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5,040
Artifacts
26
Industries

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5040
Industry
SKILL

Define Problem Statement

Creates a clear problem framing document with user impact, business context, and success criteria. Use when starting a new initiative, realigning a drifted project, or communicating up to leadership.

product-management
SKILL

Define Prioritization Framework

Run applicable prioritization frameworks (RICE, ICE, MoSCoW, Weighted Scoring, Kano) against a list of features or initiatives. Produces a comparison table showing where rankings agree and diverge across frameworks, and an executive summary with recommendation. Framework applicability is filtered by data availability; Kano requires customer research. Refuses to fabricate scores; produces an estimation scaffold when input data is missing.

product-management
SKILL

Define Opportunity Tree

Creates an opportunity solution tree mapping desired outcomes to opportunities and potential solutions. Use for outcome-driven product discovery, prioritization, or communicating product strategy.

product-management
SKILL

Define Jtbd Canvas

Creates a Jobs to be Done canvas capturing the functional, emotional, and social dimensions of a customer job. Use when deeply understanding customer motivations, designing for jobs, or reframing product positioning.

product-management
SKILL

Define Hypothesis

Defines a testable hypothesis with clear success metrics and a validation approach. Use when forming assumptions to test or aligning a team on what success looks like, before any experiment is designed. To design the A/B test or experiment that will validate the hypothesis, use measure-experiment-design.

product-management
AGENT

Cs Syllabus

Course supplementary reading list persona. Walks 3 forcing intake questions (syllabus input format + course audience + year range) before parsing. Halts at grouping checkpoint after Phase 2 (proceed/merge/split/add/remove). Searches Consensus sequentially at 1 q/sec with applied-domain weaving (e.g., 'enzyme kinetics food processing' not just 'enzyme kinetics'). Calibrates summary jargon to audience (undergrad defines every term; grad assumes technical fluency). Writes Bloom higher-order discussion questions tied to learning outcomes. Generates .docx via bundled JS script.

education-k12+2
SKILL

Cs Syllabus

/cs:syllabus <syllabus-file-or-paste> — Generate curated supplementary reading list from any course syllabus. 3-Q grill-me (input format + audience + year range) + grouping checkpoint → Consensus searches per section with applied-domain weaving → .docx via bundled JS script with audience-calibrated summaries + Bloom higher-order discussion questions.

education-k12+2
SKILL

Syllabus

Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.

education-k12+2
SKILL

Cs Research

/cs:research <question> — Default research entry point. Hybrid router: classifies question deterministically and either delegates to specialist (pulse / grants / litreview / dossier / patent / syllabus) OR runs own plan-decompose-search-synthesize fallback. Always surfaces routing decision; accepts override. NEVER silent delegation.

ai-prompt-engineering+2
AGENT

Cs Pulse

Multi-source recency research persona. Walks 2–4 forcing intake questions one at a time (topic specificity, angle, time window, platform scope), runs Reddit + HN + Web in parallel (1 q/sec per platform), optionally pulls X/Twitter, and synthesizes cross-platform patterns into a citation-disciplined briefing. Refuses vague topics. Refuses to bundle intake questions. Refuses to fabricate sources or cite training knowledge as session results.

marketing-growth-copy+2
SKILL

Cs Pulse

/cs:pulse <topic> — Multi-source recency research. Grill-me intake (topic / angle / window / scope), then parallel Reddit + HN + Web (1 q/sec per platform), optional X/Twitter, cross-platform synthesis. Output: ${RESEARCH_DIR}/pulse/<slug>-<date>.md + full briefing in chat.

ai-prompt-engineering+2
SKILL

Pulse

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

ai-prompt-engineering+2
SKILL

Cs Notebooklm

/cs:notebooklm — NotebookLM browser automation. Action-routing intake (Q1: read / add source / Studio output / create new) + per-action Q2-Q4 branching. Fire-and-notify for slow Studio ops. Mandatory custom prompts (defaults are mediocre). Requires browser automation environment — fails clean on web.

ai-prompt-engineering+2
SKILL

Notebooklm

Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.

ai-prompt-engineering+2
AGENT

Cs Litreview

Academic literature orientation persona. Walks 3 forcing intake questions (research question specificity + framework hint + tentative depth) before any Consensus search, then runs reconnaissance + targeted searches per depth tier, then halts at an interactive checkpoint for framework + sub-area + depth confirmation before consuming search budget. Refuses parallel Consensus calls (1 q/sec is non-negotiable). Refuses to cite training knowledge as session results. Refuses to skip the post-Phase-2 checkpoint. Outputs an 8-section .docx research guide as a 'launching pad' for a researcher entering an unfamiliar field.

ai-prompt-engineering+2
SKILL

Cs Litreview

/cs:litreview <research-question> — Academic literature orientation. Grill-me intake (question + framework + depth), Consensus recon, framework checkpoint, sequential budget-allocated searches (5/10/20), 8-section .docx research guide output. Sibling of /cs:pulse (research pack).

education-k12+2
SKILL

Litreview

Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain Consensus search.

academic+2
AGENT

Cs Dossier

Decision-grade entity research persona. Walks 6 forcing intake questions (subject identity + subject type + purpose + hypothesis-MANDATORY + depth + sensitivities). Refuses to produce a dossier without Q4 hypothesis stated. Allocates ≥30% of search budget to disconfirming evidence (refuses confirmation-biased dossiers). Tags every flag with source-reliability tier (primary/secondary/tertiary). Outputs 9-section .docx with verdict on hypothesis (SUPPORTED/PARTIALLY/DISPROVEN/INCONCLUSIVE) + 3-5 finding-tied conversation hooks.

ai-prompt-engineering+2
AGENT

Cs Research Ops Orchestrator

Evidence-first R&D operations lead. Routes enterprise research inquiries (clinical study design / R&D finance / market research / product research) to the right sub-skill via the research-ops-skills orchestrator. Forks context to keep heavy intake (protocol drafts, program ledgers, survey exports, interview transcripts) out of the parent thread. Signature forcing question — "What decision does this research drive, and what's your confidence?"

ai-prompt-engineering+2
SKILL

Cs Research Ops

Top-level Research Operations router. Classifies an enterprise research inquiry (clinical study design / R&D finance / market research / product research) and forks context to the right sub-skill via the research-ops-skills orchestrator, returning a ≤200-word digest with a named owner and one grill challenge.

product-management+2
SKILL

Cs Research Finance

R&D program finance. Build a multi-period program budget with the F&A (indirect) split, track burn rate and runway against value-inflection milestones, and route R&D cost items to a capitalize-vs-expense determination. Every budget surfaces its assumptions; capex-vs-opex routes to a named finance owner and never auto-decides. Direct invocation of the research-finance skill.

finance+2
SKILL

Cs Market Research

Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always show method + assumptions. Direct invocation of the market-research skill.

sales-gtm-revops+2
SKILL

Cs Grill Research Ops

Matt Pocock-style docs-anchored grilling for a Research Operations plan — clinical study, R&D budget, market size, or product study. Walks the plan against the research canon (ICH E9, IAS 38, Cochran, Kotler, Nielsen) one question at a time, recommends an answer per question, and refuses to invoke any sub-skill until the lane-defining decisions are locked. Use before running /cs:research-ops on a fuzzy plan.

operations+2
SKILL

Research Ops Skills

Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).

data-science-ml+2

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