Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Full Sentry Snapshots setup for Apple/Cocoa projects. Use when asked to "setup SnapshotPreviews", "setup Apple snapshot testing", "upload Apple snapshots to Sentry", "setup Apple snapshot GitHub Actions", or "setup Apple selective snapshot testing".
Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking for the closed loop, proper verification, or an evidence trail. Ordinary work does not run this.
Code review checklists, PR review patterns, feedback techniques, and review automation. Use when user asks to "review this code", "code review checklist", "PR review template", "review best practices", "write review feedback", "review this PR", "how to give feedback on code", "PR too large", "split this PR", "review turnaround time", "automated code review", "CODEOWNERS", "pair review", "when to request changes", "code review tool", "review security", "design review", "performance review", "test coverage review", or any code review and feedback tasks.
Read-only review leaf executor. By default it reviews the current diff, and it also reviews design proposals, or performs a repo-level audit on user request. Returns results in a single round and does not create sub-agents. Formerly named cs-code-review.
Check docs.celo.org, docs.minipay.xyz, and other live sources for drift against this repo's cached reference files (contracts, network info, docs sitemap, ecosystem, grants, MiniPay, wFIAT access) and fix or flag it. Use when asked to check docs updates, run the docs watch, or on the scheduled weekly run.
Use when reviewing a pull request in the deepseek-harness repo — orients the reviewer to this codebase's standards (AGENTS.md conventions, defensive patterns, ADRs, quality gates) and the review-specific checks that code alone can't show
Generate a complete Midnight Network hello-world DApp from scratch — Compact smart contract, headless Node.js tests with vitest, wallet setup using testkit-js FluentWalletBuilder, DUST generation, deploy and call transactions, and all supporting config. Use when a user wants to build a full Midnight DApp, bootstrap a new project, understand the end-to-end lifecycle, or needs working boilerplate for wallet + contract + tests. This skill produces all files needed to run on local devnet (Docker stack) using the official midnightntwrk/example-hello-world reference implementation.
Triage and fix review findings that already exist on a GitHub PR, then reply and resolve the threads. Use on a CodeRabbit review URL, a PR whose review comments still need working through, or a local /review-pr findings file. When the PR has no findings yet, /review-pr produces them first.
This skill should be used to review, lint, or fix Markdown formatting in READMEs, ADRs, runbooks, how-tos, design docs, or any `.md` file. PROACTIVELY activate on "review this markdown", "lint this doc", "lint this README", "style-check this doc", "fix the formatting", "review my README", "is this markdown valid?", "fix the headings", "fix the list indentation", "ATX vs setext", "should I use a TOC?", "markdown style guide", or "Markdown linter." Provides: two-pass syntax/style review with line-referenced findings.
Comprehensive Odoo security auditor for model access rules, HTTP route authentication, sudo() usage, SQL injection risks, and record rule completeness across Odoo 14-19. <example> Context: User wants a full security audit user: "Run a complete security audit on my HR module" assistant: "I will audit access rules, HTTP routes, sudo usage, and SQL injection risks across all files in the module." <commentary>Full audit trigger - comprehensive security review.</commentary> </example> <example> Context: User wants to check access rules user: "Check if all models have proper access rules in ir.model.access.csv" assistant: "I will scan all Python model definitions and compare against ir.model.access.csv to find missing read/write/create/unlink rules." <commentary>Access check trigger - ir.model.access.csv completeness.</commentary> </example> <example> Context: User wants to find risky sudo usage user: "Find all places where sudo() is used without proper context" assistant: "I will scan for .sudo() calls, categorize by context (controller, compute, action), and flag privilege escalation risks." <commentary>Sudo finder trigger - privilege escalation risk analysis.</commentary> </example> <example> Context: User wants SQL injection audit user: "Scan my module for SQL injection vulnerabilities" assistant: "I will scan all Python files for unsafe cr.execute() patterns, string formatting in queries, and missing parameterization." <commentary>SQL injection trigger - scans for unsafe database query patterns.</commentary> </example>
Diff-based PR review across code quality, test coverage, silent failures, type design, and comment quality with severity-ranked findings. Triggers on: "review my PR", "review this code", "check my changes", "audit this PR", "code review". NOT for pre-landing gate, use pre-landing-review.
Creates isolated container environments for testing local uncommitted changes before pushing. Use when testing library changes, multi-repo coordination, or validating "works on my machine" → "works in CI". Provides git bundle snapshots, embedded git server, selective URL rewriting, and package manager cache isolation. Works with any coding agent via standalone CLI, shell scripts, or Docker Compose.
Professional UX and landing page audit. Identifies the top 3 critical quality issues, auto-implements fixes, and generates a Loom video transcript to share improvements with the lead. Use when auditing a website or landing page for professionalism and craft.
Read-only audit of one persisted skore report: `audit/NN_<stem>.py` (jupytext percent), 1:1 with `experiments/` and `journal/`. Do not execute it. Run `materialize.py` once. Never call `skore.evaluate` or `project.put`. TRIGGER when a completed run needs an audit, the user asks to audit, show, or re-audit an experiment, a re-run overwrote the same `put()` key, or they want a narrative of a past experiment without `journal/ideas/` files. STOP when `status.setup.pending` is non-empty (load `setup-ml-project`), or when there is no approved design, no report, or no agent feature: explain and send the user to setup, `evaluate-ml-pipeline`, or `model-ml-pipeline`. Also stop for raw-data exploration or sourcing a future experiment. HOW TO USE: confirm journal, experiment, smoke, and report; place the file from `templates/audit.py`; copy `templates/materialize.py` to scratch; execute once; derive G-AUDIT-FINDING; then the continue-or-close gate. Resolve skore symbols with `api get`.
Integrate third-party CAPTCHA-solving services into automated browser sessions for authorized testing, QA, and legitimate automation workflows on sites you own or have permission to access. Use this skill when the user needs to: handle CAPTCHA challenges in Playwright or Puppeteer test suites, integrate reCAPTCHA v2/v3, hCaptcha, Cloudflare Turnstile, or image-based CAPTCHA solving into approved automation, or complete CAPTCHA token injection in QA scripts. Also triggers on technical terms: \"recaptcha integration\", \"hcaptcha integration\", \"turnstile integration\", \"captcha solving service\", \"CAPTCHA token injection\", \"captcha API\". Always use this skill for any authorized CAPTCHA-related automation — even simple cases benefit from correct token injection patterns.
File or link CawPlan defects from failed/blocked TestRail Results via QA Insights A4. Use when: SQA wants to turn a TestRail Failed/Blocked Result into a CawPlan Ticket, paste a tests/view URL to file a defect, review an A4 defect draft, create a BUGFIX/FEATURE ticket, or link an existing ticket back to TestRail defects. Cold start with tests/view MUST call execution failures --test-id BEFORE defects draft; never use test_id as the draft path result_id. A4 write path (create-ticket) MUST use --body-file or --body JSON — never field flags like --description or --result-id. NOT for: importing test cases (use `cawplan-testcase-import`); creating TestRail Plans/Runs (use `cawplan-testplan-layout`); release risk assessment; generating requirements or test points.
Orchestrate CawPlan QA Insights TestRail test plans from Version/Ticket-scoped imported cases with preview-first workflow. Use when: creating or executing A2 TestRail Plans/Runs for a CawPlan version or one or more CawPlan tickets after cases have been imported. NOT for: importing test cases (use `cawplan-testcase-import`); generating test points; execution summaries; defect filing; release risk. Do NOT use `cawplan api GET /api/v1/public/openapi/product/{product_id}` to resolve product name — that route does not exist.
Use when writing new code, naming modules/files/functions/variables, reviewing or refactoring for readability, reducing complexity, deciding whether an abstraction belongs, or reviewing with CUPID; also the constructive hacker ethos (curiosity, craftsmanship, open-source contribution, remove drudgery; never unauthorized access). Behavior-preserving refactors within a function, class, or file route to the `refactoring/` sub-skill. Pairs with language skills (typescript, go, lisper). DO NOT USE for behavior changes (`testing` (TCRDD reference)), cross-file dependency untangling (`mikado-method`), or pure simplicity/YAGNI debates (`simple`)
This skill should be used when the user asks to "triage coderabbit", "check coderabbit comments", "analyze coderabbit review", "fetch coderabbit findings", "coderabbit PR", "review coderabbit suggestions", "run coderabbit review", "coderabbit review", "review with coderabbit", or mentions analyzing CodeRabbit PR comments or running CodeRabbit CLI review locally.
Within an isolated scope, implement a confirmed Comet GitHub Issue or review blocker through its owning Native or Classic workflow, risk-matched tests, generated Runtime checks, and careful delivery. Use when the user explicitly asks to fix a verified Issue or PR problem.
Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs.
Parallel C++ Core Guidelines code review using multiple specialized sub-agents. Use when reviewing C++ code, modules, or files against C++ Core Guidelines to identify violations. Each sub-agent reviews against a specific guideline section (Functions, Classes, Resource Management, etc.) and outputs findings to separate markdown files in the review/ directory, followed by a consolidated summary.
Hunt for real bugs in the vigiles CLI/codebase with a parallel expert fan-out, then FIX them directly — source-trace each defect to file:line, add a regression test, commit per theme. Use when asked to dogfood vigiles, find/fix bugs across the CLI the source-traced way, or fan out agents to audit the tool on itself.
Audit a doc, guide, README, or block of prose against the good-docs-writing style guide and report violations. Use when the user asks to review, critique, lint, or check the voice and tone of documentation or text. Produces a structured findings report (file:line, rule violated, offending text, suggested rewrite) and does NOT edit files unless explicitly asked.
Migrate to Langfuse from another LLM observability/evals platform (LangSmith, Arize AX, Phoenix, Braintrust, Helicone, Promptfoo, ...). A one-off vendor switch: live tracing cutover plus transfer of historical traces, datasets, prompts, and evaluators. Not for anything Langfuse-to-Langfuse — SDK or version upgrades, project/region moves, self-hosted to Cloud — and not for moving in-code prompts into Langfuse; the main langfuse skill covers those.
Guide for using molt verify to compare source and target databases for schema and row-level consistency after a migration. Use when running verify commands, tuning concurrency/sharding, handling schema mismatches, or validating data integrity post-migration.
Generate OpenHarmony C++ unit tests following HWTEST_F framework conventions. Supports Mock strategies, BUILD.gn configuration, NDK/NAPI interfaces, and maintains 75%+ coverage requirements with strict code style consistency. Use when generating unit tests for OpenHarmony C++ source files.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
