alterlab-skill-finder

name	alterlab-skill-finder
description	The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Task
compatibility	No API key or network required. A routing + orchestration front-end: it reads the bundled skill index and hands off to the matching AlterLab skill(s). Multi-agent execution uses the host's subagent/Workflow tools where available (Claude Code, Cowork); on surfaces without them it decomposes the work into sequential phases.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"dispatches to any AlterLab skill; composes multi-step runs via alterlab-research-pipeline, alterlab-workflow-orchestration, and alterlab-ssci-orchestrator"}

AlterLab Skill Finder — Name the Task, Get the Right Skill (or the Whole Workflow)

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

1. Classify the task — what is the user doing (find literature? clean data? fit a model? write a section? query a database? design a study?).
2. Map to a domain (table below).
3. 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.
4. Apply it — invoke the skill and do the work. Tell the user which skill you picked and why (one line).
5. If the routing genuinely forks, ask ONE clarifying question before committing.

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:

1. 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.
2. 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.
3. 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:
   • 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
4. 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).
5. EXECUTE. Run it as a multi-agent workflow — spawn subagents via the host's Task/Workflow tools where available (Claude Code, Cowork); on surfaces without them, 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-orchestration models 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 (17 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	core / writing-tools	alterlab-paper-writer, alterlab-scientific-writing, alterlab-grant-writer
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-sec-edgar, …
Turkish academic system (YÖK, ÜAK, DergiPark, TÜBİTAK, doçentlik)	turkish-academia	alterlab-dergipark, alterlab-tubitak-proposal, alterlab-docentlik-eligibility, …
Teaching, IRB, grant admin, accreditation, recommendation letters	faculty-life	alterlab-syllabus-ai-policy, alterlab-irb-consent, …
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)

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.