Claude Code Skills · page 126

Use whenever the user wants to find, rank, or shortlist directories, listing sites, or launch platforms where they can submit a software product, SaaS, app, tool, or startup — to get backlinks, referral traffic, and launch-day reach. Triggers on "where can I submit my SaaS for launch", "list of Product Hunt alternatives", "directories to get backlinks for our app", "high-authority software listing sites", "where should I list my startup", or "pull submission details for these 8 directory domains", even when described indirectly (we're launching next week, where do we post; how do we get backlinks by listing in directories). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-mcp-directories for MCP-server listings and find-ai-directories for AI-tool / AI-agent / agent-skill listings. Skip finding a firm/agency to hire (use the find-* agency skills), finding products *inside* a directory ("recommend the best CRM"), building a directory site (do-the-work), local/business directories for brick-and-mortar listings, and link-building *services*.

Use whenever the user wants to find, shortlist, vet, or enrich US recruiting and staffing firms — executive search/retained search, RPO, tech/sales/healthcare recruiting, contingent/contract staffing, and temp staffing. Triggers on "find me an executive search firm for a CFO search", "shortlist three retained-search boutiques in NY focused on tech", "we need RPO support for a 50-engineer hiring push", or "pull contact info for these 8 staffing firm domains", even when described indirectly (need help hiring at scale, executive recruiter for senior roles). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip when the user wants to hire someone as their own employee (job-board questions, in-house recruiter hires, "where should I post the role"), individual job-seekers looking for recruiters to represent them, candidate-side career coaching, non-US firms, individual freelance recruiters.

Use whenever the user wants to find, shortlist, vet, or enrich US SEO agencies — technical SEO, on-page/off-page, link-building, content-led SEO, local SEO, ecommerce SEO, B2B SEO, and SEO audits. Triggers on "find me an SEO agency in Texas", "shortlist three technical SEO consultancies for SaaS", "link-building and on-page for our ecommerce store", or "pull contact info for these 8 SEO firm domains", even when described indirectly (organic traffic flat, improve Google rankings, search visibility). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings, third-party listings. Defer to find-marketing-agency when scope spans multiple marketing services beyond SEO. Skip SEM/PPC/paid-search asks, web-dev asks (use find-web-developer), "how do I rank" DIY questions, SEO tool recommendations (Ahrefs, Semrush), in-house SEO hires, non-US firms, individual freelancers.

Use whenever the user wants to find, shortlist, vet, enrich, or research US professional-services firms — law, marketing, consulting, accounting, IT services, architecture, engineering, HR, PR, design, and similar B2B service providers. Triggers on requests like "find me a PPC agency in California", "shortlist three boutique IP law firms", "build a longlist of 50 mid-size IT consultancies", or "here are 12 agency domains — pull contact info and confirm which are US-based", even when the need is described indirectly without naming a category. Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog with filters for industry, services, location, size, ratings, and third-party listings. Skip when the user is asking for personal/consumer services for themselves (an individual's own legal, tax, or medical needs), non-US firms, individual freelancers, retail/ecommerce/SaaS-product companies, recruiting-an-employee tasks, or general web research that doesn't need a structured firm directory.

Use whenever the user wants to find, shortlist, vet, or enrich US software development firms — custom software, web development, mobile app development, backend/API development, DevOps/cloud, system integration, and hosting. Triggers on "find a software dev shop in Austin", "shortlist three custom-software firms with healthcare experience", "we need a mobile app developer for our iOS launch", or "pull contact info for these 10 dev shop domains", even when described indirectly (build a tool, ship a feature, technical partner). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-web-developer for strictly website/landing-page projects. Defer AI/ML, ML pipelines, model building, and data-engineering asks — those are a sibling industry, not software development. Skip in-house engineer hires, code-writing/debugging tasks, cloud-product comparisons, hardware/civil engineering, non-US firms, individual freelancers.

Use whenever the user wants to find, shortlist, vet, or enrich US web development firms — building, refreshing, or rebuilding marketing sites, landing pages, ecommerce, WordPress/Webflow/Shopify, headless CMS, microsites, and web frontend work. Triggers on "find a web developer for a marketing landing page", "shortlist three Webflow agencies in California", "rebuild our ecommerce site on Shopify", or "pull contact info for these 8 web dev shop domains", even when described indirectly (redesign and rebuild our site, ship a microsite). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for custom backend/API/mobile/internal-tool work — anything beyond a website. Defer to find-marketing-agency when scope spans broader marketing beyond the build. Skip in-house web-engineer hires, "how do I build X" DIY questions, hosting/CMS-product comparisons, non-US firms, individual freelancers.

The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".

Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.

Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.

Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + readiness polling); query construction is documented inline in this skill — write `documents.search(...)` calls directly, validated against `pc.preview.indexes.describe(...)` output.

Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.

Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.

Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.

Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.

Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools.

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USE WHEN AIDEV anchors / per-dir AGENTS.md / ADRs have changed in this repo and the smart-context-surfacing PreToolUse hook needs an updated manifest. Rebuilds `.claude-leverage-context-map.json` at the repo root by running `scripts/build-context-map.py`. Read-only on source — only writes the manifest.

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Scaffolds a complete .NET solution following Clean Architecture principles with proper layer separation (API, Application, Domain, Infrastructure). Creates project structure, dependency injection setup, and cross-cutting concerns configuration.

Generates CQRS Commands with Handlers, Validators, and Request DTOs following Clean Architecture patterns. Commands represent actions that modify state and return Result types for proper error handling.

Generates CQRS Queries with Handlers and Response DTOs for read operations. Uses Dapper for optimized read queries, bypassing the domain model for better performance.

Generates Domain Entities following DDD principles with factory methods, private setters, domain events, and proper encapsulation. Supports aggregate roots, child entities, and value objects.

Generates Repository interfaces and implementations following the Repository pattern. Provides data access abstraction for aggregate roots with EF Core implementations.

Generates Entity Framework Core configurations using Fluent API. Maps domain entities to database tables with proper relationships, constraints, and conventions.

Generates RESTful API Controllers with proper routing, versioning, authorization, and MediatR integration. Follows REST conventions and Clean Architecture patterns.

Generates Minimal API endpoints following Microsoft's recommended approach. Creates fast, testable HTTP APIs with minimal code using MapGet/MapPost/MapPut/MapDelete. Preferred over controller-based APIs for new projects.

Implements the Result pattern for explicit error handling without exceptions. Provides Result, Result<T>, and Error types for clean, predictable control flow in domain-driven applications.

Generates Domain Events and their handlers following DDD patterns. Implements event raising in entities, MediatR notification handlers, and the Outbox pattern for reliable event processing.

Generates MediatR Pipeline Behaviors for cross-cutting concerns like logging, validation, exception handling, caching, and performance monitoring. Implements the decorator pattern around handlers.

Generates FluentValidation validators for commands and queries. Includes common validation rules, custom validators, async validation, and integration with MediatR pipeline behaviors.

Configures JWT Bearer authentication for .NET APIs. Includes token generation, validation, refresh tokens, and user context extraction from claims.

Implements permission-based authorization with custom attributes, policy providers, and authorization handlers. Provides granular access control beyond simple role-based authorization.

Implements the Outbox pattern for reliable domain event processing. Ensures events are persisted in the same transaction as the aggregate changes and processed asynchronously with guaranteed delivery.

Generates scheduled background jobs using Quartz.NET. Includes job definitions, triggers, cron scheduling, dependency injection, and persistent job store configuration.

Implements email service abstraction with SendGrid provider. Includes template support, localization, async sending, and domain event integration for transactional emails.

Implements email service using AWS SES for .NET APIs. Designed for applications with HTML template support, placeholder replacement, and Result pattern error handling.

Configures health checks for database, external services, and custom application checks. Provides liveness and readiness endpoints for container orchestration.

Generates audit trail infrastructure for entities. Implements IAuditable interface, EF Core SaveChanges interceptor, and automatic population of CreatedAt, UpdatedAt, CreatedBy, and UpdatedBy fields.

Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.

Implements the Specification pattern for encapsulating query logic. Enables composable, reusable, and testable query criteria with support for includes, ordering, and pagination.

Generates unit tests for command and query handlers using xUnit and NSubstitute. Implements Arrange-Act-Assert pattern with comprehensive test coverage for success and failure scenarios.

Configures integration tests with WebApplicationFactory and Testcontainers. Provides test database setup, authentication helpers, and utilities for testing API endpoints with real dependencies.

Configures structured logging with ILogger<T> and ILoggerFactory following Microsoft best practices. Includes Serilog setup, log enrichment, and logging source generators for high-performance logging.

Implements ASP.NET Core rate limiting middleware for API protection. Covers fixed window, sliding window, token bucket, and concurrency limiters with custom policies.

PostgreSQL database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Npgsql and EF Core.

SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.

Implements the Options pattern for strongly-typed configuration in .NET. Covers IOptions<T>, IOptionsSnapshot<T>, and IOptionsMonitor<T> with validation and reload support.

Audit designs against 19 rules across Figma files and code (HTML/CSS/React/Vue/Tailwind). Auto-detects framework and design system (MUI, Chakra, shadcn/ui, Ant Design, Radix, Bootstrap). Runs code superpowers across all 19 categories: aria, focus, contrast, tokens, responsive, motion, forms, navigation, spacing, states, microcopy, elevation, iconography/SVG. Flags color blindness risk. Audits dark patterns, ethical design, Nielsen's heuristics. Outputs before/after diffs, developer handoff reports, wireframe-to-spec. English and Korean. Triggers on: check my design, review my UI, audit my layout, is this accessible, design review, color contrast, WCAG, a11y, pixel perfect, Figma audit, CSS check, dark patterns, is this GDPR compliant, is this manipulative, is my form accessible, is my dark mode correct, wireframe to spec, heuristic review, Nielsen audit, usability heuristics, 디자인 검토, 접근성 확인, 색상 대비, 다크 패턴, 와이어프레임 스펙.

Complete, populate and fill out 3-statement financial model templates (Income Statement, Balance Sheet, Cash Flow Statement). Use when asked to fill out model templates, complete existing model frameworks, populate financial models with data, complete a partially filled IS/BS/CF framework, or link integrated financial statements within an existing template structure. Triggers include requests to fill in, complete, or populate a 3-statement model template

Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model, including financial-model integrity checks like BS balance, cash tie-out, and logic sanity. Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", and "model review".

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", and "this data is messy".

Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format.

Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.

This skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.

Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.

Updates a presentation with new numbers — quarterly refreshes, earnings updates, comp rolls, rebased market data. Use whenever the user asks to "update the deck with Q4 numbers", "refresh the comps", "roll this forward", "swap in the new earnings", "change all the $485M to $512M", or any request to swap figures across an existing deck without rebuilding it.

Investment banking presentation quality checker. Reviews a pitch deck or client-ready presentation for (1) number consistency across slides, (2) data-narrative alignment, (3) language polish against IB standards, (4) visual and formatting QC. Use whenever the user asks to review, check, QC, proof, or do a final pass on a deck, pitch, or client materials — including requests like "check my numbers", "reconcile figures across slides", "is this client-ready", or "what am I missing before I send this out".

Operating doctrine for AI-era enterprise sales. Use when an agent works on accounts, deals, outreach, coaching, or pipeline — any revenue-affecting decision.

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Use when the user wants to set up a new project or add enforcement tooling (TDD, secret scanning, file size limits, git hooks, CLAUDE.md templates) to an existing project. Also use when the user says "bootstrap", "scaffold", "set up my project", or "add quality enforcement".

Turn Markdown outlines, notes, docs, or plans into an interactive mind map — a single self-contained HTML file that opens offline anywhere (markmap.js, white-on-dark, zoom/expand/export toolbar, search that keeps matches in context). Use whenever the user asks for a mind map, markmap, or concept map, wants to visualize or diagram the structure of a document or topic, summarize notes as a navigable tree, make an outline clickable or explorable, or embed such a map in Streamlit — even when they don't literally say 'mind map'.

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Use when the user wants to create a new CLAUDE.md from scratch for a repository, bootstrap project memory for a new repo, set up a repo for Claude Code for the first time, or completely rewrite an existing CLAUDE.md deemed unsalvageable. Produces a lean, high-signal file under 80 lines (120 hard cap) by scanning the repo for mechanical content (stack, commands, structure) and interviewing the user for judgment content (WHY, gotchas, out-of-scope, approval gates, testing philosophy, external docs like Confluence pages). NOT for auditing or incrementally improving an existing CLAUDE.md — that is the claude-md-improver skill's role.

Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".

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Creates and switches to a new, conventionally named branch — derives the name from an inline description, conversation context, or local git diffs. Preserves all local changes. Never commits or pushes. Use when you want a properly named branch for new or in-progress work.

Iterative auto-fix code review — runs `/optimus:code-review` in a fresh subagent context per iteration, applies fixes, runs tests, bisects failures, and continues until convergence or the iteration cap (default 8, hard cap 20). Each iteration runs in an isolated subagent so context does not accumulate. Requires a test command in .claude/CLAUDE.md. Use when single-pass review leaves issues or for thorough cleanup before a release.

Reviews local changes, PRs/MRs, or branch diffs against project coding guidelines using 5 to 7 parallel review agents (bug detection, security/logic, guideline compliance x2, code simplification, test coverage, contract quality). Use before committing, on open PRs/MRs, or to review any branch diff. HIGH SIGNAL only: real bugs, logic errors, security concerns, and guideline violations. For iterative auto-fix in a loop, use `/optimus:code-review-deep`.

Suggests conventional commit messages by analyzing staged, unstaged, and untracked git changes — read-only, never commits. Use when a commit message suggestion is needed without actually committing.

Stages, commits, and optionally pushes local changes with a conventional commit message — analyzes diffs, generates the message, confirms with the user, and commits. On protected branches, offers to create a feature branch automatically. Multi-repo aware. Use when ready to commit work in one step.

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Prepares a project for Claude Code — generates CLAUDE.md with progressive disclosure docs, auto-format hooks, and test infrastructure (framework, coverage tooling, testing docs). Detects empty directories and offers new-project scaffolding via official stack tooling before setup. Also audits and syncs existing documentation against source code. Replaces /init. Supports single projects, monorepos, and multi-repo workspaces (separate git repos under a shared parent directory). Use to bootstrap a new or existing project for Claude Code, or re-run to update an outdated setup.

Fetches and optimizes context from a JIRA issue for AI-assisted development. Searches assigned issues or fetches by key. Distills title, description, acceptance criteria, sprint context, and comments into a structured task description. Analyzes the codebase to surface missing criteria, scope, and risks. Optionally enriches the JIRA issue with a structured analysis comment, and for Complex-scope work can spawn implementation tickets in JIRA. Re-running on the same key refreshes the local task with the latest JIRA state instead of overwriting prior enrichment. Use before /optimus:tdd, /optimus:brainstorm, or /optimus:branch to pull task context from JIRA, or to refresh existing context after JIRA edits.

Configures Claude Code permissions for safe agent autonomy. Creates settings.json with allow/deny rules and a path-restriction hook. Use after /optimus:init to enable autonomous agent workflows, or standalone to lock down a project's permission boundaries.

Creates or updates a pull request (GitHub) or merge request (GitLab) for the current branch using the Conventional PR format — intent, summary, changes, rationale, and test plan. Captures the implementation conversation's intent into the PR description when run in the same session. Use when a branch is ready for review, or to update an existing PR/MR description.

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Iterative project-wide refactoring — runs `/optimus:refactor` in a fresh subagent context per iteration, applies fixes, runs tests, bisects failures, and continues until convergence or the iteration cap (default 8, hard cap 20). Supports `testability` or `guidelines` focus to prioritize finding categories. Each iteration runs in an isolated subagent so context does not accumulate. Requires a test command in .claude/CLAUDE.md. Use for thorough guideline alignment or testability cleanup before /optimus:unit-test.

Refactors existing code for guideline compliance and testability using 4 parallel analysis agents (guideline compliance, testability barriers, duplication/consistency, code-simplifier). Two goals — align code with project guidelines AND make untestable code testable so /optimus:unit-test can safely increase coverage. Use after /optimus:init to align existing code, before /optimus:unit-test to remove testability barriers, or periodically to prevent tech debt. Supports "testability" focus (after unit-test flags untestable code) or "guidelines" focus (after init establishes rules) to prioritize finding categories, and flexible scoping. For iterative refactor in a loop, use `/optimus:refactor-deep`.

Removes files installed by /optimus:init and /optimus:permissions from the project. Compares each file against plugin templates and classifies as unmodified, likely generated, or user-modified. Always asks before deleting. Git-tracked files are noted as recoverable. Tests are never touched. Monorepo and multi-repo aware. Use for clean reinstall or to stop using optimus.

Use when starting a new project or product and you want a docs-first plan before writing code — scaffolds an empty, product-neutral spec-driven-development cascade (product vision, MVP PRD, target tech-stack) for a human to fill, then hands off to brainstorm. Emits skeletons only; it never authors product content and never overwrites existing docs.

Guides test-driven development — decompose a feature or bug fix into behaviors, then cycle through Red (failing test) → Green (minimal implementation) → Refactor for each one. Requires /optimus:init and working test infrastructure. Use when starting a new feature or bug fix with test-first discipline.

Iterative test-coverage improvement loop — dispatches `/optimus:unit-test` (unit-test phase) and `/optimus:refactor` with testability focus (refactor phase) into fresh subagent contexts per cycle, applies tests, runs the test suite, bisects refactor failures, and continues until coverage plateaus or the cycle cap (default 5, hard cap 10). Use to drive coverage up on a codebase that has untestable barriers — the loop alternates between writing tests and unblocking testability so a single skill cannot stall.

Implements an approved spec by having Claude design and run its own Claude Code dynamic workflow (real parallel subagents) — you hand it the goal and constraints, it chooses the orchestration. Test-first is enforced as a quality bar (tests accompany or precede code and the suite is left green), not as supervised Red-Green-Refactor. A peer of /optimus:tdd for spec implementation; prefer it for large or parallelizable specs where one linear pass is slow. Requires /optimus:init and a spec (auto-detects docs/specs/ or docs/jira/, or pass a path). Uses meaningfully more tokens than a normal session. Use when a spec is ready to build and you want fan-out implementation instead of turn-by-turn TDD cycles.

Creates a git worktree for isolated parallel development — new branch in a separate directory with project setup and test baseline. Enables multiple Claude Code sessions on different tasks simultaneously. Multi-repo aware. Use when you need to work on something else without disturbing current work.

Use when creating or updating AGENTS.md files, .github/copilot-instructions.md, or other AI agent rule files, onboarding AI agents to a project, standardizing agent documentation, or when anyone mentions AGENTS.md, agent rules, project onboarding, or codebase documentation for AI agents.