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Found 1,462 Skills
Choose and combine Eve storage primitives to give agents persistent memory — short-term workspace, medium-term attachments and threads, long-term org docs and filesystem. Use when designing how agents remember, retrieve, and share knowledge.
Use when auditing or adapting newly created, downloaded, forked, installed, or community Agent Skills to the user's tools, habits, directories, session history, and preferred workflows, especially when triggers feel wrong, noisy, or too generic.
End-of-session adversarial review loop. Assemble the session's work into a role-assigned, self-contained brief, then run independent reviewers in parallel — an isolated code-reader (the idea-validator agent) that reads the ACTUAL files and web-checks technology currency, plus an external-family model if you have one — synthesize where they agree vs diverge, apply the cheap-safe fixes immediately, record a measurable plan for the rest, and CHALLENGE reviewer claims you disagree with (never blind-accept). Use at the close of a substantive coding or design session, when the user says "session review", "review my session", "stress-test this session", or types /session-review. Skip for trivial one-off edits.
Give this repo's CLAUDE.md / AGENTS.md a checkup — size vitals vs official guidance, dead references, dead commands, stale claims — then backtest every rule against the repo's own Claude Code session history to see which rules were actually followed, ignored, or never used, and produce a doctor-style HTML report with evidence-cited prescriptions. Use when asked to check, diagnose, audit, review, improve, optimize, lint, grade, fix, clean up, shorten, or "doctor" CLAUDE.md, AGENTS.md, or agent instruction/memory files, or to find out whether CLAUDE.md rules actually work.
Use when an implementation task has just been completed and the user wants independent verification that the work is actually correct. Dispatches two subagents that perform the SAME verification checks independently, then merges their findings into one deduplicated report with severity and confidence. Triggers when the user says "verify this", "double-check the implementation", "cross-check what was done", "did this actually work", "verify the task", or invokes a slash command for verification. Especially valuable right before commits, PRs, or hand-offs, where over-claiming is most costly.
Run a 5-dimensional quality assessment (Clarity/Conciseness/Actionability/Consistency/Minimal-slop) on any LLM prompt / document (markdown, YAML, JSON, TOML) and iteratively refine it until the target score is reached. The default target score is 90, which can be overridden using the target_score parameter (e.g., target_score=95). This is used when users want to validate and improve the quality of LLM-facing prompts, instructions, or skills.
Giới thiệu Spec Kit là gì, các thành phần chính, và nên chạy skill nào khi nào (setup-context → c4-model → plan-backlog → backlog-status). Dùng skill này khi user hỏi "spec kit này là gì", "kit này dùng sao", "bắt đầu thế nào", "có những skill nào", hoặc ngay sau khi cài skill lần đầu và chưa biết bắt đầu từ đâu.
Reviewing one Task's diff on two axes in parallel sub-agents — Spec (does the code do what the spec and the ticket asked?) and Standards (does it follow the repo's conventions, the test rules and the smell baseline?) — and reporting them side by side. The reviewer step of /archie-implement.
Test a half-formed idea in a questioning conversation, with different personas probing its weak points, until the user can act on it or drop it with confidence. Optionally writes a short brief for planning skills to build on. Use when the user says 'forge an idea', 'pressure-test this idea', 'stress-test my thinking', or 'harden this idea'
Heartbeat-driven 7-day BotLearn tutorial reminders — fetches quickstart pages daily, tracks progress, presents tips in the user's language, auto-stops after Day 7.
AI-powered deep stock analysis engine for A-share/HK/US markets with 51 investor personas, 22 data dimensions, 180 quantitative rules, and 17 institutional methods
Agentic and machine-to-machine payments on Stellar. Covers x402 (HTTP 402 paid APIs via OZ Channels facilitator, fee-sponsored clients) and MPP (Machine Payments Protocol) in both Charge mode (per-request Soroban SAC) and Channel mode (off-chain commits, high-frequency). Defaults to USDC (SEP-41 SAC) on `stellar:testnet`/`stellar:pubnet` (CAIP-2). Use when selling a paid API to AI agents, building an x402 client, or designing a payment-channel architecture for high-frequency agent traffic.