Loading...
Loading...
Found 770 Skills
Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.
User Guide for Feishu IM Message Reading Tool, covering session message retrieval, thread reply reading, cross-session message search, and image/file resource download. **Use this Skill when:** (1) Need to retrieve historical messages of group chats or one-on-one chats (2) Need to read reply messages in threads (3) Need to search messages across sessions (by keywords, senders, time, etc.) (4) Messages contain images, files, audio, video that need to be downloaded (5) Users mention "chat history", "messages", "what was said in the group", "thread replies", "search messages", "images", "file download" (6) Need to filter messages by time range or get more messages by pagination
Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
Translates Mermaid sequenceDiagrams describing cryptographic protocols into ProVerif formal verification models (.pv files). Use when generating a ProVerif model, formally verifying a protocol, converting a Mermaid diagram to ProVerif, verifying protocol security properties (secrecy, authentication, forward secrecy), checking for replay attacks, or producing a .pv file from a sequence diagram.
Pendo platform help — product analytics, in-app guides, session replay, NPS/CSAT surveys, feature adoption tracking, Leo AI. Use when Pendo guides aren't showing, feature tagging is tedious, analytics data looks wrong, users aren't completing onboarding, NPS scores are flat, need help with Pendo API or aggregation queries, setting up Pendo for the first time, or comparing Pendo to Appcues or WalkMe. Do NOT use for in-app messaging strategy across platforms (use /sales-in-app-messaging) or general customer feedback strategy (use /sales-customer-feedback).
Luban - Skill Polishing Workshop. Transform a "usable Skill" into a public Skill asset that is "understandable, installable, shareable, verifiable, and continuously evolvable". The methodology consists of five craftsman-like steps: 1. Material Inspection: First challenge whether the premise of this Skill is valid; directly state if the "material" is not worth polishing. 2. Peer Research: Search for similar Skills online to clarify its position in the ecosystem. 3. Dimension Measurement: Evaluate using three metrics - structure, actual testing, and live verification (live verification means reconciling with real running outputs; a green CI can be deceptive). 4. Iterative Refinement: Freeze the original version as a baseline; only retain changes that pass the verification gate, otherwise revert. Try to institutionalize verification methods as tools and rules in the repository. 5. Post-Release Iteration: Release is not the end; maintain a benchmark observation list, and start the next iteration based on real feedback. This tool is used when users want to upgrade, optimize, polish, productize, or release their self-developed Skills. The final deliverables include a structured Skill Polishing Report, directly replaceable rewritten segments, and a shareable "Graduation Certificate" result card that can be screenshot. Trigger phrases include but are not limited to: "Let Luban take a look at this skill", "Polish at Luban's Workshop", "Polish my skill", "Upgrade my skill", "Optimize this skill", "Skill check-up", "Skill audit", "Productize my skill", "How to release this skill", "Benchmark against similar skills", "Why no one installs my skill", "Help me publish my skill to GitHub/ClawHub", "Improve SKILL.md". Even if users only provide a Skill directory, GitHub repository link, or a segment of SKILL.md saying "Help me figure out how to modify it", it should be triggered as long as the context is about making the Skill more usable and shareable. Do NOT use this for creating a new Skill from scratch (use skill-creator), regular code review (use code-review), or rewriting ordinary prompts unrelated to Skill assets.
Implement secure webhook handling with signature verification, replay protection, and idempotency. Use when receiving webhooks from third-party services like Stripe, GitHub, Twilio, or building your own webhook system.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions. Covers Gen1/Gen2 operators, subengines (Python, Node.js, C++), structured data operators, and repository objects.
Face swap and deepfake generation using ModelsLab's Deepfake API. Swap faces in images and videos with high-quality AI-powered face replacement technology.
Motivation science framework based on Daniel Pink's "Drive". Use when you need to: (1) design features that leverage intrinsic motivation, (2) create progress systems that support mastery, (3) craft purpose-driven messaging and missions, (4) audit if product mechanics undermine autonomy, (5) design team structures and incentives with AMP principles (Autonomy, Mastery, Purpose), (6) understand why gamification fails, (7) replace carrot-and-stick approaches with intrinsic motivation.
Migrates JSON Schemas between draft versions for use with z-schema. Use when the user wants to upgrade schemas from draft-04 to draft-2020-12, convert between draft formats, update deprecated keywords, replace id with $id, convert definitions to $defs, migrate items to prefixItems, replace dependencies with dependentRequired or dependentSchemas, adopt unevaluatedProperties or unevaluatedItems, or adapt schemas to newer JSON Schema features.