Skill type: ROUTER / LAUNCHER. Users say "use AlterLab skills" or fire alterflow without
knowing the 230+ skill names. This is the front door: it reads the task, picks the AlterLab skill(s)
that fit, and either uses one skill or launches a clarified, multi-agent workflow.
Core Mission
WHEN THE USER NAMES ALTERLAB BUT NOT A SKILL, YOU PICK THE SKILL(S).
SIMPLE TASK → ROUTE TO ONE SKILL. BIG / KEYWORD TASK → CLARIFY, THEN ORCHESTRATE MANY.
ALWAYS ASK YOUR QUESTIONS BEFORE YOU START A MULTI-STEP RUN.
Two modes
| Mode | Fires on | What happens |
|---|---|---|
| Route (default) | "use AlterLab skills to…", "which AlterLab skill for…", any generic AlterLab ask | classify the task → map to a domain → name and apply the best-fitting skill(s) — Anthropic's routing pattern |
| Orchestrate | a task that spans several dependent stages (research → write → review, etc.); the keyword alterflow — aliases alterresearch / ultralab — is an explicit shortcut into this mode |
clarify → select skills → plan a multi-agent workflow → confirm → execute — Anthropic's orchestrator-workers pattern |
When to Use This Skill
- "Use AlterLab skills to [do X]." / "Which AlterLab skill should I use for [task]?"
- "Is there an AlterLab skill / workflow for [task]?"
- "alterflow — investigate [topic] and draft a paper." (→ Orchestrate mode)
- Any request that references AlterLab by name without specifying a skill.
Does NOT Trigger
| The request is really about… | Route to | Why not this skill |
|---|---|---|
| A specific named skill ("run alterlab-deep-research", "use survey-analysis") | that skill directly | The user already knows it; no routing needed. |
| Composing a bespoke multi-agent workflow the user is hand-designing | alterlab-workflow-orchestration |
This skill calls that engine; it doesn't replace its mechanics. |
| A task with no AlterLab framing at all | (answer normally) | Nothing to route. |
Route mode — the method
- Classify the task — what is the user doing (find literature? clean data? fit a model? write a section? query a database? design a study?).
- Map to a domain (table below).
- Select the skill(s) — name the best fit. For an exact niche name, consult
references/skill_index.md(every AlterLab skill, grouped by domain, one-liner each) or match the installed AlterLab skill descriptions. - Check it is installed. The index lists all AlterLab skills, but the host can only run the ones
installed there. In Claude Code a skill is available when it appears in the host's skill list as
alterlab-<domain>:<skill>; a Skill call that returns Unknown skill means its plugin is not installed. Then stop and use the not-installed reply below — do not retry, and do not search for other tools. - Apply it — invoke the skill and do the work. Tell the user which skill you picked and why (one line).
- If the routing genuinely forks, ask ONE clarifying question before committing.
Not-installed reply
Name the skill and why it fits, then how to get it. Its plugin is alterlab- plus the domain id shown
in backticks in the index heading (for example turkish-academia → alterlab-turkish-academia):
- Claude Code:
/plugin install alterlab-<domain>@alterlab-academic-skills, then/reload-plugins(oralterlab-complete@alterlab-academic-skillsfor every domain). - Claude app (claude.ai): upload
skill-<skill-name>-<version>.zipfrom the latest GitHub release (Customize ▸ Skills → Upload a skill).
If an installed skill covers part of the task, offer it as a stopgap and say what it will not cover; otherwise wait for the user to install the plugin.
Orchestrate mode — alterflow (clarify FIRST, then multi-agent)
Fires when the task spans several dependent stages (e.g. "investigate X and write it up",
"research → analyze → publish") — the alterflow keyword is just an explicit shortcut into this
mode, not the only way in; detect the multi-stage intent even when the user never types it. This is
Anthropic's orchestrator-workers pattern (a lead agent dynamically decomposes the task, delegates
to workers, and synthesizes) with a human-in-the-loop clarify/confirm gate in front. Do not start
executing — run this sequence:
- CLARIFY (mandatory, before any execution). Ask 2–4 sharp scoping questions — e.g. the research question / deliverable, quantitative vs qualitative, the dataset or corpus, the target output (report, paper, model, figures), depth/time budget, and language. Do not guess when the answer changes the plan.
- SELECT. From the answers, choose the AlterLab skills the job needs (use the domain map + the index). List them. First ask whether it should be multi-agent at all: if the work is tightly coupled, needs one shared context, or has little that runs in parallel (Anthropic's caution — most coding-like, dependency-heavy tasks), prefer a sequential pipeline (prompt-chaining) or a single Route-mode skill over a fan-out.
- PLAN. Lay out the dynamic multi-agent workflow — the phases, which skill runs in each, where
subagents fan out in parallel, and the evaluator-optimizer verification/critique step. Scale
the subagent count to complexity and default low (Anthropic's rule of thumb: simple fact-finding
≈ 1 agent; a focused comparison ≈ 2–4; only genuinely broad work ≈ 10+). Reuse the existing
orchestrators rather than reinventing them:
- a packaged job — whole-manuscript citation audit, independent review panel, claim stress-test,
PRISMA dual screening, response to reviewers, grant mock panel, literature map →
alterlab-research-workflows(in Claude Code:/alterlab-workflows:<name>) - end-to-end research→publish →
alterlab-research-pipeline(deep-research → paper-writer → paper-reviewer, revision loops) - a whole social-science study →
alterlab-ssci-orchestrator(design → measurement/reflexivity → sampling → analysis module → inference) - a bespoke fan-out / judge-panel / adversarial-verify workflow →
alterlab-workflow-orchestration
- a packaged job — whole-manuscript citation audit, independent review panel, claim stress-test,
PRISMA dual screening, response to reviewers, grant mock panel, literature map →
- CONFIRM — plan and its rough cost. Show the selected skills + the phases in a few lines, plus an effort estimate (≈ how many phases / subagents). Multi-agent runs spend far more tokens than a single pass, so let the user opt in knowingly; get a go-ahead (or incorporate a correction).
- EXECUTE. The user's go-ahead in step 4 is their opt-in to a multi-agent run. In Claude Code
with dynamic workflows, launch a matching packaged workflow (
/alterlab-workflows:<name>), or write a workflow script for a bespoke plan that fans out beyond a handful of workers; for a few workers, spawn subagents (the Agent tool). On surfaces without these tools, execute the phases sequentially and keep the same hand-offs. Give every spawned subagent a complete task spec — an objective, an output format, which skills/tools/sources to use, and clear boundaries — or workers duplicate work and leave gaps (alterlab-workflow-orchestrationmodels these specs). Carry each stage's artifact to the next (the pipelines define the hand-off contracts).
The one rule: questions before execution. A short clarify step beats a wrong 20-agent run.
Domain routing map (18 domains)
| If the task is about… | Domain | Representative skills |
|---|---|---|
| Find literature, fact-check, discover, manage references | research-tools / core | alterlab-deep-research, alterlab-research-lookup, alterlab-pyzotero, alterlab-citation-verifier |
| Write / draft / revise a paper, abstract, grant, poster; disclose AI use | core / writing-tools | alterlab-paper-writer, alterlab-scientific-writing, alterlab-research-grants, alterlab-ai-use-disclosure |
| Review / critique a manuscript | core | alterlab-paper-reviewer |
| Query a scientific database (PubMed, ChEMBL, UniProt, GEO, …) | databases | alterlab-pubmed, alterlab-chembl, alterlab-uniprot, … (39) |
| Genomics, proteomics, single-cell, structure prediction | bioinformatics | alterlab-scanpy, alterlab-alphafold, alterlab-biopython, … |
| Chemistry, drug discovery, docking, ADMET | cheminformatics | alterlab-rdkit, alterlab-deepchem, … |
| Clinical decision support, trials, medical imaging | clinical-research | alterlab-clinicaltrials, alterlab-clinical-decision, … |
| ML, statistics, data analysis, dataframes | data-science | alterlab-scikit-learn, alterlab-statistical-analysis, alterlab-statsmodels, alterlab-pymc |
| Plots, charts, figures, schematics | visualization | alterlab-matplotlib, alterlab-scientific-viz, alterlab-infographics |
| Lab platforms (Benchling, DNAnexus, Opentrons) | lab-integrations | alterlab-benchling, … |
| Quantum, geospatial, materials, astronomy, digital humanities | domain-specific | alterlab-qiskit, alterlab-geopandas, alterlab-pymatgen, … |
| Convert / handle documents, Markdown, notebooks, PDFs | document-tools | alterlab-markitdown, alterlab-pdf-explore |
| Finance, economics, market/financial data | finance-economics | alterlab-fred, alterlab-edgartools, … |
| Turkish academic system (YÖK, ÜAK, DergiPark, TÜBİTAK, BİDEB, doçentlik, KVKK) | turkish-academia | alterlab-dergipark, alterlab-tubitak-proposal, alterlab-tubitak-bideb, alterlab-docentlik-eligibility, alterlab-kvkk-dmp, … |
| Research ethics, IRB / ethics-committee applications, consent forms | research-tools (Türkiye: turkish-academia) | alterlab-research-ethics, alterlab-tr-research-ethics |
| Teaching, grant reporting, accreditation, recommendation letters, preprints | faculty-life | alterlab-syllabus-ai-policy, alterlab-grant-reporting, alterlab-accreditation-aol, … |
| Research-rigor gates (pre-registration, test choice, transparency) | methodology | alterlab-test-selection-guard, alterlab-preregistration-discipline |
| Design/run a whole social-science study (survey, qualitative, causal, multilevel, meta, missing data) | social-science-workflow | alterlab-ssci-orchestrator (+ 16 gates & modules) |
| Run a packaged multi-agent job — citation audit, review panel, PRISMA screening, rebuttal, grant mock panel, literature map, claim stress-test | workflows | alterlab-research-workflows (/alterlab-workflows:<name>) |
Full per-domain listing of every skill with a one-liner: references/skill_index.md.
Output pattern
Task: <one-line restatement>
Mode: route | orchestrate
AlterLab skill(s): <name(s)> — <why this fits>
[orchestrate] Questions first: <2–4 scoping questions> ← ask, then wait
[orchestrate] Plan: <phase 1 skill → phase 2 skills (fan-out) → verify → deliver>
→ on confirmation, execute.
References
references/skill_index.md— every AlterLab skill, grouped by domain, one-liner each (generated, kept in sync with the catalog).
Part of the AlterLab Academic Skills suite.