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Run fable-mode execution discipline on Claude Haiku. Routes the task to the @fable-worker-haiku agent, whose definition carries the staged loop with tightened verification (no bare "unverified" allowed) and an escalate-don't-improvise rule. Trigger when the user explicitly asks for thorough/systematic handling run cheaply or fast ("fable on haiku", "deep work mode but cheap", "stage this on haiku"). For bulk mechanical work. Do NOT use for tasks needing synthesis — benchmark note: at n=1 the skill's effect on Haiku swung both directions (+25 / −17); route quality-critical work to fable-sonnet instead.

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Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extraction task", or is about to pull structured fields from legal docs without a fixed model.

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Pick the right LLM for LEGAL RESEARCH & ANALYSIS — issue-spotting, rule application, case/statute analysis, memos, and multi-step agentic research. Vendor-neutral routing grounded in mid-2026 benchmarks (Vals AI LegalBench across 124 models; Harvey Legal Agent Benchmark for agentic work). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model for legal research / case analysis / a memo", "best AI for legal reasoning", "route this research task", or is starting legal analysis without a fixed model.

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Pick the right LLM for CONTRACT DRAFTING — generating, redlining, or rewriting contract language from instructions. Vendor-neutral routing grounded in mid-2026 legal benchmarks (legalbenchmarks.ai Contract Drafting). Asks up to 4 quick questions (cost, speed, accuracy/ stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to draft this clause/agreement", "best AI for drafting contracts", "route this drafting task", or is about to generate/redline contract text and hasn't fixed a model.

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Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.

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Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening systems.

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NIS2 Compliance Navigator — scope classification, Art. 21 gap analysis (0-4 maturity scoring), and compliance roadmap under EU Directive 2022/2555 with deep German BSIG-neu coverage and profiles for Italy, France, Netherlands, Austria, Spain. Use when: (1) User mentions "NIS2", "NIS-2", "BSIG", "BSIG-neu", "NIS2UmsuCG", "Cyberbeveiligingswet", "Loi Résilience", "NISG", "decreto legislativo 138", (2) User asks if their organization falls under NIS2 or needs a cybersecurity compliance assessment, (3) User mentions essential/important entities, Annex I/II, BSI registration, § 30 BSIG, incident reporting, management body liability, supply chain security, (4) User wants a NIS2 gap analysis, readiness assessment, or compliance roadmap, (5) User asks about NIS2 fines, enforcement, or Nachweispflicht, (6) User asks about NIS2 in any EU Member State.

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Mandatory external verification workflow for ALL non-trivial factual claims before presenting them as true. This skill MUST be used whenever Claude is asked to research any topic, answer factual questions, provide current information, draft documents containing factual claims, give legal advice or cite legal authority, discuss current events or public figures, provide technical or scientific information, state statistics or data points, or answer ANY question where the answer could have changed over time. Also trigger when the user asks Claude to 'check', 'verify', 'confirm', 'research', 'look up', or 'find out' anything, or when the user needs information that requires freshness, precision, or source accuracy. This skill overrides any default tendency to answer from memory. If a task involves stating facts, citing sources, or providing current information — use this skill.

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Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.

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Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.

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Design discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject content.

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Use *before* starting work in a domain you don't know well, to surface the "unknown unknowns" — the things you don't even know to ask about — and learn just enough to prompt and decide well. Implements the "blind spot pass" pattern from Anthropic's Fable "finding your unknowns" field guide. Triggers when you say "I'm new to this", "I don't know what to ask", "teach me before we start", "blind spot pass", "unknown unknowns", "find my unknowns", or the Korean "내가 뭘 모르는지 알려줘" / "이 분야 처음인데" / "먼저 가르쳐줘" / "블라인드 스팟", or when you hand off a task while admitting you're a non-expert in that field (color grading, video editing, legal, tax, finance, design, an unfamiliar codebase, etc.). Do NOT use for simple factual questions, domains you already know, or directly-actionable work — answer those directly or route to planner / architect.

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Turn a one-line description of a repetitive task into a reusable, self-guarding slash command. loop-forge diagnoses the task into one of 5 loop shapes (Batch / Pipeline / Refine / Watch / Explore), interviews for the blanks, and auto-injects two safety devices the user didn't know they needed — an independent verifier (maker ≠ checker) and a hardstop (a budget/count/cooldown ceiling) — then previews the result and stamps it as a `/command` they can run forever. Use when the user says "/loop", "/loop-forge", "/make-it-loop", "automate this", "make this repeatable", "turn this into a command", "do this for all 100 items", "do X every time Y happens", "generate several and pick the best", or otherwise wants to capture a recurring task as a reusable slash command instead of re-typing the prompt by hand. Works in any language: it interviews the user and writes the stamped command in the user's own language. Non-goal — it does not schedule unattended runs (launchd/cron) or publish externally on its own; it stamps the reusable command and stops there.

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Expert patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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Expert patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do

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Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

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Full brand naming workflow for founders, agencies, and businesses. Use this skill whenever the user says "help me name this brand", "brand naming", "I need a name for", "name ideas for", "what should I call my brand/company/product", "naming a startup", "brand name suggestions", "help with naming", or shares a brand brief and asks for name options. Also triggers when the user shares existing name options and asks for feedback, evaluation, ranking, or scoring of those names. Auto-detects whether to run the full generation workflow or the evaluation workflow based on what the user provides. Always use this skill for any brand or product naming task — even if the user just casually mentions needing a name.

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Set up (or toggle) a macOS desktop banner that appears whenever Claude Code finishes a task, showing a one-line summary of what was done. Use when the user says "notify me when Claude is done", "desktop notification on completion", "set up the task banner", "mute the banner", or "unmute the banner".

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Write how-to guides and tutorials that naturally integrate affiliate product recommendations. Triggers on: "write a how-to guide", "tutorial for [task]", "step by step guide to [goal]", "how to [verb] with [product]", "write a tutorial blog post", "guide on how to [task]", "beginner guide to [topic]", "walkthrough for [product]", "write an educational article", "how do I [task] blog post", "write a tutorial that promotes [product]".

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Audit and improve the prompt surface of Avibe Agents across backends (Claude, Codex/GPT, OpenCode) — global and project rules, Agent system prompts, Skills, delegation briefs, and Task and Watch messages — using real run evidence. Use when an Agent misbehaves (stalls, over-asks, over-reaches, ignores or over-applies a rule), after a model or backend change, or when the user asks to review, clean up, or tighten prompts.

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Phase 0 of the prd-taskmaster pipeline. Resolves the active backend, initializes the project, configures the provider stack when the TaskMaster backend is active (DETECT-FIRST — never overwrite a working user config), and verifies the AI pipeline. Autonomous: zero user questions unless a hard block is hit. Declares the Setup phase complete so DISCOVER can follow.

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Execute multi-task implementation plans by dispatching a fresh implementer subagent per task with independent review gates. Use when implementing plans, executing multi-task PRs, or transitioning from planning to implementation.

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Make 100 versions of Claude fight to the death over one task. Spins up N sub-agents (default 100, --quick for 16), gives every one the exact same task plus a different strategy card (reasoning mode, workflow, strategy), then runs a single-elimination bracket: they attack each other's solutions, defend and revise, and a judge scores every match on a written rubric until one solution survives. Use when the user is not satisfied with an answer, calls it a bad answer, says try again or do better, says "arena", or asks to make them compete.

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