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alterlab-skill-finder

스타46

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업데이트2026년 7월 2일 17:04

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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

AlterLab-IEU

AlterLab-IEU/AlterLab-Academic-Skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

관련 직업SOC

SOC 직업 분류 기준

기타 컴퓨터 관련 직업컴퓨터 및 수학직·SOC 15-1299

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3 개 파일

SKILL.md

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이 저장소의 다른 Skills

같은 저장소

alterlab-meta-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-missing-data

AlterLab-IEU/AlterLab-Academic-Skills

Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-multilevel-models

AlterLab-IEU/AlterLab-Academic-Skills

Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-qualitative-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-ssci-orchestrator

AlterLab-IEU/AlterLab-Academic-Skills

Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that each gate reads and appends, and enforcing gate order with PASS / WARN / BLOCK semantics (design-gate and inference-gate are fail-closed). It is thin: it routes and holds the artifact, it does not run analysis itself. Use when a researcher describes a whole social-science study end to end, asks to run the methods pipeline or a full methodology review, or wants the design-to-inference workflow coordinated rather than a single step. For a single stage trigger that gate directly (alterlab-ssci-design-gate, -measurement-gate, -sampling-gate, -inference-gate); for multi-agent execution mechanics see alterlab-workflow-orchestration. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-ssci-reflexivity-gate

AlterLab-IEU/AlterLab-Academic-Skills

Gates trustworthiness before qualitative or interpretivist claims are written — the qualitative analog of the measurement gate. Checks that researcher positionality and role are stated, that the four Lincoln & Guba trustworthiness criteria are addressed (credibility via triangulation / member-checking / prolonged engagement, transferability via thick description, dependability via an audit trail, confirmability via reflexive bracketing), and that reflexivity is evidenced rather than asserted. Fail-closed: interpretivist generalizing or causal language is refused without the corresponding warrant. Use when a study is qualitative / ethnographic / interpretivist, when reviewing trustworthiness or reflexivity, or before writing up thematic or grounded-theory findings. For quantitative measurement validity prefer alterlab-ssci-measurement-gate; for running the coding prefer alterlab-qualitative-analysis; for the final claim audit prefer alterlab-ssci-inference-gate. Part of the AlterLab Academic Skills suite.

2026-07-0246

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

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.
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.

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:
- 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
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. 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.

alterlab-skill-finder

이 저장소의 다른 Skills

이 저장소의 다른 Skills

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

Core Mission

Two modes

When to Use This Skill

Does NOT Trigger

Route mode — the method

Orchestrate mode — alterflow (clarify FIRST, then multi-agent)

Domain routing map (17 domains)

Output pattern

References

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

Core Mission

Two modes

When to Use This Skill

Does NOT Trigger

Route mode — the method

Orchestrate mode — alterflow (clarify FIRST, then multi-agent)

Domain routing map (17 domains)

Output pattern

References

Orchestrate mode — `alterflow` (clarify FIRST, then multi-agent)

Orchestrate mode — `alterflow` (clarify FIRST, then multi-agent)