Search Results: lora
Found 455 Skills
data-science-interactive-apps
Interactive web apps for data science: Streamlit, Panel, and Gradio. Use for prototyping ML models, creating data exploration dashboards, and sharing insights with non-technical stakeholders.
deep-research
Conduct exhaustive, citation-rich research on any topic using all available tools: web search, browser automation, documentation APIs, and codebase exploration. Use when asked to "research X", "find out about Y", "investigate Z", "deep dive into...", "what's the current state of...", "compare options for...", "fact-check this...", or any request requiring comprehensive, accurate information from multiple sources. Prioritizes accuracy over speed, cross-references claims across sources, identifies conflicts, and provides full citations. Outputs structured findings with confidence levels and source quality assessments.
gathering-feature
The drum sounds. Bloodhound, Elephant, Turtle, Beaver, Raccoon, Deer, Fox, and Owl gather for complete feature development. Use when building a full feature from exploration to documentation — secure by design.
qa-test
Manual QA testing — verify features end-to-end as a user would, using every tool available (browser, macOS, bash, APIs). Focuses on what formal test suites cannot capture: visual correctness, UX flows, usability judgment, integration reality, edge cases, and failure modes. Standalone or composable with /ship. Triggers: qa, qa test, manual test, test the feature, verify it works, exploratory testing, smoke test, end-to-end verification.
tinkering
Safe experimentation framework for AI agents. Creates isolated sandbox environments for trying new features, testing approaches, and exploring solutions without polluting the main codebase. USE WHEN: Agent needs to try something uncertain, explore multiple approaches, test a new library, prototype a feature, or run a technical spike before committing to implementation. PRIMARY TRIGGERS: "experiment with" = Setup sandbox + run experiment "try this approach" = Quick experiment in sandbox "spike" / "POC" / "prototype" = Time-boxed technical investigation "tinker" / "tinkering mode" = Enter experimentation workflow "explore options" = Multi-approach comparison in sandbox NOT FOR: Debugging (use debugger), testing (use test runner), or committed feature work (use git branches). DIFFERENTIATOR: Unlike git branches (for committed direction), tinkering is for "I don't know if this will work" exploration. Try 5 things in sandbox before committing to a branch. Faster feedback, zero codebase pollution.
create-skill
Add a new skill to the LaunchDarkly agent-skills repo. Use when creating a new SKILL.md, adding a skill to the catalog, or aligning with repo conventions. Guides exploration of existing skills before creating.
data-science-eda
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
copy-lab
Orchestrate copy exploration. Brief, generate 5 distinct approaches, adversarial review, iterate to 90+ composite, present catalog, user selects, execute.
agentic-infra
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
qa-run
8-agent QA loop: browser exploration via Playwright MCP, then analyze, plan, test, audit, heal, expand, snapshot. Quality gate score >= 85 to pass.
cangjie-docs-navigator
Intelligent Retrieval Assistant for Cangjie Language Documentation. Supports 4 search modes (Direct Search, PageIndex Intelligent Retrieval, Hybrid Mode, Exploratory Learning). It is used when users need to: (1) Query Cangjie syntax (variable declaration, function definition, generics, etc.), (2) Look up standard library APIs (String, Array, HashMap, etc.), (3) Learn about Cangjie features or get started with the language, (4) Conduct any documentation queries related to Cangjie/cangjie/cj. It uses four MCP tools: cangjie_docs_overview, cangjie_list_docs, cangjie_search, and cangjie_get_doc for intelligent retrieval.
brainstorming
Facilitates collaborative design exploration before implementation. Explores user intent, constraints, and requirements through structured dialogue, then produces an approved design document. Must be invoked when entering plan mode or planning any implementation. Triggers: brainstorm, design, plan, "let's think through," "how should we build."