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Found 571 Skills
Apply Upper Echelons Theory (Hambrick and Mason, 1984) to analyze how top management team characteristics — demographics, experiences, values — shape strategic choices and organizational outcomes. Use this skill when the user needs to evaluate TMT composition effects on strategy, predict strategic direction from leadership profiles, assess whether managerial discretion enables or constrains executive influence, or when they ask 'does leadership background matter for strategy', 'how does TMT composition affect decisions', or 'why did this management team make that choice'.
Apply first principles thinking to break problems down to fundamental truths and reason up from there. Use this skill when the user is stuck in conventional thinking, needs to challenge assumptions, find breakthrough solutions, or evaluate whether something is truly impossible vs just assumed to be — even if they say 'everyone does it this way', 'is there a fundamentally better approach', 'why does it have to cost this much', or 'challenge my assumptions'.
Educational map of risk exposure screening—typical risk indicator taxonomies, exposure value and percentage, address-level vs transaction-level engines, and common template families (entity label, multi-hop interaction, blacklist). Use when the user asks how commercial screening tools reason about labeled addresses, tainted flows, or deposit vs withdrawal checks—not for legal sanctions determinations or substituting a vendor’s live rules.
Richard Feynman's Integrity Audit applied to any analysis, business plan, or decision. Spawns a team of specialist agents — Source Auditor, Self-Deception Hunter, Translation Tester, Cargo Cult Inspector, Confidence Inverter — who each apply a distinct lens from Feynman's framework to detect dishonesty, self-deception, and cargo cult reasoning. The lead synthesizes into a verdict: is this analysis honest, or is it fooling itself? Use when the user says "feynman this", "integrity audit", "is this honest", "am I fooling myself", "cargo cult check", or wants to stress-test any analysis, plan, or claim before trusting it. Works standalone or as a meta-audit after /munger or /thiel.
Builds robust, tool-specific prompts from user intent using a structured extraction and routing engine. Use when the user asks for prompt creation, prompt repair, prompt decomposition, or adapting prompts across Claude, GPT, reasoning models, Gemini, coding IDEs, autonomous agents, and image tools.
Professional-grade contract review skill that adds comment-based issue annotations without changing original text. Enforces a four-layer review (entity verification, basic, business, legal), writes structured comments (issue type, risk reason, revision suggestion) with risk level encoded via reviewer name, and generates a contract summary, consolidated opinion, and Mermaid business flowchart (with rendered image). Output language must follow the contract’s language.
Provides image recognition capabilities for non-multimodal models (such as pure text models like deepseek-v4-pro, GLM-5.1, mimo-v2.5-pro, etc.). This skill is automatically triggered when the main model cannot recognize images, when users send screenshots/design drafts/UI screenshots for analysis, or when users say 'Look at this image', 'Analyze this screenshot', 'What's wrong with this image'. It also applies to any scenario where users paste images but the current model does not support image input. Supports simultaneous recognition of multiple images, with primary-backup fallback achieved by configuring multiple image recognition models. It can also be manually triggered using the commands /skill:vision-support or /vision. Iron Rule: The models configured for this skill are only used for image content recognition and will never participate in main logical reasoning. Note: If the current model is itself a multimodal model (such as Claude Sonnet 4, GPT-4o, Gemini, etc. that can directly recognize images), do not use this skill; let the main model recognize directly.
Router skill for LLMQuant investor-lens workflows. Use when the user wants an investor-style reasoning overlay grounded in LLMQuant Data evidence.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Meta-skill for improving and optimizing prompts using Anthropic's prompt engineering best practices. Provides the 4-step improvement workflow (example identification, initial draft, chain of thought refinement, example enhancement), keyword registries for documentation lookup, and decision trees for improvement strategies. Use when improving prompts, optimizing for accuracy, adding chain of thought reasoning, structuring with XML tags, enhancing examples, or iterating on prompt quality. Delegates to docs-management skill for official prompt engineering documentation.
Generate platform-specific social post variants (Twitter/X, LinkedIn, Reddit) from one source input. Works with or without Node.js script. Includes platform reasoning, quality review, and guardrails against cross-posting spam.
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".