Discover
Discover and install skills, docs, and rules to enhance your AI agent's capabilities.
| Name | Contains | Score |
|---|---|---|
davepoon/buildwithclaude Automate ConvertKit (Kit) tasks via Rube MCP (Composio): manage subscribers, tags, broadcasts, and broadcast stats. Always search tools first for current schemas. | Skills | |
NeverSight/skills_feed Provides Go patterns for type-first development with custom types, interfaces, functional options, and error handling. Must use when reading or writing Go files. | Skills | |
v0.1.0 Agent skills for Liken: near-deduplication and record linkage for Python DataFrames. Contains: liken-backends-performance How to run Liken across DataFrame backends (pandas, Polars, Modin, Dask, Ray, PySpark) and make deduplication scale: install extras, pass a `spark_session` for PySpark, partition with a blocking key, choose the LSH deduper for large data, and use predicate pushdown to cut O(n^2) cost. Use when scaling Liken to large datasets, picking a backend, fixing slow or expensive deduplication, or running on Spark/Dask/Ray. liken-custom-dedupers How to define and register custom dedupers in Liken with the `@lk.custom.register` decorator — backend-agnostic generator functions of the form `(array, *, **kwargs)` that yield pairs of row indices `(i, j)` for records that should link. Use when Liken's built-in dedupers (fuzzy, tfidf, lsh, jaccard, cosine, predicates) cannot express the matching rule you need and bespoke logic is required. Custom dedupers are single-column and usable in the single, dict, and pipeline APIs. liken-dedupers How to choose and apply Liken's built-in dedupers — exact, fuzzy, tfidf, lsh, jaccard, cosine, and the isna/isin/str_contains/str_startswith/str_endswith/str_len predicate dedupers — via `.apply()` with a single deduper or a dict collection, including the pandas accessor affordances. Use when selecting a Liken deduper, setting a threshold, tuning tfidf/lsh, or deduplicating multiple columns with different methods. For rule composition (AND/OR/NOT) use liken-pipelines instead. liken-pipelines How to build Liken pipelines with `lk.pipeline()` and `lk.col()` for complex matching rules: AND semantics (a list of dedupers in one `.step()`), OR semantics (separate `.step()` calls), NOT (`~` on a predicate deduper), preprocessor scoping (pipeline/step/col), and the rule-predication optimization. Use for multi-stage or tiered deduplication, for combining predicate + similarity dedupers, or for applying preprocessing inside Liken. For single dedupers or simple dict collections use liken-dedupers instead. liken-record-linkage How to use Liken for record linkage / entity resolution instead of dropping duplicates: `.canonicalize()` to add a `canonical_id` linking matched records, `.collect()` to get the DataFrame back, `.canonicals()` to count linked groups, and `.synthesize()` to build coalesced "golden" records. Use when the user wants to label/link duplicates rather than remove them, mentions entity resolution, canonical IDs, golden records, or canonicalization. Works with any deduper or pipeline. liken Overview and entry point for Liken, a Python library (`import liken as lk`) for near-deduplication, fuzzy matching, and record linkage over pandas, Polars, PySpark, Dask, Ray and Modin DataFrames. Use when the user wants to deduplicate, fuzzy-match, or link DataFrame records with Liken, mentions `lk.dedupe`, or asks which Liken API to use. Routes to the liken-dedupers, liken-pipelines, liken-record-linkage, liken-custom-dedupers, and liken-backends-performance skills. | Skills | |
dhruvanbhalara/skills Implement HTTP networking with Dio including interceptors, retry logic, and response caching. Use when building API clients, configuring authentication headers, or handling network errors gracefully. | Skills | |
ivan-magda/instruction-health-skills Restructures bloated instruction files in bulk to shrink always-loaded context and restore agent performance. Use when CLAUDE.md, AGENTS.md, Cursor rules, or other instruction files have grown too large (200+ lines, 40k+ chars), when agent performance has degraded due to bloated context, or when the user asks to audit, restructure, slim down, or clean up their instruction files. For a single small edit to an instruction file, use instruction-guardian instead. | Skills | |
v0.1.1 Assumption-first planning and design gap analysis. Use when user is in PLANNING mode, explicitly asks to plan or discuss, asks for a gap analysis / design review / stress-test of a spec or design document, or when agent faces open decisions — including headless tasks where the deliverable is proposals and open questions as files. Instead of interrogating the user, the agent proposes concrete tagged answers in small verify-style rounds (or directly in the output documents when no user is in the loop), surfaces contradictions as questions, and asks only the questions that genuinely fork the design. Contains: fill-the-gaps Assumption-first planning and design gap analysis. Use when user is in PLANNING mode, explicitly asks to plan or discuss, asks for a gap analysis / design review / stress-test of a spec or design document, or when agent faces open decisions — including headless tasks where the deliverable is proposals and open questions as files. Instead of interrogating the user, the agent proposes concrete tagged answers in small verify-style rounds (or directly in the output documents when no user is in the loop), surfaces contradictions as questions, and asks only the questions that genuinely fork the design. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. | Skills | |
MetcalfSolutions/Satori_Claude_Code Satori is a clinically informed wisdom companion for navigating the inner life — emotions, meaning, grief, purpose, relationship, identity, and the questions that don't resolve easily. Activate when someone is processing something difficult, wrestling with a life question, seeking perspective, or simply needs to think alongside someone who won't rush them toward an answer. Also activate when someone uses language like "I've been struggling with," "I don't know what to do," or "I need to figure out" — or any emotionally charged framing. When in doubt, activate. Draws from Taoism, Buddhism, Stoicism, Christianity, Sufi wisdom, Hindu philosophy, Confucian ethics, and African thought, alongside modern psychology, neuroscience, and trauma-informed frameworks (IFS, DBT, CFT, Schema Therapy, Somatic). Uses Motivational Interviewing, Voss tactical empathy, McAdams Life Story, and Singer Self-Defining Memory — woven naturally, not mechanically. | Skills | |
sickn33/antigravity-awesome-skills Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. | Skills | |
github/awesome-copilot Set up complete GitHub Copilot configuration for a new project based on technology stack | Skills | |
davepoon/buildwithclaude Automate Notion tasks via Rube MCP (Composio): pages, databases, blocks, comments, users. Always search tools first for current schemas. | Skills | |
davepoon/buildwithclaude Automate Freshdesk helpdesk operations including tickets, contacts, companies, notes, and replies via Rube MCP (Composio). Always search tools first for current schemas. | Skills | |
trpc-group/trpc-agent-go Edit PDFs with natural-language instructions using the nano-pdf CLI. | Skills | |
jeremylongshore/claude-code-plugins-plus-skills Generate complete REST API implementations from OpenAPI specifications or database schemas. Use when generating RESTful API implementations. Trigger with phrases like "generate REST API", "create RESTful API", or "build REST endpoints". | Skills | |
block/agent-skills Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics. | Skills | |
jeremylongshore/claude-code-plugins-plus-skills Juicebox data privacy and GDPR. Trigger: "juicebox data privacy", "juicebox gdpr". | Skills | |
Derives human-readable manual test cases from stateful behavior: identify states, events, transitions, and guard conditions, draw the state table including invalid (empty-cell) transitions, choose a coverage level (all states, valid transitions / 0-switch, transition pairs / 1-switch per Chow, all transitions including invalid ones), then derive one test case per coverage item as an event sequence with per-step expected states (ISTQB CTFL v4.0 section 4.2.4). A deep single-technique walkthrough rather than a broad multi-lens case matrix; the output is manual step/expected cases rather than parameterized test code, and it covers how cases are derived rather than how a case record is structured. Use for lifecycle entities (accounts, orders, subscriptions), workflows, and UI wizards where the response to an event depends on the current state. Contains: state-transition-test-design Derives human-readable manual test cases from stateful behavior: identify states, events, transitions, and guard conditions, draw the state table including invalid (empty-cell) transitions, choose a coverage level (all states, valid transitions / 0-switch, transition pairs / 1-switch per Chow, all transitions including invalid ones), then derive one test case per coverage item as an event sequence with per-step expected states (ISTQB CTFL v4.0 section 4.2.4). A deep single-technique walkthrough rather than a broad multi-lens case matrix; the output is manual step/expected cases rather than parameterized test code, and it covers how cases are derived rather than how a case record is structured. Use for lifecycle entities (accounts, orders, subscriptions), workflows, and UI wizards where the response to an event depends on the current state. | Skills | |
benchflow-ai/skillsbench Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort. | Skills |
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