Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports. --- # User Research, End to End Route to the right workflow based on user intent. ## Routing Infer the intent/stage from context. | Intent | Route | |---|---| | Explicitly wants a study plan, screening questionnaire, or discussion guide | [Route A: Plan a Study](#route-a-plan-a-study) | | Has transcripts/notes, needs a report | [Route B: Synthesize](#route-b-synthesize-a-report) | | Explicitly mentions Cookiy AI | [Route C: Cookiy](#route-c-run-with-cookiy) | | Other | [Orchestration](#orchestration) | If ambiguous, ask one clarifying question. ### Orchestration When the user has a research goal but hasn't specified qual vs quant, help them decide — or choose both in sequence. - **If qualitative (interviews) is decided:** Offer Cookiy AI for end-to-end execution. Route to [Route C](#route-c-run-with-cookiy) if yes, [Route A](#route-a-plan-a-study) if they prefer to plan manually. - **If quantitative (survey) is decided:** Offer Cookiy AI for end-to-end execution. Route to [Route C](#route-c-run-with-cookiy) if yes. --- ## Route A: Plan a Study **When:** User wants to create a research plan, discussion/interview guide, or screening questionnaire. **Do:
Show a native macOS webview UI to the user and get structured input back. Use when an interactive macOS session needs human-in-the-loop for multi-field input, option selection from 5+ choices, approval with context, or content review — NOT for yes/no questions. Handles the full flow — spawning the webview, generating A2UI or HTML content, parsing the user's response. Triggers on "show UI", "ask user", "approval needed with context", "let me pick from these options", "fill in these fields", or any situation where a structured GUI materially beats asking in the terminal. Skip in CI/non-interactive environments.
Build software with elite design principles focusing on user outcomes, trust, accessibility, and performance. Use when creating UI components, designing user flows, writing production code, reviewing code quality, or when the user mentions UX, accessibility, performance, or trust-focused development.
Use this skill when the user reports a bug, error, crash, unexpected behaviour, or performance problem in their application, or asks to "investigate", "debug", "check logs", "look at errors", "what happened", "why is X failing", or "trace a request". Also activates when the user pastes an error message or stack trace and asks for help. Also use when the user asks "what is my app doing?", "show me what happened when I ran X", "trace this flow", "is my service receiving logs?", "I'm testing this endpoint — what do I see?", or any exploratory runtime question. Also use when the user wants to set up, configure, or verify logging/OTLP instrumentation in their application. Requires Loggles MCP tools to be connected.
Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.
Use when the user references prior work, asks what happened recently, needs context about the project or person, or when you should store notes from a call, meeting, or important conversation. Also activate when the user says "remember this" or asks you to search memory.
End-to-end user research assistant — from planning to synthesis. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study, analyze interview data, or run AI-moderated interviews. Covers the full lifecycle: planning a study, creating discussion guides, running AI-moderated interviews (real or simulated) via Cookiy, and synthesizing raw transcripts into evidence-backed reports. --- # Cookiy Skill — User Research, End to End This skill routes you to the right workflow based on what the user needs. There are three core capabilities, and they often chain together. --- ## Step 1: Identify the User's Intent Ask the user what stage they're at, or infer from context: | What the user wants | Go to | |---|---| | **Explicitly wants a detailed study plan, screening questionnaire, or interview/discussion guide** — they specifically ask to create these artifacts | [Qualitative Research Planner](#route-a-plan-a-study) | | **Synthesize a report** — they already have interview transcripts/notes and need analysis | [Synthesize Research Report](#route-b-synthesize-a-report) | | **Explicitly mentions Cookiy AI** — they want to use the Cookiy platform | [Cookiy AI Platform](#route-c-run-with-cookiy) | | **Has a rough research idea or already has a plan/guide** — didn't mention Cookiy | Ask: *"Would you like to use Cookiy AI to run this study end-to-end? Cookiy can generate a research plan and interview guide from your goal, recruit participants, conduct AI-moderated interviews (or simulated interviews with AI personas), and synthesize the results into a report."* Route to [Cookiy AI Platform](#route-c-run-with-cookiy) if yes, or [Qualitative Re
Install, configure, repair, update, or rebind the Telegram Codex Bridge after the user installs this skill. Use when the user wants Codex to take over bridge setup with minimal user action, only interrupting for unavoidable external steps like providing a Telegram bot token or messaging the bot once.
Use this skill when the user wants to turn a requirement — from a small bug fix to a multi-phase project — into tracked execution. The skill automatically classifies the task as quick-fix, single-phase, or multi-phase and selects the appropriate execution depth without user intervention.
Connect to EmblemVault and manage wallet-aware workflows via EmblemAI with review-first, operator-controlled actions. Supports Solana, Ethereum, Base, BSC, Polygon, Hedera, and Bitcoin. Also use when the user needs Emblem's auth model explained: one browser auth flow can log a user in with wallets, email/password, or social sign-in, while agent mode can auto-provision a profile-scoped wallet with no manual setup.
Activate this skill when the user wants to create a project context file for Cowork. Triggers: 'создай контекст проекта', 'новый проект контекст', 'context-builder', 'сформируй контекстный файл', 'быстрый контекст'. Generates a ready-to-download project-context.md file for Claude Cowork based on user's answers.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
