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Found 2,211 Skills
Optimize agent skills to reduce context bloat while preserving answer coverage. Use when: (1) A skill's SKILL.md body exceeds ~250 lines or duplicates its references/ files (2) A skill's YAML description is verbose or triggers false positives from sibling skills (3) Planning or executing a body/reference split for a skill (4) Auditing skill token efficiency
Create and maintain an Obsidian-style graph memory bank in a code repository: small atomic Markdown nodes with YAML frontmatter, cross-links, explicit backlinks, and release/entity-driven coverage for fast AI-agent context retrieval. Use when asked to build/upgrade a 'memory bank', 'graph memory', 'obsidian docs', 'суперсвязанную графовую документацию', or when you need structured docs under docs/ that let an AI agent pull minimal but precise context.
Create comprehensive unit tests, integration tests, and end-to-end tests using pytest for Python projects. Specializes in FastAPI testing with TestClient, async testing with pytest-asyncio, SQLModel/SQLAlchemy database testing, fixture generation, and test configuration setup. Use when you need test coverage, want to implement TDD/BDD, create test suites for functions or API endpoints, add edge case testing, or improve code quality with automated testing. Triggers include requests like "write tests for this module", "create pytest fixtures", "test this FastAPI endpoint", "setup pytest configuration", or "generate test file".
OWASP Mobile Top 10 security testing for Android and iOS — local storage, certificate pinning bypass, IPC abuse, and binary protections.
Production-ready CI/CD configurations for Playwright — GitHub Actions, GitLab CI, CircleCI, Azure DevOps, Jenkins, Docker, parallel sharding, reporting, code coverage, and global setup/teardown.
Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.
Optimize the content of Official Accounts articles in local Markdown files to make them more suitable for Chinese users aged 16-50 to read on the WeChat Official Accounts Platform. It supports optimizing article structure, language expression, and typography, as well as improving opening attractiveness, paragraph rhythm, and end conversion. This skill is applicable when users need to optimize Official Accounts articles, improve Markdown content quality, and enhance article reading experience.
Automated trailing stop loss for leveraged perpetual positions on Hyperliquid. Monitors price via cron, ratchets profit floors through configurable tiers, and auto-closes positions on breach via mcporter — no agent intervention for the critical path. Works for LONG and SHORT. ROE-based (return on margin) tier triggers that automatically account for leverage. Use when protecting an open Hyperliquid perp position, setting up trailing stops, managing profit tiers, or automating position exits on breach.
WOLF v6 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Manages multiple strategies simultaneously, each with independent wallets, budgets, slots, and DSL configs. 7-cron architecture with Emerging Movers scanner (90s, FIRST_JUMP + IMMEDIATE_MOVER), DSL v4 trailing stops (combined runner every 3min, 4-tier at 5/10/15/20% ROE), SM flip detector (5min), watchdog (5min), portfolio updates (15min), opportunity scanner v6 (15min, BTC macro + hourly trend + disqualifiers), and health checks (10min). Same asset can be traded in different strategies simultaneously. Enter early on first jumps, not at confirmed peaks. Minimum 7x leverage required. Requires Senpi MCP connection, python3, mcporter CLI, and OpenClaw cron system.
Opinionated trailing stop loss preset for Hyperliquid perps with tighter defaults than DSL v4. 4 tiers with per-tier breach counts that tighten as profit grows (3→2→2→1), auto-calculated price floors from entry and leverage, stagnation take-profit that closes if ROE ≥8% but high-water stalls for 1 hour. Same ROE-based engine as DSL v4 — different defaults, fewer knobs. Use when you want aggressive profit protection with minimal configuration.
HOWL v2 — Hunt, Optimize, Win, Learn. Nightly self-improvement loop for the WOLF autonomous trading strategy. Runs once per day (via cron) to review all trades from the last 24 hours, compute win rates, analyze signal quality correlation, evaluate DSL tier performance, identify missed opportunities, and produce concrete improvement suggestions for the wolf-strategy skill. v2 adds fee drag ratio (FDR) analysis, holding period bucketing, LONG vs SHORT regime detection, rotation cost tracking, cumulative drift detection, and gross vs net profit factor separation. Use when setting up daily trade review automation, analyzing trading performance, or improving an autonomous trading strategy through data-driven feedback loops. Requires Senpi MCP connection, mcporter CLI, and OpenClaw cron system.
Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and self-assessments without modifying their static .md configuration.