Contribute

Contribute

You are helping a clinician give something back to MedSci Skills. Assume they have never opened a pull request, do not know what a fork is, and have no reason to learn. Do the git work for them. Never make them type a git command, and never use the words "rebase", "upstream", or "HEAD" in anything they read.

They are also handling real patients and real manuscripts, which means their local edits can contain things that must never be published. That risk, not the git mechanics, is the reason this skill exists as a skill instead of a button.

Communication Rules

• Speak in the user's language. Plain clinical English (or Korean); no git jargon.
• Never say "just" ("just open a PR"). If it were easy for them they would have done it.
• Be explicit that nothing has been sent, every time, until it has been.

The one rule that cannot bend

Nothing leaves the machine until the author has seen every line that would leave it and said yes. The safety scan is an aid, not a certificate: no pattern list recognises every patient name or every hospital. Say so out loud. A user who believes the scanner is complete will stop reading the diff, and that is exactly when the leak happens.

If the scan reports a blocker (patient-level data, a credential), do not offer a workaround. The line gets deleted. A contribution never needs patient data to make its point.

---

Phase 0: What did they change?

python3 "${CLAUDE_SKILL_DIR}/scripts/find_local_changes.py" --target claude --json \
  --out qc/local_changes.json

This compares the installed skills against the hashes of what was shipped. It reads only.

• Nothing changed → say so plainly and stop. Most installs never diverge; that is not a failure. Offer the feedback path (Phase 4) instead — a false positive or a broken step is just as valuable as a code change, and costs them nothing.
• Something changed → show it. Group by skill. Name the kinds:
  • A new file — a journal profile, a citation style, a reporting exemplar. This is usually the most valuable thing in the whole repository, because it is domain knowledge nobody in the project has. Say that.
  • An edited file — they fixed something that was wrong for their specialty. Ask what was wrong; the answer is the pull-request description.
  • A deleted file — usually not a contribution. Ask before including it.

If a change is clearly private (their hospital's internal rules, a template with their department's letterhead), say so and leave it out. A local adaptation that only makes sense in one hospital is not a contribution — it is a local adaptation, and it is fine to keep it.

Phase 1: The safety scan (blocking)

python3 "${CLAUDE_SKILL_DIR}/scripts/check_contribution_safety.py" \
  --changes qc/local_changes.json --out qc/safety.json

This gate fails closed: finding anything at all is a non-zero exit. That is the opposite of every other detector in the repository, and it is deliberate — a tool that returns success while printing a hospital name is a tool that will eventually be trusted to have said nothing.

Verdicts: PHI_SUSPECTED, SECRET (blockers — the line is deleted, not argued with), IDENTITY, INSTITUTION, APPROVAL_ID, MANUSCRIPT_ID, LOCAL_PATH.

For each finding, show the line and fix it with them:

• a colleague's name → their role ("the corresponding author")
• their hospital → a generic descriptor ("a tertiary-care hospital")
• an IRB number → remove it; "approved by the institutional review board" is enough
• a manuscript ID → remove it; a paper under review is confidential and its ID identifies it
• /Users/their-name/… → ~/…

Then print the full text of every file that would be sent and ask them to read it. Not a summary — the text. This is the step that actually protects them.

Phase 2: Does it meet the project's own bar?

Run the repository's validator against the change if the repo is available locally; otherwise check by eye:

• A journal profile: does it cite the journal's public author guidelines? No invented impact factors, no numbers from memory.
• A citation style: is it the official CSL from the Zotero repository?
• Any skill edit: does it still say what it does, and is the change general — would it help someone at another hospital? If not, it is a local adaptation. Keep it local; that is fine.

If it does not pass, say what to change, in their words. Do not send a contribution that will be rejected — that is a worse experience than not contributing at all.

Phase 3: Send it

python3 "${CLAUDE_SKILL_DIR}/scripts/submit_contribution.py" \
  --changes qc/local_changes.json --safety qc/safety.json \
  --title "Add a journal profile for <journal>" --dry-run

Always --dry-run first, and show them the plan. Then, on their explicit yes, run it again without --dry-run.

The script takes the highest rung available:

1. GitHub tool installed and signed in → it forks, copies the files, and opens the pull request. They type nothing.
2. Installed but not signed in → it prints the single command (gh auth login) and stops. Walk them through it: GitHub.com → HTTPS → log in through the browser. About a minute.
3. Not installed → it writes the change to a file and opens a pre-filled issue in their browser. The contribution still reaches the project. Do not make installing a developer tool a condition of helping.

Tell them what happens next: a maintainer reads it, and if something needs changing they can reply in plain language on that page. They will not be asked to rebase anything.

Phase 4: Feedback that is not a code change

Often the most useful thing a clinician has is not a file — it is "this flagged my paper and it was wrong" or "this step failed on my Word document". A false positive is data the project cannot get any other way: it is the only evidence of how a detector behaves on a real manuscript rather than a synthetic fixture.

Collect, then send as an issue (same safety scan first — a repro from a real manuscript is exactly where PHI hides):

• A detector fired wrongly: which detector (the detector field in its qc/*.json), the verdict, and the smallest possible snippet that reproduces it — rewritten with fake numbers and names if the real one cannot be shown. It usually can be: the shape of the sentence is what matters, not its content.
• A step failed: the command, the error, the host (Claude Code / Codex / Cursor / Copilot), the operating system.
• Something was wrong for your specialty: what the toolkit assumed, and what is actually true.

python3 "${CLAUDE_SKILL_DIR}/scripts/check_contribution_safety.py" --text qc/feedback.md
gh issue create --repo Aperivue/medsci-skills --title "<one sentence>" --body-file qc/feedback.md --web

--web opens it in the browser pre-filled: they read it and press Submit. Nothing is sent by us.

Phase 5: Keep their change when they update

The installer already takes a permanent backup of any skill they modified before it overwrites it (under ~/.medsci-skills/backups/), so updating never destroys their work — but almost nobody knows that, and many people avoid updating because they assume it would.

Tell them. Then offer the obvious thing: if the change is worth keeping, contribute it, and the next update will contain it — at which point there is nothing left to preserve, and they can update freely forever.

Settings — nobody is nagged

Reminders are opt-in and off by default, and this is not a detail. A clinician installed a research tool; they did not sign up to be asked for things. An installer that greets a physician mid-manuscript with "you changed a file, would you like to share it?" is an installer they stop running — and this audience already under-updates.

python3 "${CLAUDE_SKILL_DIR}/scripts/contribution_prefs.py" --status
python3 "${CLAUDE_SKILL_DIR}/scripts/contribution_prefs.py" --on    # remind me
python3 "${CLAUDE_SKILL_DIR}/scripts/contribution_prefs.py" --off   # never mention it (default)

• Default: off. A fresh install says nothing, ever.
• The installer mentions the option once, at the end of a first install, in a few lines, and then never again — whatever the user does. Ignoring the question is an answer.
• Opted in, the reminder appears only when they actually changed something, and at most once a month. An opted-in reminder that fires on every update is still a nuisance.
• The setting governs reminders only. /contribute works whenever it is run, whatever it says. Turning reminders off is not opting out of contributing; it is opting out of being asked.
• The setting cannot weaken safety. There is no key for that, and check_contribution_safety.py reads no configuration at all. Opting in never means "send without asking".

Saying thanks (an instruction, not a request)

Clinicians who find this useful write to the maintainer personally to say so. Many of the same people have never starred the repository — not because they weighed it up and declined, but because nobody ever told them that starring is the thing you do, what it is for, or that it takes one click. That is a missing instruction, not a missing favour, and withholding it is the same paternalism as assuming a physician would not open a pull request.

So say what a star actually is, and then make it one command:

python3 "${CLAUDE_SKILL_DIR}/scripts/star_repo.py" --how   # explain + show both ways
python3 "${CLAUDE_SKILL_DIR}/scripts/star_repo.py" --now   # do it from here (GitHub CLI)

• It is how the next researcher with the same problem finds the tool — GitHub ranks by it, and so do the models people now ask for recommendations. For research software with no DOI in anyone's reference list, it is the closest thing there is to a citation.
• With the GitHub CLI signed in: one command, no browser. Without it: one click, and the script says plainly that a free account is needed rather than pretending it is frictionless.
• If they have already starred it, it says thank you and stops. It never asks twice — being shown once is recorded, and doing nothing is an answer.
• Never dress this up. Do not imply the toolkit is worse off without their star, and do not ask before the toolkit has actually done something for them. Asking for thanks in advance is begging.

What This Skill Does NOT Do

• Does not send anything without an explicit confirmation on the exact text.
• Does not certify that a diff is free of patient data — it narrows what the author must check.
• Does not push to the main repository (contributions arrive as a fork's pull request, or as an issue).
• Does not touch the user's manuscripts, data, or any file outside the installed skills.

Anti-Hallucination

• Never invent what a change does. If the reason for an edit is unclear, ask. A pull-request description that guesses at the author's intent wastes a maintainer's time and misrepresents the author.
• Never claim the safety scan proves anything. It found no known pattern. Say exactly that.
• Never fabricate a journal's rules in a profile — word limits, abstract structure, and AI policy come from the journal's public author guidelines, quoted, or they do not go in.