Discover and install skills to enhance your AI agent's capabilities.
| Name | Contains | Score |
|---|---|---|
trpc-group/trpc-agent-go Best practices for using the oracle CLI (prompt + file bundling, engines, sessions, and file attachment patterns). | Skills | |
santosomar/ethical-hacking-agent-skills Performs authorized fuzzing of web applications and APIs to discover input validation failures, parser bugs, and stability issues. Use when testing HTTP endpoints, request parameters, payload handling, and error behavior under malformed or unexpected inputs. | Skills | |
bap-jorkim/agent-skills-fork-feb-25 Engineering principles for building software like a senior engineer. Load when tackling non-trivial development work, architecting systems, reviewing code, or orchestrating multi-agent builds. Covers planning, execution, quality gates, and LLM-specific patterns. | Skills | |
mukul975/Anthropic-Cybersecurity-Skills Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment. | Skills | |
alirezarezvani/claude-skills Deprecated redirect skill that routes legacy 'content creator' requests to the correct specialist. Use when a user invokes 'content creator', asks to write a blog post, article, guide, or brand voice analysis (routes to content-production), or asks to plan content, build a topic cluster, or create a content calendar (routes to content-strategy). Does not handle requests directly — identifies user intent and redirects to content-production for writing/SEO/brand-voice tasks or content-strategy for planning tasks. | Skills | |
NeverSight/learn-skills.dev Technical debt detection and remediation. Run at session end to find duplicated code, dead imports, security issues, and complexity hotspots. Triggers: 'find tech debt', 'scan for issues', 'check code quality', 'wrap up session', 'ready to commit', 'before merge', 'code review prep'. Always uses parallel subagents for fast analysis. | Skills | |
NeverSight/learn-skills.dev Yellow Network and Nitrolite (ERC-7824) development best practices for building state channel applications. Use when building apps with Yellow SDK, implementing state channels, connecting to ClearNodes, managing off-chain transactions, or working with application sessions. | Skills | |
dtsola/xiaoyaosearch Expert code review of current git changes with a senior engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements. | 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 | |
github/awesome-copilot Guides the Copilot CLI on how to use the WorkIQ CLI/MCP server to query Microsoft 365 Copilot data (emails, meetings, docs, Teams, people) for live context, summaries, and recommendations. | Skills | |
smartcontractkit/chainlink-agent-skills Help developers build with Chainlink Data Streams, including credentials guidance, report decoding, REST and WebSocket report retrieval with official Go/Rust/TypeScript SDKs, High Availability streaming, on-chain report verification, real-time frontend displays, report schema guidance, SQLite persistence, and timestamp lookback. Use this skill whenever the user mentions Chainlink Data Streams, Streams Direct, Data Streams reports, report schemas, report decoding, data-streams-sdk, or real-time low-latency market data from Chainlink. | Skills | |
launchdarkly/ai-tooling Scripted onboarding for LaunchDarkly: quiet execution, fixed sequence, SDK install, first flag with a live reveal, MCP offered afterwards. Enforces step completion before advancing and redirects drift. Use when adding LaunchDarkly, setting up or integrating feature flags in a project, SDK integration, or 'onboard me'. | Skills | |
team2027/2027-skills Audit and rewrite error messages, exceptions, and API/SDK/CLI failure output so an AI agent (and a human) can self-correct without leaving the terminal. Use when designing or reviewing how a tool fails — error strings, HTTP error bodies, auth/credential errors, CLI errors, thrown exceptions — especially for libraries, APIs, and CLIs that AI agents consume. Grounded in patterns from 2027.dev's evals of 136 dev tools across 3,500+ agent runs. | 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 | |
udecode/plate React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements. | Skills | |
administrakt0r/AI-Agents-Safe-Coding-Skills Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. | Skills | |
sickn33/agentic-awesome-skills Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok. | Skills | |
SnowingFox/ai-skills Turn the current conversation context into a PRD and publish it to the project issue tracker. Use when user wants to create a PRD from the current context. | Skills | |
g14wx/staffSync Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation. | Skills |
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