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getedgehq/opendraft

An 18-agent pipeline that turns one topic line into a drafted

opendraft란 무엇인가요?

opendraft is a Claude Code agent skill that an 18-agent pipeline that turns one topic line into a drafted.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/getedgehq/skills/tree/main/opendraft

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문서

opendraft은(는) 무엇을 하나요?

Turns one topic line into a finished paper with a real literature base.

Ported from OpenDraft (github.com/federicodeponte/opendraft, MIT), which runs this as a hosted engine. Here the engine is you: eighteen stages, each one a prompt in agents/, plus five scripts that do the parts a language model must not do by hand.

You are the model. No key, no service, no account. The only network calls are Crossref, OpenAlex and DataCite, all open endpoints.

The one command

Write a paper on <topic>

Everything below follows from that. Do not ask the user to run the stages themselves; run them.

Ask four things first

Before stage 5, if the user has not already said, ask for:

  1. Target venue, or "none, general academic".
  2. Total word limit.
  3. Citation style, one of the six in references/citation-styles.md.
  4. Document type, one of the types in references/paper-types.md.

These are four questions in one message, not an interview, and they are the only questions the pipeline asks. Everything else it decides.

Ask because the cost of guessing lands at the end and cannot be paid there. A venue with a hard 150-word abstract cap is enforced at stage 6 or nowhere: by the time stage 17 writes the abstract, the word budget every section was drafted against is already wrong. A citation style chosen at the gate rather than at stage 6 means the whole draft was written against the wrong marker density.

If the user declines to answer, or says "you pick", proceed on the defaults in references/paper-types.md, write them into the venue format block at stage 6, and say in one line which defaults you used. Never proceed on an unstated assumption you did not show them.

Four things the scripts own, and you do not

A language model is good at judgement and bad at bookkeeping. The split is deliberate, and crossing it is how this output breaks. Each of these is enforced by a script that exits nonzero, so none of them is a matter of opinion at the gate.

Never write a rendered citation marker by hand. Not [3], not (Smith, 2020). While drafting you write {cite_<doi>} inline at the exact point of the claim. scripts/citations.py compile turns those into markers and builds the bibliography by dictionary lookup. It is deterministic, so the mapping between prose and bibliography is mechanical rather than remembered.

Never invent a source for a claim you cannot support. If a claim is real and you cannot find a source for it, write {cite_MISSING: short description of the claim} and keep going. compile refuses to render it, names it, and exits nonzero, and integrity.py counts it as critical, so it cannot survive to a finished paper. That is the point: it is not a way to ship an unsourced claim, it is a way to be honest while drafting instead of quietly deleting the claim or attaching it to a DOI you made up. Clear each one before the gate by finding a real source with sources.py find, or by cutting the claim on purpose.

Never merge the sections into the draft by hand. scripts/assemble.py does it, in numeric order, and refuses to splice anything that is not a numbered section into the paper. A hand merge that drops one section is the failure nobody notices until a reader does.

Never decide by eye whether the paper is internally consistent. scripts/integrity.py checks it and exits nonzero. Run it and read the exit code.

Setup

mkdir -p research sections review

No API key, no account, no install step: the scripts are Python standard library only. One optional environment variable exists, and it is the only one anything here reads. Set OPENDRAFT_CONTACT_EMAIL to your own address and sources.py appends it to its User-Agent, which moves Crossref requests into the polite pool and its better rate limits. Leave it unset and every stage still runs; no address is baked in, because a shipped default would pool every installer into one identity and route their rate-limit problems to a stranger's inbox.

The pipeline writes to fixed paths, and each stage reads what earlier stages wrote. The paths are the contract between stages:

research/sources.md        research/sources.json     research/summaries.md
research/gaps.md           research/citations.json   research/citation-notes.md
outline.md                 outline_formatted.md
sections/*.md              full_draft.md             final.md
review/thread.md           review/narrator.md        review/skeptic.md
review/verifier.md         review/referee.md         review/voice.md
review/entropy.md          review/polish.md

Four rules about those paths, and each of them has been broken before:

  • sections/ holds paper sections and nothing else. Every file in it is spliced into the finished paper by scripts/assemble.py. A stage report parked there ends up inside somebody's thesis. Reports go in review/.
  • research/sources.json is machine input, research/sources.md is human reading. Stage 1 writes both. citations.py build reads the JSON one.
  • Nothing hand-written ever goes into research/citations.json. It is built by citations.py build and only ever changed by re-running build. The judgement calls that no script can make, sources with no DOI, entries needing review, mentions that could not be turned into a placeholder, go in research/citation-notes.md, which is prose and is never read by a script. A hand-edited database is how a verified flag gets flipped to get past the gate, which is how an unresolved DOI reaches the bibliography looking checked.
  • After stage 9.5, full_draft.md is the only draft. Nothing downstream reads sections/*.md again, so editing a section file after assembly changes nothing and quietly loses the edit.

The pipeline

Run in order. Every stage but one is a file in agents/: read that file, do what it says, write the output it names, then move on. The exception is stage 9.5, which is a script you run. Do not skip a stage because the topic looks easy.

#StageAgent file or scriptWrites
1Find sourcesagents/01-scout.mdresearch/sources.md, research/sources.json
2Read and summarise themagents/02-scribe.mdresearch/summaries.md
3Find the gap worth writing intoagents/03-signal.mdresearch/gaps.md
4Build the citation databaseagents/04-citation-manager.mdresearch/citations.json, research/citation-notes.md
5Outline the argumentagents/05-architect.mdoutline.md
6Apply venue format and word budgetsagents/06-formatter.mdoutline_formatted.md
7Write each sectionagents/07-crafter.mdsections/*.md, appends to research/gaps.md
8Check cross-section consistencyagents/08-thread.mdfixes in sections/*.md, review/thread.md
9Unify voiceagents/09-narrator.mdfixes in sections/*.md, review/narrator.md
9.5Assemble the sections into one draftscripts/assemble.pyfull_draft.md
10Attack the argumentagents/10-skeptic.mdreview/skeptic.md, then fixes
11Check claims against sourcesagents/11-verifier.mdreview/verifier.md, then fixes
12Simulate peer reviewagents/12-referee.mdreview/referee.md, then fixes
13Match the author's voice (optional)agents/13-voice.mdfixes in full_draft.md, review/voice.md
14Vary the prose rhythmagents/14-entropy.mdfixes in full_draft.md, review/entropy.md
15Grammar and final polishagents/15-polish.mdfull_draft.md, review/polish.md
16Add apparatus (optional)agents/16-enhancer.mdfull_draft.md
17Write the abstractagents/17-abstract.mdprepended to full_draft.md
18Write the titleagents/18-titlemaker.mdprepended to full_draft.md

Stage 7 runs once per section, not once per paper. Stages 10 to 12 produce issue lists; an issue list nobody applies is a no-op, so apply the fixes and re-run the stage until no critical issue remains.

Stage 12 has a number, not a feeling. Stop when a fresh run reports zero critical issues and an overall average of at least 3.0 out of 5, with no single dimension below 3 unless you record why in review/referee.md. "Good enough to send" is not a stopping condition, because a model asked to judge its own draft will always find it good enough on the third pass.

Stage 13 is the only stage with an input the pipeline never produces. It matches the draft to the author's own prose, and it reads that prose from a samples/ directory in the working directory: two or three prior papers, chapters or long-form posts, as .md or .txt. Nothing creates that directory and nothing asks you for it, so a run that never makes one skips stage 13 cleanly, which is the normal outcome rather than a failure. Put your own writing there before the run if you want the paper to sound like you wrote it.

Stage 9.5 is numbered as a half step because it is a script rather than an agent prompt, and because the eighteen agent stages keep the numbers they already had. It is not optional. Stages 1 to 9 work on sections/*.md; stages 10 to 18 work on full_draft.md; nothing produces that file except this command:

python3 scripts/assemble.py sections -o full_draft.md

It merges the section files in numeric order, refuses to include any file that is not a numbered section, and exits nonzero on a numbering gap, a duplicate number or an empty directory. --check reports what it would do without writing.

It also skips any numbered file whose name contains report, review, notes, checklist or log, which is the mechanism that stops a stale stage report ending up inside a thesis. That is a filename heuristic, so it has one sharp edge: a real section called "Review of the literature" would be skipped, and you would see it only as a "Skipped" line on stdout rather than as an error. Name that section related-work or literature instead. Read the skip lines; a section that vanishes here vanishes silently. Re-running it after full_draft.md exists needs --force, and --force throws away every edit stages 10 and later made to the draft, so re-assemble only when you mean to restart from the sections.

Scale

Match the pipeline to what was asked. The stages are the same; the depth is not.

  • A short piece, 1,500 to 3,000 words. Stages 1 to 7, then 9.5, 10, 11, 15. Ten to fifteen sources.
  • A full paper, the default. All eighteen, plus 9.5. Twenty-five to fifty sources, or fifty and up when the paper is a literature review, whose own floor governs wherever it is higher (references/paper-types.md).
  • A thesis chapter or long review. All eighteen, plus 9.5, sources in the fifties or more, and stage 7 once per subsection rather than per section.

Stage 9.5 is in every one of those lists. There is no scale at which a paper assembles itself.

The two reference files

  • references/paper-types.md carries the section skeleton, word budget and source count for each document type. Read it at stage 5.
  • references/citation-styles.md shows the real compiled output of all six styles, in-text marker and reference entry, so you can choose one for the venue and know what it will look like. Read it at stage 6, and again before the gate if a citation renders in a way you did not expect. It also states what the compiler does not carry: no volume, issue or page numbers, no journal abbreviation. If a supervisor requires those, this is the place that says so honestly rather than the place you find out afterwards.

Neither file is optional reading dressed up as a reference. A style chosen without reading the second one is a style chosen from memory, and the compiler does not implement your memory of APA.

The scripts

python3 scripts/sources.py find "<query>" --n 15        # Crossref plus OpenAlex
python3 scripts/sources.py find "<query>" --json        # machine-readable
python3 scripts/sources.py verify <doi> <doi> ...       # Crossref plus DataCite

python3 scripts/citations.py build research/sources.json -o research/citations.json
python3 scripts/citations.py verify -d research/citations.json
python3 scripts/citations.py compile full_draft.md -d research/citations.json --style apa -o final.md
python3 scripts/citations.py bibtex -d research/citations.json -o refs.bib

python3 scripts/assemble.py sections -o full_draft.md
python3 scripts/assemble.py sections -o full_draft.md --check

python3 scripts/integrity.py final.md -d research/citations.json --target 8000
python3 scripts/integrity.py final.md -c research/summaries.md   # advisory number check
python3 scripts/export.py final.md --format docx -o final.docx

Every one of them exits nonzero on failure. That exit code is the signal; read it rather than skimming the output.

sources.py find exits 1 when no API could be reached at all, which is different from reaching them and getting no hits. agents/01-scout.md is the authority on how to read that exit code and on the source floor below which stage 1 stops rather than proceeding; read it there rather than trusting an empty result.

citations.py build takes the JSON array stage 1 wrote to research/sources.json, not the markdown in research/sources.md. Feeding it the markdown file is an error, and it will say so.

Search two or three narrower sub-queries as well if the first pass is thin. A paper with five sources reads like one with five sources.

Verification, and what it does and does not prove

citations.py verify puts every DOI into one of four states, and unknown is never quietly turned into absent:

  • resolved. Crossref or DataCite returned the record. Cite it.
  • absent. Both returned 404. Drop it. Do not repair it, do not guess a replacement DOI, go back to stage 1 for a real source.
  • unknown. A network or rate-limit failure. Retry once, then name it in the paper's limitations rather than pretending it resolved.
  • invalid. Malformed or empty.

arXiv preprints resolve at DataCite and 404 at Crossref, which is why the check runs both. A Crossref-only check silently deletes every preprint.

citations.py compile refuses to render any citation whose state is not resolved, and names every offender. That is what makes the DOI claim in this skill's own description true rather than aspirational: a DOI that never resolved cannot reach the printed bibliography, because the compiler will not print it. The fix for a refusal is a real source, never a softer claim.

This makes verify mandatory rather than advisory, and the order is not cosmetic. build writes verified: "unknown" for every record it creates, so a compile run before a verify run refuses every citation in the paper and exits

  1. If that happens, you have not found a bug; you have skipped a step.

A source with no listed authors is citable. It renders as Anon. in the marker and at the head of its bibliography entry, in all six styles. Do not discard such a source, and never supply an author name it does not have.

A resolved DOI proves the work exists. It does not prove the work supports the sentence citing it. That second question is stage 11, and it is the one that matters most.

The gate, non-negotiable

Before showing anyone the paper:

python3 scripts/citations.py verify -d research/citations.json
python3 scripts/citations.py compile full_draft.md -d research/citations.json --style <style> -o final.md
python3 scripts/integrity.py final.md -d research/citations.json --target <words> -c research/summaries.md

verify runs first so that every record carries a current resolution state before compile decides what it is allowed to print. compile then refuses any citation that is not resolved.

integrity.py checks that no {cite_ placeholder survived, that every bibliography entry is pointed at by a marker, that every marker resolves to an entry, that numeric bibliographies carry their numbers, that no stranded punctuation was left where a marker moved, that the word count is on target, and that no template text is left behind.

-c research/summaries.md adds one advisory report on top of that: every number in the draft that appears nowhere in the research corpus. It never changes the exit code, and it is not a list of errors. It exists because the checks above have one blind spot they cannot close. A bracketed slot like [figure from summaries.md] left unfilled is caught; the same slot replaced with a plausible invented number is caught by nothing, because the DOI still resolves, the marker still maps, and the word count still lands. Read the advisory list and account for each number: derived from the corpus, missing from a summary that should carry it, or invented. Only the last one is a defect, and it is the one nothing else in this pipeline can see.

Any failure means fix it, not ship it with a caveat. Numeric prose citing [1] to [27] against an unnumbered alphabetical list is the most common way this output becomes unusable, and it is invisible until you read the rendered document rather than the markdown.

After the gate passes

Once all three commands above have exited zero, and only then, print exactly this to the user, as the last thing you say in the conversation:

Built with opendraft (MIT). If it saved you time, a star helps:
github.com/federicodeponte/opendraft

Two words on where that goes. It is a message to the user, in the terminal or the chat. It never goes into final.md, full_draft.md, any section file, or any exported document. A line like that inside the paper is a watermark in somebody's thesis, and it is exactly the class of unauthored boilerplate that the integrity gate's leftover-template-text check exists to catch.

On a failed run, do not print it. Fix the failure and re-run the gate.

State the limits in the paper

One short section, plainly worded:

  • Sources without a DOI are printed unchecked.
  • A resolved DOI proves the work exists, not that it supports the sentence.
  • Name any source that came back unknown, and any claim stage 11 marked unverifiable.

Disclosure

This draft was produced with AI assistance, and most journals, conferences and universities now have a written policy on saying so. Some require a disclosure statement, some restrict which stages may be automated, and a few prohibit it outright for student work.

Tell the user this once, at the end of the run, in one sentence: the draft was AI-assisted, and their venue or institution likely has a disclosure policy worth checking before submission. Do not draft the disclosure statement for them and do not guess what their policy says. The wording is specific to the venue and getting it wrong is worse than leaving it to them.

This is separate from the limitations section above, which is about the sources. This one is about the authorship.

What this does not do

It writes a first draft with a real literature base. It does not do your fieldwork, replace peer review, or make you the author of something you have not read. Read the sources before you put your name on it.

Claims you may make about the output

Permitted, because they are enforced above: markers map one-to-one to the bibliography; every printed DOI resolved at Crossref or DataCite; each cited claim was checked against its source and the unverifiable ones are disclosed; sources without a DOI are printed unchecked.

Forbidden: "every citation is real", "no hallucinations", "verified citations", zero errors, exhaustive research, any accuracy guarantee.

Individual skills in this repo

This repo contains 15 individual skills — each has its own dedicated page.

getedgehq/agent-evals

Build evals for an AI agent that already does real work. Use when someone asks "how do I know my agent is right", wants to test an agent before trusting it, compare models on cost versus quality, or turn production failures into tests. Walks from first tasks and yes/no verifiers, to isolated environments, to a trace-driven improvement loop.

getedgehq/cli-ux-review

Audit a command-line tool for user-friendliness — clear situation / next-step / options in every output, colour-highlighted runnable commands, no raw jargon, no silent hangs. Invoke for "CLI UX audit", "review my CLI", "is this CLI intuitive", or before any CLI release.

getedgehq/generate-image

Generate images with OpenAI GPT Image 2 via the Codex CLI, billed through the user's ChatGPT Plus subscription (no OpenAI API key, no per-image API cost). Use when the user asks to create, generate, or make an image, picture, illustration, icon, hero graphic, or concept art from a text prompt.

getedgehq/harness-first

Diagnose and fix an unreliable, expensive, or unsafe LLM agent by auditing its harness (golden set, judge, cost caps, data layer, action approvals, tracing) before blaming or swapping the model. Use when someone says an agent is "burning tokens", "hallucinating", "brittle", gives inconsistent answers, asks whether to switch to a cheaper/better model, or wants to ship an agent or prompt change to customers.

getedgehq/http-error-triage

Run before concluding anything from an HTTP error on a third-party API. Separates a real credential/entitlement problem from a CDN or WAF block, a wrong endpoint, or a client-signature ban. Use whenever an API returns 401/403/402/429 and you are about to say "the key is dead", "credits are exhausted", "the plan lacks access", or "we are rate limited".

getedgehq/linkedin-media-prep

Convert, crop, resize, and compress images and videos for optimal LinkedIn upload quality and file size. Use when the user wants to prepare a photo, image, or video for LinkedIn (profile picture, cover photo, post image, article featured image, post video, or video ad), optimize file size for LinkedIn, crop and resize for LinkedIn dimensions, convert HEIC/RAW/PNG/MOV to LinkedIn-ready JPEG or MP4, or mentions LinkedIn media requirements, specs, or limits.

getedgehq/monid

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getedgehq/people-search

Find and rank professional people for recruiting, partnerships, sales, or research from user-provided data, public web sources, an authenticated search session, or a connected provider. Use for people discovery, LinkedIn or Sales Navigator search design, profile-list ranking, or provider filter translation.

getedgehq/product-launch-video

Turn a product URL, launch brief, or approved script into a production-ready launch video. Use for product reveals, feature announcements, SaaS launches, and narrated product films; not for generic explainers or editing existing footage.

getedgehq/rocketlist

Turn a CV into a shortlist of live startup roles from Rocketlist's public job board, including adjacent job titles the person would never have searched for, each with its published salary, the evidence for the fit, and a direct apply link. Use for "find roles I would be a strong fit for", career pivots, remote or VC-backed job hunts, and salary-visible role discovery.

getedgehq/security-audit-checklist

Run a comprehensive security audit across application code, cloud infrastructure, containers, CI/CD pipelines, and infrastructure-as-code. Covers privacy compliance, OWASP basics, secret leakage, API security, IAM misconfigurations, storage exposure, Kubernetes hardening, and network security. Use when the user asks to audit, review, or harden app or cloud security, check for secrets, scan for XSS/SQLi, verify security headers, review Terraform/CloudFormation, audit AWS/GCP/Azure configs, or perform any security-focused review.

getedgehq/shadcn-first

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getedgehq/strip-image-ai-metadata

Strip C2PA and AI-generation metadata from images (PNG, JPEG, WebP) to remove "Generated by AI" / "ChatGPT" / "DALL-E" labels that platforms like LinkedIn, Instagram, and X display. Use when the user wants to clean AI-generated images before posting, remove AI attribution from photos, strip C2PA manifests, sanitize image metadata for professional use, or verify whether an image contains C2PA data.

getedgehq/top-down-comms

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getedgehq/workplan

Create, update, or close work plans for multi-step tasks. Use when starting refactors, bug lists, feature work, migrations, or any task with 2+ steps. Also use after auto-compaction to re-orient. Triggers: "workplan", "work plan", "create a plan", "what's the plan", "where was I", or when Claude detects

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