Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when the user describes a research goal without naming a skill, or the task spans several skills.
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Aperivue/medsci-skills orchestrate --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/orchestrate .claude/skills/orchestrate && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .claude/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrateType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Aperivue/medsci-skills orchestrate --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/orchestrate .agents/skills/orchestrate && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .agents/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Aperivue/medsci-skills orchestrate --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/orchestrate .cursor/skills/orchestrate && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .cursor/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Aperivue/medsci-skills.git --path skills/orchestrate--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Aperivue/medsci-skills orchestrate --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/orchestrate .gemini/skills/orchestrate && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .gemini/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Aperivue/medsci-skills orchestrateInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/orchestrate .github/skills/orchestrate && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .github/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Aperivue/medsci-skills --skill orchestrate -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Aperivue/medsci-skills orchestrate --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/orchestrate .opencode/skills/orchestrate && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "orchestrate" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/orchestrate into .opencode/skills/orchestrate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "orchestrate", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
orchestrateA skill your agent uses when the user describes a research goal without naming a skill, or the task spans several skills.
Orchestrate is an agent skill from Aperivue/medsci-skills. Use when the user describes a research goal without naming a skill, or the task spans several skills. Classifies the request, plans the order and routes to the right medsci-skills skill(s) instead of producing their output itself.
Its SKILL.md is about 8.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/data_flow_contract.md`, `references/dialogue_nodes.md` and `references/report_template.md`).
It sits in Research & Science. The repository describes itself as: Agent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor &… The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 3b14ae2. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
gitghFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Orchestrate loads about 8.1k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 3,794 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from Aperivue/medsci-skills at commit 3b14ae2, republished under its MIT licence (© Aperivue). 3,794 words, ~8,133 tokens.
.claude/skills/orchestrate/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.Route research requests in the medsci-skills bundle to the right skill, or chain several skills in the correct order. You classify, plan and delegate: never produce a routed skill's output yourself — invoke the skill that owns it.
| Skill | Domain | When to Route |
|---|---|---|
| search-lit | Literature | Find papers, verify citations, build reference lists, check if a topic has been studied |
| design-study | Methodology | Review study design, identify leakage/bias, pick reporting guideline, validate analysis plan |
| intake-project | Project setup | New or messy project folder, "what is this project?", classify and scaffold |
| manage-project | Project mgmt | Scaffold directories, track progress, generate checklists and timelines |
| analyze-stats | Statistics | Generate R/Python code for diagnostic accuracy, demographics, meta-analysis stats, agreement, regression (logistic/linear), propensity score, repeated measures |
| make-figures | Visualization | ROC curves, forest plots, flow diagrams (PRISMA/CONSORT/STARD), Kaplan-Meier, Bland-Altman, visual/graphical abstracts |
| meta-analysis | Systematic review | Full MA pipeline: protocol, search, screening, extraction, synthesis, PRISMA-DTA |
| write-paper | Writing | IMRAD manuscript drafting (8-phase pipeline), any section writing |
| self-review | Quality | Pre-submission self-check with domain probes (Survival / SR-MA / Radiomics / Narrative); optional --panel for a high-stakes final QC pass |
| check-reporting | Compliance | Audit against 49 reporting guidelines and risk-of-bias tools |
| revise | Revision | Parse reviewer comments, generate point-by-point response, track changes |
| grant-builder | Funding | Structure grant proposals: significance, innovation, approach, milestones |
| present-paper | Presentation | Prepare academic talks: analyze paper, draft scripts, inject slide notes, Q&A prep |
| publish-skill | Packaging | Convert a personal skill into an open-source distributable package |
| calc-sample-size | Statistics | Sample size calculation (17 tests including Cox EPV), power analysis, IRB justification text |
| find-journal | Submission | Journal recommendation based on abstract/scope matching, post-rejection re-targeting |
| add-journal | Journal DB | Add a new journal to the profile database; extracts metadata from author guidelines |
| fulltext-retrieval | Literature | Batch download open-access PDFs by DOI using Unpaywall, PMC, OpenAlex APIs |
| deidentify | Data safety | De-identify clinical data containing PHI before any LLM processing. Standalone Python CLI (no LLM). |
| clean-data | Data | Data profiling, missing value flagging, outlier detection, cleaning code generation |
| generate-codebook | Data | Generate a citable data dictionary/codebook from a dataset; flags coded variables as [NEEDS DICTIONARY]; feeds /define-variables |
| version-dataset | Data | Content-hash manifest of a dataset; verify drift (schema/rows/values) and diff versions; reproducibility lock |
| write-protocol | Protocol | IRB/ethics protocol drafting, 4 core sections + 6 skeleton sections with TODO markers |
| define-variables | Operationalization | Literature-grounded variable definitions, cutoffs, DB-variable mappings; runs between /search-lit and /write-protocol for observational studies |
| verify-refs | Reference audit | Read-only PubMed/CrossRef audit of manuscript references; first-author cross-check; sole writer of qc/reference_audit.json. Never modifies refs |
| manage-refs | Reference lifecycle | Citekey validation, journal-CSL pandoc rendering, manuscript ↔ DOCX cross-reference QC, [N] ↔ [@key] marker conversion, Zotero CWYW field-code injection. Sole writer of manuscript_final.docx, qc/xref_audit.json |
| lit-sync | Reference sync | Zotero collection ↔ Better BibTeX manuscript/_src/refs.bib ↔ Obsidian literature notes. Sole writer of refs.bib (auto-export); upstream of manage-refs |
| obsidian-paper-vault | Vault build | A folder of PDFs → templated Obsidian literature notes + atomic concept notes synthesized across them. Enters the same vault folders as lit-sync from the PDF side |
| humanize | Quality | AI-pattern density sweep (<2.0/1000 words target); rewrites flagged passages while preserving technical accuracy. Phase 7.5 of write-paper |
| academic-aio | Visibility | AI-search-engine optimization for medical AI papers (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace, RAG tools). Opt-in checklist; never auto-applies edits |
| render-pdf-doc | Document layout | Non-bibliography academic markdown → PDF (proposal, briefing, anchor doc, IRB cover, reference table), CJK-aware. Boundary opposite of manage-refs scripts/render_pandoc.sh |
| fill-protocol | Form filling | Institutional Word form filling (.doc/.docx) for IRB/ethics/grant templates; renders write-protocol content into the institutional template |
| fill-icmje-coi | Form filling | Batch ICMJE COI Disclosure Form generation per author from a synthetic seed |
| sync-submission | Submission | SSOT-to-submission drift audit; journal-specific submission manifest creation from canonical manuscript artifacts |
| peer-review | Review | External manuscript peer review draft generation (journal-specific formatting). Use ONLY for reviewing other authors' work, never for self-review |
| review-paper | Writing | Scaffold/draft a literature review (narrative / scoping PRISMA-ScR / systematic). Distinct from write-paper (original research) and meta-analysis (pooling) |
| polish-language | Quality | Academic-English consistency lint + non-native clarity polish (abbreviations, US/UK spelling, ranges, P/p case, units). Style-only; distinct from humanize (AI-tell removal) and check-reporting (guideline items) |
| author-strategy | Analysis | PubMed author-profile analysis: study-type classification, trajectory-archetype, publication-strategy report from a name |
| batch-cohort | Analysis | Generate N analysis scripts from one validated methodology template × many exposure/outcome combinations (same method, swap variables) + summary matrix |
| replicate-study | Analysis | Replicate an existing cohort study's methodology on a different database: design extraction, variable-harmonization table, replication-difference report |
| cross-national | Analysis | Cross-national comparison study (KNHANES + NHANES + CHNS or parallel surveys): variable harmonization + parallel weighted analysis |
| ma-scout | Systematic review | Meta-analysis topic discovery + feasibility (professor-first profile→gap, or topic-first question→gap→co-author) before a protocol exists |
| find-cohort-gap | Methodology | Research-gap discovery from a longitudinal cohort DB: profile strengths, match PI expertise, literature-saturation scan, ranked topic proposals |
| design-ai-benchmarking | Methodology | Design/validity review for benchmarking AI systems against a human-expert reference panel (rubrics, calibration probes, panel construction, IRR targets) — before data collection |
| model-selection | Modeling | Choose the model for an imaging study: a paper-grounded architecture family (task + modality + data scale + imbalance → shortlist), then vet the concrete repo / checkpoint (licence, version pin, weight provenance; flags an evaluation arm on the benchmark the model was developed or tuned on) |
| imaging-data | Modeling | Profile an imaging dataset before any modelling decision (spacing/orientation, intensity domain, label integrity, foreground fraction, target volume) and gate it against the plan; then plan/audit DICOM/NIfTI intake, resampling, normalisation and augmentation with a preprocessing manifest + data-stage leakage gate before model-scaffold |
| model-scaffold | Modeling | Generate a reproducible runnable PyTorch training repo (patient-level seed-locked split, task model, train/eval scripts, repro record) — between choosing an architecture and validating a trained model |
| radiomics-ml | Modeling | Produce/audit a radiomics / tabular-ML study (imaging or clinical features → penalised logistic / SVM / RF / gradient-boosting / MLP → outcome) with a nested-CV / feature-stability / calibration / external-validation gate (no GPU) |
| model-assessment | Validation | Validate/evaluate a trained imaging model: split-leakage gate + validation design (internal vs external, comparator, sizing); task-correct held-out metrics (Dice + boundary, AUROC + AUPRC with bootstrap CIs, FROC/mAP, calibration, subgroups) → per-case table; uncertainty/OOD/abstention for deployment claims; Grad-CAM/explainability sanity checks + localisation |
| mllm-eval | Evaluation | Design/audit an evaluation harness for an LLM/MLLM clinical task (report generation, VQA, extraction/classification): adjudicated reference, clinical-efficacy metrics beyond BLEU/ROUGE, hallucination, contamination, prompt-sensitivity, reader study |
| model-card | Documentation | Generate a Model Card + Datasheet + data-quality pass for an engineer-built imaging model from user-supplied facts, with a completeness gate |
| contribute | Setup | Offer a local edit (a journal profile, a fix) back as a pull request or issue without typing git; blocks on patient data in the diff and sends nothing until confirmed. Also files a false positive or a failed step |
| setup-medsci | Setup | Read-only runtime diagnostic (Python, R, Node, Claude Code, Git, Zotero, MCP servers): pass/fail table with the setup doc for each missing component |
Classify each request into one of these intents.
Route to the skill whose Available Skills row matches: name it with a one-line reason, then invoke it. The rows below cover modes and flags, look-alike skills, and non-English phrasings:
| User says something like... | Route to |
|---|---|
| "I have a messy folder, help me organize" | /intake-project |
| "Set up a new project" / "Create project scaffold" | /manage-project init |
| "Review my manuscript before submission" | /self-review |
| "Brutally / harshly check before submission" / "top-tier journal final check" / "multi-reviewer / panel review" / "review it from stats, clinical, and imaging angles" / "혹독하게 제출 전 점검" | /self-review --panel --json |
| "Review someone else's manuscript" / "Journal club critique draft" / "외부 논문 리뷰 답변" | /peer-review |
| "Which journal should I submit to?" / "Find a journal" / "I was rejected, where else?" | /find-journal |
| "Sync submission" / "Retarget journal" / "Check SSOT drift" | /sync-submission |
| "Add a journal profile" / "저널 프로필 추가" | /add-journal |
| "Download PDFs" / "Get full texts" / "PDF 다운로드" | /fulltext-retrieval |
| "Visual abstract 만들어줘" / "Graphical abstract" / "GA 생성" | /make-figures |
| "De-identify my data" / "Remove PHI" / "비식별화" / "익명화" / "Anonymize patient data" | /deidentify |
| "Write an IRB protocol" / "Draft ethics submission" / "Research protocol" | /write-protocol |
| "Fill IRB protocol form" / "기관 양식 채워줘" / "심사면제 요청서 채움" / "동의면제 양식" | /fill-protocol |
| "Define my variables" / "Justify cutoff" / "Phenotype definition" / "변수 정의 근거" / "ad-hoc 정의 피하기" | /define-variables |
| "Write a case report" / "I have an interesting case" | /write-paper (case-report mode) |
| "Generate a cover letter" / "Write cover letter for submission" | /write-paper (Phase 8+, requires completed manuscript) |
| "Render manuscript to DOCX" / "Build final .docx" / "Cascade reformat references" / "Apply journal CSL" / "Re-render with Vancouver" / "회람용 docx" / "Zotero CWYW injection" | /manage-refs |
| "Render proposal to PDF" / "Anchor doc PDF" / "한글 학술 PDF" / "Briefing handout PDF" / "IRB cover PDF" / "non-bib markdown → PDF" | /render-pdf-doc |
| "Sync Zotero" / "Refresh refs.bib" / "Better BibTeX export" / "Zotero ↔ Obsidian 동기화" / "literature note 만들어줘" | /lit-sync |
| "PDF를 Obsidian 노트로" / "논문 요약 노트 만들어줘" / "이 폴더의 PDF 정리해줘" / "build a paper vault" / "second brain" / "extract concepts from papers" | /obsidian-paper-vault |
| "ICMJE COI 양식 일괄 생성" / "공저자 COI 폼 만들기" / "Disclosure form batch" | /fill-icmje-coi |
| "AI search optimization" / "Perplexity-friendly abstract" / "RAG visibility" / "GEO checklist" / "Elicit/Consensus 노출" | /academic-aio |
| "Remove AI patterns" / "AI 티 제거" / "humanize this section" / "GPT 흔적 지워줘" | /humanize |
The Nodes column lists the decision forks to render in interactive mode (Dialogue Protocol
below); N1–N11 are defined in ${CLAUDE_SKILL_DIR}/references/dialogue_nodes.md.
| Scenario | Skill chain | Nodes |
|---|---|---|
| New project, no prior work | intake-project -> search-lit -> design-study -> manage-project init | N1, N2 (if user wants manuscript output), N3 |
| Data ready, need a paper | manage-project init -> analyze-stats -> make-figures -> write-paper | N6 (PHI gate), N3, N4 (journal timing), N2 |
| Draft exists, prepare for submission | self-review -> check-reporting -> verify-refs -> humanize -> academic-aio (opt-in --aio) -> manage-refs (DOCX build + xref QC --strict) -> manage-project checklist | N4 (if not yet locked), N8 (only if self-review returns fatal) |
| Submission rendering & cascade reformat | manage-refs (Workflow A pandoc citeproc, or B Zotero CWYW) -> manage-refs scripts/check_xref.py --strict -> verify-refs -> sync-submission build | N10 (Workflow A vs B selection — see manage-refs SKILL.md decision tree) |
| Cascade rejection re-target | find-journal (exclude rejected) -> manage-refs (re-render with new CSL) -> write-paper Phase 8+ (new cover letter) -> sync-submission build --journal {new} | N4 |
| Non-bibliography academic deliverable (proposal/briefing/anchor doc) | write-protocol -> fill-protocol (institutional form available) ⫶ render-pdf-doc (markdown-only, no form) | N11 (form available vs not) |
| Reference housekeeping cycle | lit-sync (Zotero ↔ refs.bib auto-export) -> manage-refs scripts/check_citation_keys.py -> verify-refs --strict (first-author cross-check) | — |
| ICMJE COI batch (multi-author submission) | fill-icmje-coi (per-author docx generation from synthetic seed) -> manual circulation | — |
| Medical-AI paper, AI-search visibility pass | self-review -> humanize -> academic-aio (title, abstract, summary box, README / CITATION.cff / HF card) | N4, N9 (section entry for re-edit scope) |
| Reviewer comments received | revise -> analyze-stats (if new analyses needed) -> make-figures (if new figures needed) | N1 |
| Meta-analysis from scratch | search-lit -> fulltext-retrieval -> meta-analysis (handles its own pipeline internally) | N2 (MA type), N5 (synthesis scope) |
| Grant writing | search-lit -> grant-builder | N2 (option 5) |
| Conference presentation | present-paper (handles its own pipeline internally) | N1 |
| New study, need IRB protocol | search-lit -> design-study -> calc-sample-size -> define-variables -> write-protocol | N3, N2 (option 4 — protocol) |
| Observational cohort study (retro/screening/registry) | intake-project -> design-study -> search-lit -> define-variables -> write-protocol -> analyze-stats -> write-paper | N1, N2, N3 |
| Data with PHI, need full pipeline | deidentify -> clean-data -> analyze-stats -> make-figures -> write-paper | N6 (mandatory), N3, N4 |
| Data ready, need cleaning first | clean-data -> analyze-stats -> make-figures -> write-paper | N6, N3, N4 |
| Full submission chain | write-paper -> self-review -> check-reporting -> find-journal -> write-paper (Phase 8+ cover letter) -> manage-project checklist | N4, N8 (if recovery triggered), N9 (on re-entry) |
| Post-rejection resubmission | find-journal (exclude rejected journal) -> write-paper (Phase 8+ new cover letter) | N4 |
| Case report pipeline | search-lit (similar cases) -> write-paper (case-report mode) -> self-review -> check-reporting (CARE) -> find-journal | N2 (option 2), N4 |
Panel mode (/self-review --panel) is opt-in, never automatic. The chains above use
single-pass self-review. Add --panel only when the user asks for a high-stakes final pass,
because it spawns several reviewer agents plus an editor (several times the tokens); never enable it
by default, including in --e2e. Never combine --panel with --fix: a panel diagnoses and
prioritizes, and the fix loop is a separate pass.
If the intent is genuinely unclear, ask ONE clarifying question — never two in one turn. If you can make a reasonable inference, act on it and confirm; inside a pipeline, render the matching node instead of asking. Examples:
project_state.json or STATUS.md in the working directory first. If found, read it and suggest the next logical step. If not found, ask what they're working on.Without --e2e, a multi-skill chain runs one decision node per fork. Never replace a node with a
plan list and "Shall I proceed?", because that silently commits the paper type, study design, target
journal or recovery branch. Also pause at write-paper's built-in gates (outline approval, discussion
planning).
Load ${CLAUDE_SKILL_DIR}/references/dialogue_nodes.md the first time the pipeline enters a decision fork
in the current session; it holds each node's options, rendering template and autonomous default.
Per-fork execution sequence:
unlocks / locks /
recovery_cost per option, autonomous default announced./self-review surfaces a
Step 7.4a trigger), route to the recovery node (N8) instead of continuing the chain.Control words. back re-enters the previous node. pause halts the pipeline and returns
control to the user. skip is allowed only for a node whose locks scope is empty (rare) —
otherwise explain why skipping is not available.
When the user asks to "run the full pipeline" or similar, run the Standard Pipeline below.
--e2e Flag--e2e entry)Before invoking any downstream skill in --e2e mode, run the following 4 checks. A failure on any
one halts the pipeline. Write all four outcomes verbatim to manuscript/<id>/REPORT.md (see
§"REPORT.md Generation") under Frozen / Version status + Source artifacts checked.
STATUS.md or project_state.json in the
working directory and confirm the current phase. If neither exists, halt with
STATUS_MISSING unless the user passes --no-status.manuscript/<id>/v_*_package/. If the latest v_N
carries a _FROZEN marker file or INDEX.md::frozen=true, this run is
restricted to a v_(N+1)_package/ branch. Any attempt to write directly into
v_N halts with FROZEN_VIOLATION.analysis/_analysis_outputs.md; Phase 7
self-review requires manuscript/manuscript.md. Missing → halt with
REQUIRED_INPUT_MISSING: <path>.k but a prior phase is
incomplete, halt with DEPENDENCY_MISS: [Phase i, Phase j] by default. Only
when the user explicitly passes --auto-extend may the orchestrator prepend
the missing phases and continue.--e2e Pipeline BehaviorWhen --e2e is passed (or the user says "end-to-end", "Arm A", or "fully autonomous"):
--e2e mode ON.--autonomous to /write-paper when invoking it.--json to /self-review and /check-reporting when invoking them.default (from dialogue_nodes.md) and log the choice to qc/_pipeline_log.md as:
[orchestrate] N{id}: defaulted to option {n} ({label}) — {autonomous_rationale}.RECOVERY_HALT_HUMAN_DECISION in the log.--e2e, because AI-search visibility is a
pre-submission concern and running it on every autonomous iteration would waste tokens
and invite silent rewrites that violate the skill's "never edit silently" contract.
Enable it only when the user explicitly adds --aio (or the pipeline is preparing a
preprint / GitHub README / HF card alongside submission). When enabled, schedule it
after /humanize so the checklist anchors on QC-confirmed and human-readable text, and
surface the PASS/PARTIAL/FAIL report to the user — never auto-apply its edits./analyze-stats → analysis/tables/*.csv, analysis/figures/*, analysis/_analysis_outputs.md, analysis/analyze.py/make-figures --study-type {type} → reads analysis/_analysis_outputs.md → analysis/figures/*.pdf, analysis/figures/*.png, analysis/figures/_figure_manifest.md/write-paper --autonomous (if --e2e) → reads analysis/ → manuscript/manuscript.md (DOCX rendering delegated to step 7)/self-review --json --fix → qc/self_review.md/check-reporting → reads manuscript/manuscript.md → qc/reporting_checklist.md + qc/reporting_checklist.json (Part D JSON; called within write-paper Phase 7, but orchestrator verifies output)/verify-refs → reads manuscript/manuscript.md → qc/reference_audit.json (sole output; row-level status in records[])/self-review --json --fix → reads manuscript/manuscript.md → qc/self_review.md + auto-fix (called within write-paper Phase 7.4, but orchestrator verifies final output)/manage-refs (Workflow A pandoc citeproc, or B Zotero CWYW) → reads manuscript/manuscript.md + manuscript/_src/refs.bib → manuscript/manuscript_final.docx + qc/xref_audit.json. Submission gate: check_xref.py --strict must pass (no MISSING_DOCX / MISSING_BODY / MISMATCH).After each skill completes, verify that expected output files exist, then pass the discovered file paths to the next skill as context. If validation fails, report the error and do NOT proceed to the next skill.
| Skill | Expected Outputs | Validation |
|---|---|---|
/analyze-stats | At least one file in analysis/tables/*.csv OR analysis/_analysis_outputs.md | Check file existence and non-empty |
/make-figures | analysis/figures/_figure_manifest.md with at least 1 entry | Parse manifest, verify listed files exist |
/write-paper | manuscript/manuscript.md (required) | Check file existence and non-empty. Do NOT require the DOCX here — manuscript_final.docx is rendered later by /manage-refs (step 7), so requiring it would halt an --e2e run before the DOCX exists |
/check-reporting | qc/reporting_checklist.md and qc/reporting_checklist.json (Part D JSON) | Parse the JSON; halt naming the file if it is absent, unparseable, or lacks an integer missing. If missing > 0 or any action_items[].status == "MISSING", halt with REPORTING_ITEMS_MISSING (list each item) and do NOT proceed to step 7 — route the gaps to /write-paper Phase 7. An inline report that wrote no file does not pass |
/verify-refs | qc/reference_audit.json (sole output) | Parse JSON; halt if submission_safe == false (i.e., FABRICATED / MISMATCH count > 0 OR duplicate_findings[] nonempty) |
/self-review | qc/self_review.md with the Phase 3c JSON block (--json) | Parse the JSON block; halt naming the file if it is absent, unparseable, or lacks an integer fatal_count. If fatal_count > 0 or any issues[].severity == "fatal", do NOT proceed to step 7 — route to N8 (Audit Recovery). A REVISE verdict with no fatal issue is logged (score, verdict, counts) in qc/_pipeline_log.md and REPORT.md, not halted. Accept the optional consensus array and R1/R2/R3 attributions that --panel adds to issues (additive, backwards-compatible) |
/manage-refs | manuscript/manuscript_final.docx, qc/xref_audit.json | DOCX exists and non-empty; xref_audit.json has submission_safe: true (no P0 blocker rows) |
/lit-sync | manuscript/_src/refs.bib (mtime updated), references/zotero_collection.json | refs.bib mtime newer than collection snapshot; refs_bib_refreshed: true in collection JSON |
On validation failure:
--e2e mode: report the error in qc/_pipeline_log.md and STOP. Do not proceed to the next skill. Output: "Pipeline halted at {skill}: {missing output}. Check the skill's output and re-run."At the termination of every --e2e invocation — whether the pipeline completed,
halted at pre-flight, or halted on post-skill validation — the Worker MUST write
manuscript/<id>/REPORT.md using the template at
${CLAUDE_SKILL_DIR}/references/report_template.md.
(none) or (unknown) — never omitted, never collapsed.qc/_pipeline_log.md (Dialogue
node defaults applied, skill invocations, halt reason if any) — not a paste of
the full log.The following actions are permanently forbidden inside --e2e autonomous flow.
On detection, the Worker halts the pipeline and records the attempt under
REPORT.md §"Tier-3 Blocked Items" as tier3_pending: <command>. Hook-confirmed
blocks and prompt-only blocks are listed separately so a future hook regression
cannot silently re-open a prompt-only block.
Hook-confirmed (where a Tier-3 confirm hook is installed):
gws gmail +send / +replyPrompt / skill guard only (no hook coverage — Worker prompt enforces):
git push, gh pr create/sync-submission build external publication pathsgit commit is allowed; a subsequent git push attempt halts. Circulation
emails are saved as a Gmail Draft only — never sent.
Read ${CLAUDE_SKILL_DIR}/references/data_flow_contract.md when chaining a skill and you need what it
reads or writes (the Standard Pipeline above already names its own files).
After the E2E pipeline completes (or when the user requests journal targeting), this workflow
is available. It is NOT part of --e2e: it runs only on explicit user invocation, because it
needs the user's journal selection.
/find-journal → top 5 recommendations based on manuscript/manuscript.md abstract/verify-refs → block fabricated or mismatched references before packagingsubmission/{journal_short}/ directory/sync-submission build --journal {journal_short} → create or refresh the derived manuscript package from the canonical manuscriptsubmission/{journal_short}/:cover_letter.md: via /write-paper Phase 8+checklist.md: journal-specific submission checklistmanuscript_final.docx: re-rendered by /manage-refs for the target journal (if format differs)/self-review with the selected journal as the target → journal scope-aware final pass (not /peer-review, which reviews other authors' work only)Before routing to any data-handling skill (clean-data, analyze-stats, make-figures),
check if the data might contain PHI:
For each CSV/Excel file the task will read that is not itself a *_deidentified.* output (a
de-identified copy of one file says nothing about the others), ask:
"Does the data contain patient identifiers (PHI)? (names, national ID / RRN, date of birth, contact details, etc.)"
/deidentify first, then continue to the originally requested skill
using the *_deidentified.* output fileDe-identification is an INTERACTIVE process requiring the researcher's active participation. Warn: "De-identification requires the researcher's direct review. You must run the script in the terminal and verify each item."
Before routing, check for context clues in the working directory:
| File found | Implies |
|---|---|
project_state.json | Active managed project -- read it to determine current phase |
STATUS.md | Project with status tracking -- read current stage and blockers |
PROJECT.md | Project identity exists -- use for context |
CLAIMS.md | Claims-to-results map exists -- writing is underway |
REVIEW_LOG.md | Revision cycle -- likely needs /revise |
*.qmd or *.tex files | Manuscript drafting in progress |
*.bib files | References exist -- may need verification |
PRISMA_*.md or QUADAS*.md | Meta-analysis or systematic review |
| Decision letter / reviewer PDF | Route to /revise |
| CSV/Excel data files without analysis scripts | Raw data may need cleaning -- suggest /clean-data first |
*_deidentified.* or audit_log.csv | Some data was de-identified -- skip the PHI Safety Gate only for the *_deidentified.* outputs themselves; run it on every other data file the task will read |
protocol_draft.md | Protocol drafting in progress -- may need /write-protocol |
sample_size_*.csv or sample_size_*.R | Sample size calculation done -- check if protocol or manuscript next |
© Aperivue, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 6 other files (references) in skills/orchestrate of Aperivue/medsci-skills.
Open the folder on GitHubat commit 3b14ae2
Orchestrate next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Orchestrate this skillAperivue/medsci-skills | 329 | — | ~8.1k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 83k | 5 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 46k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Read arXiv Paperkarpathy/nanochat | 58k | 2 repos | ~494 | Automated safety check: Pass | MIT | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT |
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
karpathy/nanochat
Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
Aperivue/medsci-skills
A skill your agent uses when validating or evaluating a trained medical-imaging model.
Aperivue/medsci-skills
A skill your agent uses when turning a folder of research PDFs into Obsidian notes, even if Obsidian is not named.
Aperivue/medsci-skills
A skill your agent uses when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar).
Aperivue/medsci-skills
A skill your agent uses when checking whether a manuscript's references are real.
Aperivue/medsci-skills
A skill your agent uses when a clinical CSV/Excel dataset needs profiling and cleaning before analysis (missing values, outliers, duplicates, type mismatches).
Aperivue/medsci-skills
A skill your agent uses when checking a radiology or medical AI study design before drafting or submission.
Categories
A skill your agent uses when the user describes a research goal without naming a skill, or the task spans several skills. Orchestrate is an agent skill from Aperivue/medsci-skills. Use when the user describes a research goal without naming a skill, or the task spans several skills.
Orchestrate fits situations like: the user describes a research goal without naming a skill; the task spans several skills.
Run `npx skills add Aperivue/medsci-skills --skill orchestrate -a claude-code`. Or copy the skill folder (skills/orchestrate in Aperivue/medsci-skills) into .claude/skills/orchestrate in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Aperivue/medsci-skills --skill orchestrate -a codex`. Or copy the skill folder (skills/orchestrate in Aperivue/medsci-skills) into .agents/skills/orchestrate in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Aperivue/medsci-skills --skill orchestrate -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/orchestrate, .gemini/skills/orchestrate, .github/skills/orchestrate and .opencode/skills/orchestrate in your project.
Going by SKILL.md and its folder, Orchestrate needs Python for the scripts in its folder and the command-line tools its instructions call (git and gh). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Orchestrate is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 8.1k tokens (SKILL.md is roughly 33k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Orchestrate: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Aperivue (a GitHub organization) maintains it in Aperivue/medsci-skills, which has 329 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 5, 2026.
Source: Aperivue/medsci-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.