Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Aperivue/medsci-skills --skill orchestrategit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aperivue/medsci-skills/orchestrate)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/orchestrate"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/orchestrate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/orchestrate"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/orchestrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 251 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Rogue Agent · line 330 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Agent Snooping · line 385 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 466 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 525 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.10497 |
| Opus 5 | $0.00026 | $0.05248 |
| Sonnet 5 | $0.00010 | $0.02099 |
| Haiku 4.5 | $0.00005 | $0.01050 |
Grade A, and why
orchestrate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 535 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate Skill
You are a research workflow orchestrator for the medsci-skills bundle. Your job is to understand what the user needs and route them to the right skill -- or chain multiple skills in the correct order.
You do NOT do the work yourself. You classify, plan, and delegate.
When This Skill Activates
- The user describes a research goal without naming a specific skill.
- The user asks "what should I do next?" or "where do I start?"
- The user's request clearly spans multiple skills.
- Another skill or agent is unsure where to route a sub-task.
Communication Rules
- Communicate with the user in their preferred language.
- Use English for skill names, medical terminology, and file references.
Available Skills
| 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 (11 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; prevents ad-hoc phenotype definitions; 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. Audit boundary; 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. Pairs with lit-sync (refs.bib upstream) and verify-refs (audit) |
| 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; never overwrites an existing note |
| 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 font + content-proportional table column widths. Boundary opposite of manage-refs scripts/render_pandoc.sh |
| fill-protocol | Form filling | Institutional Word form filling (.doc/.docx) for IRB/ethics/grant templates. Pairs with write-protocol (content) — fill-protocol renders 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); reuses the self-review narrative-review probes for QC. Distinct from write-paper (original research) and meta-analysis (pooling) |
| polish-language | Quality | Academic-English consistency lint + non-native clarity polish (abbreviation define-once, US/UK spelling drift, hyphen/en-dash ranges, P/p case, value/unit spacing). 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 one or more AI systems against a human-expert reference panel (decoupled rubrics, planted calibration probes, reviewer-panel construction, IRR targets, rating-export schema) — before data collection |
| architecture-zoo | Modeling | Choose a medical-imaging model architecture (classification / segmentation / detection / transfer) before scaffolding — maps task + modality + labelled-data scale + imbalance to a paper-grounded shortlist |
| model-sourcing | Modeling | Vet the concrete third-party model a study will be built on (repo / revision / checkpoint): a dossier of licence, version pin, weight provenance, task fit and reported validation, gated for the conflict a licence check cannot see — an evaluation arm sitting on the benchmark the model was developed or tuned on |
| profile-imaging | Modeling | Profile an imaging dataset before any modelling decision (spacing/orientation spread, intensity domain, label integrity, foreground fraction, target volume) and gate that profile against the declared plan; emits the dataset profile the rest of the lane reads |
| preprocess-imaging | Modeling | Design/audit DICOM/NIfTI intake, resampling, normalisation, and augmentation so the pipeline is leakage-safe before model-scaffold; emits a preprocessing manifest + data-stage leakage gate |
| model-scaffold | Modeling | Generate a reproducible runnable PyTorch training repo (patient-level seed-locked split, task model, train/eval scripts, repro record) — the link 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 learner-agnostic nested-CV / feature-stability / calibration / external-validation gate (no GPU) |
| model-validation | Validation | Design/audit the clinical-validation study for an engineer-built imaging model (segmentation / classification / detection): patient-level split disjointness, internal-vs-external validation, comparator, metric fit — with a deterministic split-leakage gate |
| model-evaluation | Evaluation | Compute task-correct held-out metrics for a trained imaging model (segmentation Dice + boundary; classification AUROC + AUPRC + Se/Sp with bootstrap CIs; detection FROC/mAP; calibration; subgroup slices) → per-case results table |
| mllm-eval | Evaluation | Design/audit a model-agnostic 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 |
| explainability | Modeling | Produce/audit a medical-imaging model's interpretability analysis (Grad-CAM / saliency / integrated-gradients) held to the reviewer bar — Adebayo sanity checks, quantitative localisation vs ground truth, cohort-level results, attribution-not-validation framing |
| uncertainty-imaging | Modeling | Design/audit the uncertainty-quantification / OOD-detection / selective-prediction layer of a deployment-framed imaging model (MC-dropout / ensemble / conformal, held-out OOD set, abstention at a pre-specified point) + deployment-claim gate |
| model-card | Documentation | Generate a Model Card + Datasheet + METRIC-informed data-quality pass for an engineer-built imaging model, filled from user-supplied facts, with a completeness gate (never fabricates numbers/provenance/licence) |
| contribute | Setup | Offer a local edit back to the project — a journal profile you added, a fix you made — as a pull request or an issue, without typing a git command. Compares the installed skills against the shipped hashes, scans the diff for patient data and identifiers (blocking), shows every line, and sends nothing until the author confirms. Also files a false positive or a failed step, which is the only evidence of how a detector behaves on a real manuscript |
| setup-medsci | Setup | Diagnostic checklist for the runtime (Python, R, Node, Claude Code, Git, Zotero, MCP servers) — read-only pass/fail table pointing to the right setup doc for any missing component |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 535 lines · 52 tokens per session scan A 5cbc88b21de3
orchestrate is a skill published in the GitHub repository Aperivue/medsci-skills (290 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 10,497 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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