Discover and install skills, docs, and rules 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 | |
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 | |
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 | |
RevenueCat/ai-toolkit Use when the user asks about RevenueCat data, analytics, charts, or KPIs — querying charts with get-chart-options-schema and get-chart-data, interpreting subscription metrics, or sharing dashboard chart links. For forecasts, projections, or run-rates, use revenuecat-forecasting. | Skills | |
equinor/fusion-framework Resolves unresolved GitHub PR review threads end-to-end: evaluates whether each review comment is correct, applies a targeted fix when valid, replies with rationale when not, commits, and resolves the thread. USE FOR: unresolved review threads, PR review feedback, changes requested PRs, PR review URLs (#pullrequestreview-...), fix the review comments, close the open threads, address PR feedback. DO NOT USE FOR: summarizing feedback without code changes, creating new PRs, or read-only branches. | Skills | |
elevenlabs/skills Generate music using ElevenLabs Music API. Use when creating instrumental tracks, songs with lyrics, background music, jingles, or any AI-generated music composition. Supports prompt-based generation, composition plans for granular control, and detailed output with metadata. | Skills | |
depot/skills Handles Depot CLI installation, authentication, login, project setup, organization management, and API access. Use when installing the Depot CLI, logging in with `depot login`, creating or managing Depot projects, configuring API tokens or OIDC trust relationships, setting up depot.json, managing organizations, resetting build caches, or using the Depot API/SDKs. Also use when the user asks about Depot authentication methods, token types, environment variables, or general Depot platform setup that isn't specific to container builds, GitHub Actions runners, or Depot CI. | Skills | |
pskoett/pskoett-ai-skills Captures learnings, errors, corrections, and feature requests to enable continuous improvement. Use when: (1) User corrects Claude ('No, that's wrong...', 'Actually...'), (2) User requests a capability that doesn't exist, (3) Claude realizes its knowledge is outdated or incorrect, (4) A better approach is discovered for a recurring task, (5) Receiving a Handoff block from self-healing (a recurring verified heal with Recurrence-Count at least 3) to distill into a memory file or new skill. For ACTIVE runtime failures where the agent needs to apply and verify a fix mid-task, use `self-healing` instead (it files HEAL- entries with proof; self-improvement promotes accumulated patterns). Also review learnings before major tasks. For CI-only/headless learning capture, use self-improvement-ci. | Skills | |
Branchout/branchout-skills Use when working in a manyrepo workspace managed by Branchout (~/projects/*/), finding repos the user mentions by name (even if not exactly) within the naming-convention-based folder structure, cloning updating or adding repos, running isolated builds via wrappers like branchout mvn or branchout yarn, or when someone mentions Branchout or asks what Branchout is. Detect by Branchoutfile or .branchout in current or parent directories. | 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 | |
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 | |
Dicklesworthstone/pi_agent_rust Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, setting up blockchain test suites, or validating DeFi protocols. | Skills | |
Dicklesworthstone/pi_agent_rust Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates. | Skills | |
Dicklesworthstone/pi_agent_rust Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts. | Skills |
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