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Found 919 Skills
Authors and updates customization overrides for installed BMad skills. Use when the user says 'customize bmad', 'override a skill', 'change agent behavior', or 'customize a workflow'.
Decision-grade entity research skill — produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. Forcing intake makes the user state their hypothesis upfront (what they already believe and want to verify or disprove) so the dossier tests it rather than confirms it. Output is an editable Word document (.docx) with verdict on the hypothesis, identity facts, 12-month activity timeline, network signals, reputation signals, red flags, 3-5 conversation hooks tied to specific findings, and source-provenance audit log. Uses WebSearch + WebFetch + free APIs (SEC EDGAR, GitHub, ProPublica Nonprofit Explorer) as workhorses; optional BYOK MCPs (LinkedIn, Crunchbase, Apollo, Pitchbook, SimilarWeb) enhance coverage. Triggers: 'research [company]', 'dossier on [person/company]', 'background check on [entity]', 'prep me for a meeting with [person/company]', 'due diligence on [company]', 'what should I know about [entity]', 'research [person] before I [meet/hire/invest]', 'competitor research on [company]', 'investor diligence [company]', 'interview prep for [company]'. Honors sensitivity exclusions for journalism + personal-vetting contexts.
Use this skill when the user explicitly asks to create, write, improve, or optimize a prompt for use with an AI. Trigger on phrases like "write me a prompt", "improve this prompt", "create a system prompt", "how do I ask ChatGPT/Claude to...", or "quero um prompt para...". Do NOT trigger for direct task requests where the user wants the output, not the prompt.
Router skill for LLMQuant investor-lens workflows. Use when the user wants an investor-style reasoning overlay grounded in LLMQuant Data evidence.
Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.
News Briefing + Verification Workflow. This workflow applies when users request news, headlines, daily briefings, "today's news", "latest updates", breaking news, or specific current figures/events that demand reliable sources and timestamps. Source links and local publication time are mandatory; NEVER fabricate any content.
Append conversation context to cumulative project history - never overwrites
Process images using object detection, classification, and segmentation. Use when requesting "analyze image", "object detection", "image classification", or "computer vision". Trigger with relevant phrases based on skill purpose.
Route Alibaba Cloud Model Studio requests to the right local skill (Qwen Image, Qwen Image Edit, Wan Video, Wan R2V, Qwen TTS and advanced TTS variants). Use when the user asks for Model Studio without specifying a capability.
A skill for performing web searches, research, and reasoning using the Perplexity API. Handles real-time web information retrieval, deep research analysis, and advanced reasoning tasks. Use when the user asks for web searches, research, or says things like "look up", "search for", "latest information", "research", or "analyze".
AI Native Camp Day 4 Wrap & Analyze + 콘텐츠 소화. session-wrap 스킬을 직접 만들고, history-insight와 session-analyzer로 세션을 분석하고, 콘텐츠 소화 파이프라인을 체험한다. "4일차", "Day 4", "wrap", "세션 분석", "session wrap", "세션 래핑", "fetch", "콘텐츠" 요청에 사용.
Iteratively improve any output until measurable criteria are met. Use when the user wants to refine existing work against specific standards — whether it's code, prose, data, config, or any other artifact. Triggers on phrases like "improve this", "make it better", "iterate", "refine", "keep improving", "not good enough yet", "optimize this", "polish this", "tighten this up", or when the user provides criteria and wants repeated improvement until they're satisfied. Also use when the user gives feedback on output and expects you to keep refining, even if they don't say "improve" explicitly.