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Found 808 Skills
Guides creation of high-quality SHARE.md files for shareful.ai. Covers repo setup, frontmatter, required sections, and validation. Use when the user wants to create a share, document a coding solution, contribute to shareful.ai, or run npx shareful-ai create.
Blue Ocean Strategy framework based on W. Chan Kim & Renée Mauborgne's book. Use when you need to: (1) create uncontested market space instead of competing head-to-head, (2) analyze competitive landscape (red vs. blue oceans), (3) design value innovation propositions, (4) use Strategy Canvas and Four Actions Framework (ERRC), (5) identify non-customers and convert them, (6) break the value-cost trade-off, (7) shift from competition to value innovation.
Technology adoption and go-to-market strategy based on Geoffrey Moore's "Crossing the Chasm". Use when you need to: (1) identify where your product is in the adoption lifecycle, (2) choose a beachhead market segment, (3) build a "whole product" solution for mainstream buyers, (4) position against incumbent competition, (5) transition from early adopters to mainstream market, (6) develop B2B tech marketing strategy, (7) understand why tech products fail to gain mainstream traction.
Behavior design framework based on BJ Fogg's "Tiny Habits". Use when you need to: (1) diagnose why users aren't completing key actions, (2) reduce friction using the Ability Chain, (3) design effective prompts, (4) create tiny behaviors that compound into retention, (5) audit motivation-ability mismatches, (6) design onboarding that builds lasting habits, (7) apply B=MAP to improve activation and retention metrics.
Systematic root-cause debugging: reproduce, investigate, hypothesize, fix with verification. Use when asked to "debug this", "fix this bug", "why is this failing", "troubleshoot", or mentions errors, stack traces, broken tests, flaky tests, regressions, or unexpected behavior.
Apply when defining or changing the contract of a VTEX IO app through manifest.json, builder declarations, dependencies, peerDependencies, billingOptions, and app identity. Covers how the app declares capabilities and integration boundaries. Use for scaffolding apps, splitting responsibilities across apps, or fixing contract-level link and publish issues.
Technical Document Knowledge Base (LLM Wiki) for Alibaba Cloud Tongyi Qianfan Platform. Activated when users inquire about Qianfan-related issues such as model lists, API parameters, error codes, application development (Agent/RAG/Knowledge Base/Memory/Plugins), model comparison and pricing, SDK/OpenAI compatible interfaces, multimodal capabilities (speech/image/video), Token billing, etc. It includes structured model market data in models (including contextWindow/QPM/pricing/sample code), wiki synthesis layer (topic pages/concept pages/comparison pages), and raw original document layer; for model specification issues, check models/index.md first, and for document-related issues, check wiki/index.md first.
Explain anything — turn ideas into podcasts, explainer videos, or voice narration. Use when the user wants to "make a podcast", "create an explainer video", "read this aloud", "generate an image", or share knowledge in audio/visual form. Supports: topic descriptions, YouTube links, article URLs, plain text, and image prompts.
Configure multi-project workspaces in GrepAI. Use this skill for monorepos and multiple related projects.
Configure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.
Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts/image_gen.py`) and require `OPENAI_API_KEY` for live calls.
Guidance for reverse engineering graphics rendering programs (ray tracers, path tracers) from binary executables. This skill should be used when tasked with recreating a program that generates images through ray/path tracing, particularly when the goal is to achieve pixel-perfect or near-pixel-perfect output matching. Applies to tasks requiring binary analysis, floating-point constant extraction, and systematic algorithm reconstruction.