Fetch GitHub repository contents using uithub CLI when users paste GitHub URLs. Use when users share github.com links or need to analyze repository code, issues, or pull requests.
This skill should be used when the user asks to run a command on the host machine, open an application on the host, send a desktop notification to the user, list previously approved host commands, or manage long-running background processes (daemons) on the host. Provides the host-tools binary at /home/ai-pod/.local/bin/host-tools.
Interactive visual canvas for structured communication between agent and user. Opens a rich annotatable document in the user's browser where the user reviews, comments, answers questions, and submits feedback. Supports planning, architecture reviews, code reviews, discovery interviews, implementation summaries, proposals, decision documents, and explanations.
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".
Create low-friction coursework deliverables for general education, elective, and low-stakes college assignments, including PPT slides, short papers, reading reports, reflection essays, presentation scripts, discussion posts, and course summaries. Use this skill whenever the user mentions 水课, 通识课, 选修课, 小论文, 课程论文, 读书报告, 观后感, 汇报PPT, 课堂展示, 演讲稿, or asks to turn scattered course materials into a polished student-style deliverable. --- # Coursework Helper Skill Produce practical course deliverables from messy prompts, readings, course slides, topic requirements, and user notes. The goal is to help a busy student quickly get a usable PPT, short paper, speech script, or reflection while keeping claims grounded in provided material. ## Architecture ```text User request -> Intake: infer task type, deadline pressure, source materials, tone, and format -> Setup: index materials, check official file skills, initialize output directory -> Plan: choose deliverable path and create a lightweight outline -> Draft: produce content, slides/script, or paper -> Polish: add citations/evidence notes, fix tone, export requested formats ``` | Path | Use when | Primary output | |------|----------|----------------| | `slides` | PPT/class presentation/课堂展示/汇报 | `final_slides.md`, optional `.pptx`, speech script | | `paper` | 小论文/课程论文/读书报告/观后感/心得体会 | `final_paper.md`, optional `.docx`/`.pdf` | | `script` | 演讲稿/发言稿/答辩稿/课堂分享 | `final_script.md` | | `mixed` | User asks for PPT + paper + script, or task is ambiguous | combined deliverables | ## Startup Layer ### 1. Low-Friction Intake Infer as much as possible from the user's words and files. Ask only when the missing answer would change the deliverable.
Spawn conversations with other LLMs (Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Qwen, Grok, Mistral, etc.) and fold results back into your context. TRIGGER when: user asks to talk to, chat with, use, call, or spawn another LLM or model; user mentions Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Claude (as a sidecar target), Qwen, Grok, Mistral, or any non-current model by name; user asks to get a second opinion from another model; user wants parallel exploration with a different model; user says "sidecar", "fork", or "fold".
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.
Generate Russian academic reports (.docx) formatted to GOST 7.32 — лабораторные работы, отчёты по практике, курсовые проекты, ВКР, домашние задания для любого российского вуза (ИТМО, МГУ, СПбГУ, МФТИ, Бауманка, и т.д.). Use this skill whenever the user asks for a report по ГОСТ, лабораторную работу, отчёт по практике, курсовой проект, ВКР, или любой Russian-language student paper that needs proper title page, headings, page numbers, figure/table captions. Trigger this skill even if the user only mentions "лабораторная" or "отчёт" without naming a specific university — Russian-language context (references to ИТМО / МГУ / СПбГУ / университет / ГОСТ) is enough. ITMO is the default profile (preserves the original itmo-report behavior); other universities are supported via UniversityProfile.
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
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:
