANTHROPIC_API_KEY="sk-ant-api03-..."
   

3. source ~/.zshrc or open a new terminal.

4. Confirm a positive credit balance is on the account — presubmit fails fast on credit/billing 400s rather than burning the retry budget. Empty balance halts the run on the first call.

The key is billed to the user's Anthropic account and is independent of any Claude Code subscription.

Step 3 — Where should outputs live?

Read ~/.config/presubmit/config.json for an existing output_base. If it exists and the path is writable, use it.

If it does not exist, ask the user (use AskUserQuestion):

Where should presubmit reviews be stored by default?

Offer at least these options and one custom path:

• ~/presubmit-reviews/ — generic, no project-folder assumption
• ~/Documents/presubmit/ — under Documents
• ~/Documents/GitHub/pre-submission/ — for users who keep all repos under ~/Documents/GitHub/
• Custom path

After the user picks, write the choice to ~/.config/presubmit/config.json:

{
  "output_base": "/absolute/path/the/user/picked",
  "saved_at": "ISO 8601 timestamp"
}

Also offer to write export PRESUBMIT_OUTPUT_BASE=… to ~/.zshrc so the bare CLI (without this skill) can pick up the same default. Make this offer explicit; do not write to .zshrc without asking.

The config file is the source of truth for this skill; the env var is a convenience for direct CLI invocation.

Per-paper run phase

Once setup is done, every invocation follows the same pattern.

Step 1 — Slug

Read the input filename. Derive a default slug:

• Strip extension and path.
• Lowercase.
• Replace runs of non-alphanumeric characters (other than underscores, which are preserved) with single hyphens.
• Trim leading/trailing hyphens and underscores.
• Aim for <lastname>_<year>_<short-title> shape if the filename already follows it.

Example: Denney_2026_What-Were-They-Thinking.pdf → denney_2026_what-were-they-thinking.

Confirm the proposed slug with the user via AskUserQuestion. Allow override.

Step 2 — Mode

Ask which run mode (AskUserQuestion):

• Smoke — --stop-stage 2.0. Runs metadata extraction + Red Team + numbers auditor. ~15–25 min on a 70-page paper, ~$1–2. Useful for verifying setup or catching show-stopper issues fast.
• Standard — full pipeline. ~30–90 min, ~$5–10. The default for a real audit.
• Custom — ask for additional flags (--code-dir, --math, --supp, --no-copyedit, --no-editor-note, --start-stage, --stop-stage, --skip-size-check).

Step 3 — Construct paths and run

WORK_DIR="$OUTPUT_BASE/$SLUG/presubmit_run"
mkdir -p "$WORK_DIR"
"$PRESUBMIT_DIR/.venv/bin/presubmit" "$PAPER_PATH" \
  --work-dir "$WORK_DIR" \
  -o "$OUTPUT_BASE/$SLUG/report.txt" \
  $EXTRA_FLAGS

Always pass -o: without it the CLI copies the final report to report.txt in the current working directory, leaving stray clutter wherever the agent happened to be.

Run in the background using the Bash tool's run_in_background: true. Stream the log to a file so the user (and you) can check progress separately.

Tell the user: the wall time, where to watch the live log (tail -f instructions), and what files to expect in $WORK_DIR as stages complete.

Step 4 — Report when done

When the background task notifies completion:

1. Confirm exit code is 0 and no FATAL: Claude refused appears in the log. (A smoke run — --stop-stage — also exits 0, printing Stopped at stage N as requested; judge it by the per-stage files in $WORK_DIR, since no consolidated report exists by design.)
2. Locate the consolidated report — it's the file matching $WORK_DIR/<slug>_*.txt (presubmit auto-names it <author_title_uuid>.txt), with a stable-named copy at the -o path.
3. Report wall time, total tokens (input + output across stages — visible at the end of the log), and the end-of-run dollar total (pricing.csv carries current Claude rates; cross-check the Anthropic console if rates have changed).
4. Offer to open the report (less / Read) and to write a per-paper README.md alongside the work_dir capturing: invocation date, flags used, models, wall time.

If the run failed:

• Messages.create() got an unexpected keyword argument 'thinking' — anthropic SDK is < 0.60. Fix: pip install -U 'anthropic>=0.60' in the venv.
• FATAL: Claude refused the request (likely safety policy) — a Red Team prompt tripped Claude's safety filters. The message does not name the stage; find the last ► Executing <stage> line above it in the log, then locate that stage's prompt under $PRESUBMIT_DIR/src/presubmit/prompts/. Soften it to attack the manuscript's claims, not the authors. Re-run; the pipeline is resumable.
• Marker conversion failure — surface the specific PipelineError. Common cause: marker-pdf install incomplete; verify pip show marker-pdf succeeds in the venv.
• Out-of-credit — top up at https://console.anthropic.com/, then re-run. The pipeline picks up from where it stopped.

File-naming and organization convention

$OUTPUT_BASE/                                            (from config; user-chosen)
└── <slug>/                                              (one folder per paper)
    ├── README.md                                        (offered after the run — never silently written)
    ├── report.txt                                       (stable-named copy of the report, via -o)
    └── presubmit_run/                                   (the --work-dir)
        ├── <author_title_uuid>.txt                     ← THE main consolidated report
        ├── original_source.pdf                          (cached source)
        ├── paper.md                                     (marker conversion of source)
        ├── metadata.json
        ├── pipeline_execution.log
        ├── 00a_metadata.txt … 09c_copyedit.txt          (intermediate per-stage outputs)
        └── 10_latex_body.txt                            (body without LaTeX framing)

Slug rule: <lastname>_<year>_<short-title>, lowercase with hyphens in the title. Example: denney_2026_what-were-they-thinking. Auto-derived from the input filename; user-overridable in the per-paper interview.

The main report is <author_title_uuid>.txt — it consolidates all stages into one file with these sections: header, disclaimer, overview, Editor's Note, Summary (Is It Credible? + Bottom Line), Potential Issues, Future Research, Copyediting, Proofreading. Read this first. The other files are intermediates; the raw 01a_breaker.txt, 01b_butcher.txt, etc. have unfiltered Red Team findings that are sometimes sharper than the consolidated version.

When to use this skill vs. paper-review-lite

	presubmit (this skill)	paper-review-lite (sister skill)
Where the work happens	Outside Claude Code — Python CLI calls Anthropic API	Inside Claude Code — parallel sub-agents read the paper
Cost	Per-token, billed to your API key (~$5–10/run)	Subscription only (no per-token bill)
Wall time	30–90 min unattended	Minutes; you control each pass
Depth	30+ stages: Red Team (Breaker, Butcher, Shredder, Collector, Void) + Blue Team defence + verification cascade + legal pass + copyedit + Writer Mode	~11 sub-agents: content/argument, numbers, references, DOIs, writing, CONSORT, pre-reg, figures, archive, plus 2 cross-checkers
Output	Single consolidated .txt deliverable + ~30 intermediate files	Structured pre-submit report in-conversation + .review-tmp/ scratch files
Resumable	Yes — checkpointed per stage to disk	No — single conversation pass
Math audit	Yes (--math, requires Mathpix)	No
Replication-code audit	Yes (--code-dir)	Partial (Agent 9 checks archive completeness; doesn't compare claims to code)
Refusal risk	Moderate (some Red Team stages adversarial enough to trip safety)	Low (single-pass personas, quote-grounded)
When to use	Deep audit before submission; standalone deliverable; math or code audit	Quick in-flow check; routine self-audit; no API spend

Both are legitimate self-audit tools. paper-review-lite is the everyday tool; presubmit is the heavy-artillery final pass before submission.

Quality checks (apply consistently)

• Setup phase ran first if any of: presubmit not installed, ANTHROPIC_API_KEY not set, no ~/.config/presubmit/config.json exists.
• Output base is read from ~/.config/presubmit/config.json (or asked for and saved if not), never hardcoded.
• Slug is auto-derived from the input filename and explicitly confirmed with the user before invocation.
• The user picked a run mode (smoke / standard / custom) before the run started.
• Run was launched in the background so the user can continue working; live log path was reported.
• On completion: exit code, presence of FATAL lines, wall time, total token usage, and full path to the consolidated report were all reported.
• If the run failed, the specific error was diagnosed against the known failure modes above before suggesting a generic retry.
• Per-paper README.md capturing run metadata was offered (not silently written).

Known gotchas (current as of 2026-06)

1. anthropic SDK version conflict. presubmit's pyproject.toml pins anthropic>=0.60 directly (core.py's Messages.create(thinking=…) needs it), but marker-pdf 1.10.x transitively caps anthropic at <0.47. pip resolves the conflict by backtracking marker-pdf to an older release, or by warning. After install, check pip show anthropic marker-pdf; if anthropic landed below 0.60, force it with pip install -U 'anthropic>=0.60' (runtime is unaffected — presubmit doesn't use marker's optional anthropic-LLM mode).
2. -o defaults to ./report.txt. -o / --output controls the final report copy only — without it, a stray report.txt lands in the invoking directory; without --work-dir, stage outputs land in a temp dir that gets garbage-collected. Always pass both. This skill does so automatically.
3. use_search=True is a no-op. Stage 00a (metadata) silently degrades for published papers needing a citation lookup; fine for unpublished manuscripts.
4. First marker conversion is slow. 3–5 GB of model weights download into marker's local cache (macOS: ~/Library/Caches/datalab/models/; Linux: ~/.cache/datalab/models/) on first use; subsequent runs reuse the cache.
5. Older checkouts exit 1 on intentional --stop-stage runs. Current presubmit exits 0 with Stopped at stage N as requested; if you see exit 1 with "did not produce a final report" after a smoke run, the install predates the fix — git pull && pip install -e ..