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Found 13,608 Skills
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of extending context beyond the window via filesystem strategies.
Post-implementation quality check via fresh-eyes review. Chain: Implement → Review (independent agent) → Resolve (if issues). Max 2 rounds. Auto-triggers for security-sensitive and data-mutation code. Not for code refactoring (use code-cleanup). Not for decision analysis (use agent-room). For post-deploy verification, see deploy-verify. For shipping and PRs, see ship.
Single entry point for all AEM 6.5 LTS Replication skills. Covers configuring replication agents, activating/deactivating content, using the Replication API programmatically, and troubleshooting distribution issues for Adobe Experience Manager 6.5 LTS.
Dollar Cost Averaging (DCA) for Stacks DeFi — automate recurring buys or sells of any Bitflow token pair via direct swaps. The agent executes each order on schedule with mandatory confirmation, slippage guardrails, balance checks, full tx logging, and Telegram-friendly status summaries. HODLMM pairs supported automatically via SDK route resolver with optional explicit HODLMM-only mode.
Command-line interface for Ollama - Local LLM inference and model management via Ollama REST API. Designed for AI agents and power users who need to manage models, generate text, chat, and create embeddings without a GUI.
Design patterns for the Langroid multi-agent LLM framework. Covers agent configuration, tools, task control, and integrations.
For CLI agents WITHOUT subagent support (e.g., Codex CLI). Search previous code agent sessions for specific work, decisions, or code patterns.
Comprehensive map for multi-brain, orchestration, and agent governance. Triggers when users ask to 'view the orchestration ecosystem', 'how do agents work together?', 'multi-brain workflows', or 'give agents access'.
Philip Tetlock's Superforecasting framework applied to a business decision, investment thesis, or strategic question. Spawns a team of specialist agents — Calibrator, Decomposer, Updater, Devil's Advocate, Scorekeeper — who each apply a different piece of the superforecasting methodology. The lead synthesizes into a calibrated probability estimate with Brier-scoreable predictions, explicit base rates, and an accountability structure for keeping score over time. Use when the user says "tetlock this", "what's the probability", "how confident should I be", "forecast this", "calibrate this", proposes a business thesis and wants probabilistic stress-testing, or wants to apply superforecasting to a decision. Works standalone or after /munger.
Daniel Kahneman's Cognitive Diagnostic applied to a decision, strategy, or business evaluation. Spawns a team of specialist agents — System Detector, Substitution Mapper, Prospect Theorist, Noise Auditor, Outside Viewer — who each apply a different lens from Kahneman's cognitive architecture to audit the decision for bias, noise, and cognitive traps. The lead synthesizes into a contamination assessment: which cognitive systems are operating, which substitutions are active, and whether the decision should proceed, be corrected, or be restructured. Use when the user says "kahneman this", "check my thinking", "am I biased", "audit this decision", "what am I missing", or presents any decision, strategy, or evaluation they want cognitively stress-tested. Works standalone or as a companion to /munger (Munger evaluates the business; Kahneman audits the thinking about the business).
Audit token waste across agent systems (Claude Code, OpenClaw, Hermes, OpenCode). Detect idle burns, model misrouting, and config bloat with dollar savings.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).