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Autonomous Research

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federicodeponte
autonomous-research

An 18-agent pipeline that turns one topic line into a drafted research paper, literature review, or thesis chapter. Finds real sources at Crossref and OpenAlex, drafts each section against them, attacks its own draft for weak claims, then compiles citations deterministically so in-text markers map one to one onto the bibliography and every printed DOI resolved at Crossref or DataCite. Every agent is a markdown file you can open and edit. Pure stdlib, no API key, no account. Use when someone asks to write a research paper, do a literature review, draft a thesis chapter, find sources on a topic and write them up, or names opendraft or openpaper.

Overview

Publisherfedericodeponte
Repositoryopendraft
Skill nameautonomous-research
Stars
509
Forks
87
Bundled files
56
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 56 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by federicodeponte on GitHub. Read the source before you install it.

Installation

Install the Autonomous Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/federicodeponte/opendraft/tree/master/skills/autonomous-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/federicodeponte/opendraft.git /tmp/opendraft
mkdir -p .claude/skills
cp -r /tmp/opendraft/skills/autonomous-research .claude/skills/autonomous-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autonomous Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Autonomous Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Autonomous Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Autonomous Research

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 and one evidence stage, each a prompt in agents/, plus the 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.

Write down the brief's own numbers, then check against them

Most briefs arrive with numbers already in them, in ordinary words: "about 3,000 words", "1200-1500 words", "a couple of dozen sources at least", "caps the main text at 6,000 words and the abstract at 200", "thirty or more works", "cover the hospital series, the exposure problem and the policy evaluations". Each of those is a requirement, and each of them is a requirement this pipeline loses if nobody writes it down, because every stage after the first works from an outline rather than from the request.

So write them down. At stage 6, alongside the venue format block, record what the user asked for in research/brief.json:

json
{
  "word_range": [2700, 3300],
  "min_references": 24,
  "abstract_max_words": 250,
  "required_sections": ["Introduction", "Methodology", "Discussion", "Conclusion", "References"]
}

Four rules about that file:

  • Every value in it traces to a sentence in the brief, or to the document type's own skeleton in references/paper-types.md where the user left the choice open. A number nobody asked for does not belong in it.
  • "About N" is a range, not a point. "About 3,000 words" is a request to land near three thousand, so record a band around it rather than the single number; ten percent either side is the same tolerance integrity.py has always used for --target. A stated cap ("no more than 6,000") is a ceiling and is recorded as one.
  • Stated minimums are floors, and the only way past one is more sources. "A couple of dozen at least" records as 24 and stays 24. See "Keep searching until the count is met" below.
  • Omit what the brief does not state. Nothing in the file has a default, and an absent key is simply not checked. An invented cap is as wrong as a missed one.

integrity.py then reads that file at the gate and checks the draft against it, so the numbers the user stated are tested against the delivered document rather than remembered.

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

bash
mkdir -p research/texts 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
research/brief.json        research/abstracts.json   research/evidence.json
research/index.json        research/texts/*.txt      research/figures/*
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          review/evidence.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
4.5Extract the evidence (reviews)agents/04.5-evidence.mdresearch/abstracts.json, research/texts/*.txt, research/evidence.json, research/index.json, research/figures/*, review/evidence.md
5Outline the argumentagents/05-architect.mdoutline.md
6Apply venue format and word budgetsagents/06-formatter.mdoutline_formatted.md, research/brief.json
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 4.5 runs for literature reviews, scoping reviews and any paper that compares what several studies found about the same outcomes, and is skipped otherwise. It turns findings into research/evidence.json, one verbatim quote per finding, and the scripts then compute the evidence index and draw every evidence figure and table from that file. Its number is a half step for the same reason 9.5's is.

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.

Keep searching until the count is met

Stages 1 and 4 are a loop, not a pass. Run them, then count what survived:

bash
python3 scripts/citations.py verify -d research/citations.json

It prints resolved=N and that N is the real number: it is what the paper can cite, after the DOIs that turned out to be absent, invalid or unreachable have come out. Compare N against the floor, which is whichever is higher of the min_references the brief stated and the per-type floor in references/paper-types.md.

If N is below the floor, go back to stage 1 and search again. Not the same queries: different ones. The pool ran short because the queries ran out of angles, so widen along the ones the brief itself names, the adjacent literature, the sub-questions the topic decomposes into, the review articles that would cite this work, the outcome measures by name. Then merge into research/sources.json, re-run citations.py build and verify, and count again. Keep going until N clears the floor, or until further queries stop returning anything new.

This is where verification discipline turns into its own failure mode. The right instinct, refusing to cite what did not verify, has a wrong ending: a review that drops half its sources at the verification step and then ships the half that survived has not been careful, it has been short. Fifty found and ten verified is not a ten-source review, it is a search that has to continue. The pool is a floor on what you go and find, and the only two honest ways to reach the end of this loop are to meet it or to tell the user plainly, in the delivered document, how many sources you verified and why the literature would not yield more.

Never close the gap the other way. Do not lower the floor to what you have, do not cite a DOI whose state is not resolved, do not pad the list with sources the paper never cites, and do not count one work twice under two identifiers.

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:

bash
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, 17. Ten to fifteen sources, or the brief's own minimum wherever it asks for more.
  • A full paper, the default. All eighteen, plus 9.5, and 4.5 for a review. 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, and 4.5 for a review, sources in the fifties or more, and stage 7 once per subsection rather than per section. A long review runs 8,000 to 12,000 words on sixty sources or more; its length comes from covering more of the literature and reporting more of it in the evidence tables, never from restating the same findings at greater length.

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

Stage 17 is in every one of those lists too. Every section skeleton in references/paper-types.md opens with an abstract, so a tier that skipped the stage that writes one produced a paper missing its first section, and the word it goes under is the only thing scale changes: a journal article has an abstract, a committee paper or an evidence brief has a summary, and both are the same section doing the same job. Write it in the document's own register and keep it inside whatever cap the brief states. Where the brief states none, agents/17-abstract.md carries the fallback range.

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

bash
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 -b research/brief.json     # the brief's own numbers
python3 scripts/integrity.py final.md --stats                    # count, do not check
python3 scripts/integrity.py final.md -c research/summaries.md   # advisory number check
python3 scripts/export.py final.md --format docx -o final.docx
python3 scripts/export.py final.md --format pdf -o final.pdf --template journal --kind "Narrative review"

python3 scripts/evidence.py abstracts research/sources.json -o research/abstracts.json
python3 scripts/evidence.py check research/evidence.json --abstracts research/abstracts.json --texts research/texts -d research/citations.json
python3 scripts/evidence.py index research/evidence.json -o research/index.json
python3 scripts/evidence.py figures research -o research/figures

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.

Length is a number, not an impression

Count the draft before delivering it, and count it again after every trim:

bash
python3 scripts/integrity.py full_draft.md --stats

That prints the main text's word count, the abstract's, the number of reference entries and the headings. Main text means the body: the reference list and the abstract are counted separately, because a brief that caps the main text and the abstract as two numbers is treating them as two numbers.

Do this at stage 15, before the gate, and act on what it says. Over the range, cut, and cut the paragraphs that repeat an argument already made rather than shaving a word from every sentence. Under it, go back to the sources and write what the pool actually supports; if the pool does not support more, that is a finding about the literature, and it goes in the paper as one instead of being padded over.

The estimate is the thing to distrust here. A draft that feels like five thousand words can be nine, and a paper that comes in half again over a stated cap is not a long paper, it is one the venue will not take and the reader will not finish. Neither the model that wrote the prose nor the person reading it back can tell six thousand words from nine and a half by eye; the command above can, and it takes a second.

The docx a reader opens

scripts/export.py sets the body font, the body size and the page margins on the exported docx, through the document's Normal style rather than stamped onto each paragraph, so a reader who restyles Normal restyles the paper. The defaults are Times New Roman, twelve point, one inch margins, which is the ordinary manuscript setting; --font, --font-size and --margin-inches change them where a venue's house style asks for something else.

This is set rather than left alone because pandoc's own default names no body font at all, which means the file opens in whatever the reader's word processor calls Normal, and on a current Word that is Calibri. A literature review for a supervisor, a brief for a committee and a manuscript for a journal are all documents whose typography somebody is expected to have decided. Arriving in the word processor's default is the one outcome that says nobody did.

The journal PDF

scripts/export.py --template journal typesets final.md as a two-column journal article through scripts/journal.py: a masthead, a serif title with the subtitle split off at the first colon, a two-column abstract, numbered sections, merged runs of adjacent author-year citations, and, when stage 4.5 ran, an at-a-glance strip, the evidence figures, the two evidence tables and the index equations at the draft's placeholder lines. The fonts, STIX Two Text and Source Sans 3, ship in assets/fonts/ under the SIL Open Font License. It needs pandoc and, for the PDF, weasyprint.

It invents nothing a journal page usually carries. There is no journal name, volume, issue, received date, affiliation or DOI unless you pass it: --brand sets the masthead name (default OpenDraft), --kind the article type line, --byline and --masthead-note the two lines of small print, and each takes only what is true of this paper. A placeholder whose figure was never drawn stops the export with the command that draws it, rather than printing the braces or leaving a hole. The docx, latex and plain html exports cannot place those figures, so they drop the placeholder lines and name each one on stderr.

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:

bash
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 -b research/brief.json -c research/summaries.md

When stage 4.5 ran, two more commands belong to the gate, because the figures are only as current as the file they were drawn from:

bash
python3 scripts/evidence.py check research/evidence.json --abstracts research/abstracts.json --texts research/texts -d research/citations.json
python3 scripts/evidence.py index research/evidence.json -o research/index.json
python3 scripts/evidence.py figures research -o research/figures

check must exit 0: every quote found verbatim in its source text, every study's DOI in the citation database. Then compare every S, M and leave-one-out number in final.md against what index just printed. A number that differs is a stale number, and the draft is corrected to the file, never the other way round.

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.

With -b it also checks the five things the user asked for, and this half of the gate is the one that answers for the finished document rather than for its citations:

  • the main text lands inside the word range they stated,
  • the abstract or summary is there and inside its cap,
  • the reference list meets the minimum they stated,
  • every section they named by name exists,
  • and, as it always has, every in-text citation resolves to an entry in the reference list and every entry is cited.

That last one is the one nobody checks by eye and nobody gets right by eye. A reference list is long, the markers are scattered through twenty pages, and a single entry that lost its marker in a late cut looks exactly like twenty-nine that did not. It is checked here or it is not checked.

Run this against the file you actually hand over. Exporting final.md to docx or pdf does not change its words, so checking final.md checks the export; but a paragraph edited straight into the delivered markdown after the gate ran was never checked at all, and the whole gate has to run again. There is no version of this where the thing delivered and the thing checked are different files.

If one of these truly cannot be satisfied, say which one, in one plain sentence to the user and in the document's own limitations section: the reference list came to nineteen and not to twenty-four, and here is what the searching turned up. An unmet requirement that is named is a fact the reader can act on. An unmet requirement that is silent is one they find out about from the person they sent the paper to.

-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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Autonomous Research AI skill do?

An 18-agent pipeline that turns one topic line into a drafted research paper, literature review, or thesis chapter. Finds real sources at Crossref and OpenAlex, drafts each section against them, attacks its own draft for weak claims, then compiles citations deterministically so in-text markers map one to one onto the bibliography and every printed DOI resolved at Crossref or DataCite. Every agent is a markdown file you can open and edit. Pure stdlib, no API key, no account. Use when someone asks to write a research paper, do a literature review, draft a thesis chapter, find sources on a top...

Why use Autonomous Research on TypingMind?

Because you install it once and use it with any model. Autonomous Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Autonomous Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/federicodeponte/opendraft/tree/master/skills/autonomous-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Autonomous Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Autonomous Research?

As many as you like. As long as a model supports skills, you can use Autonomous Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Autonomous Research AI skill free?

Yes. It is published on GitHub by federicodeponte under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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