Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
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Refactor Express.js/Node.js code to improve maintainability, readability, and adherence to best practices. Transforms callback hell, fat route handlers, and outdated patterns into clean, modern JavaScript/TypeScript code. Applies async/await, controller-service-repository architecture, proper middleware patterns, and ESM modules. Identifies and fixes anti-patterns including blocking event loop, improper error handling, forEach with async callbacks, and memory leaks.
Use when you have a PoC or prototype and want to rebuild it as a production-quality system from scratch. Distills the functional intent and lessons learned from the prototype, discards the implementation, and produces a reforge-seed.md that seeds a greenfield architect session. Do NOT use to improve an existing production system — use architect for that.
Guidance for generating legal chess moves using only regex pattern matching and substitution. This skill applies when implementing chess move generators constrained to regex-only solutions, FEN notation parsing/manipulation, or similar pattern-matching-based board game logic. Use this skill for tasks requiring regex-based state transformations on structured string representations.
Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.
Values a company relative to comparable firms using price multiples (PE, PBV, EV/EBITDA, EV/Sales). Implements the four-step framework (define, describe, analyze, apply) with both simple peer comparison and sector regression approaches. Use when valuing a company relative to peers, analyzing multiples, selecting comparable companies, or when user mentions PE ratio, EV/EBITDA, relative valuation, comparable companies, trading multiples, or price-to-book.
This skill should be used when the user asks to "create a replit prompt", "write a prompt for replit", "optimize for replit agent", "prepare instructions for replit", or mentions building something with Replit Agent. Transforms user requirements into optimized, structured prompts that Replit Agent understands and executes accurately with minimal iterations.
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
Provides repository design rules and anti-patterns in DDD. Checks naming conventions based on aggregates, method design based on CQS (Command Query Separation), and input/output type constraints. Use when detecting repository design problems during code review, new implementation or refactoring. Target languages: language-agnostic (Java, Kotlin, Scala, TypeScript, Go, Rust, Python and all others). Triggers: starts on repository design related requests such as "review the repository design", "the Repository name is wrong", "the return value of findById", "the repository returns DTOs", "I created a repository based on a table name", "repositories per aggregate", "repository anti-patterns", "repository CQS".
Requirement design workflow - mandatory before any creative development (new features, components, behavior changes, API/DB design): triage, parallel exploration, one-question-at-a-time clarification, adversarial validation and 2-3 option comparison produce a spec, then hand off to writing-plans. Use exploring first while the idea is unsettled; not for pure Q&A, test runs, or no-design-space small fixes (use quick-fix).
Use when the user has a research proposal (problem + proposed methodology + planned experiments) and wants it iteratively strengthened until it clears a passing grade. ScholarEval grades the proposal against the literature (Soundness + Contribution), a Judge scores that feedback 0-100 on a fixed rubric, and a Reviser rewrites the proposal to fix the worst points without diluting the research question; loops until the grade passes or the budget is hit. Not for generating a proposal from scratch, and not for running a literature survey on its own.
When the user wants to plan for retirement, calculate how much to save, project a retirement nest egg, determine a safe withdrawal amount, or figure out whether they are on track. Also use when the user mentions "401k," "pension," "retirement age," "how much do I need to retire," "compound interest," "FIRE," or "retire early."
Design a revenue model for a solopreneur business — how money flows in, from whom, and on what cadence. Use when deciding how to monetize a product or service, choosing between revenue streams, structuring recurring vs one-time income, or diversifying revenue. Covers all major revenue model types, selection criteria, revenue stream stacking, and the relationship between revenue model and product design. Trigger on "how will I make money", "revenue model", "monetization strategy", "revenue streams", "recurring revenue", "how to monetize", "business revenue model", "diversify income".
Function-triggered outreach execution via Revor for LinkedIn, Email, and WhatsApp. Use this skill to detect outreach intent, validate sending prerequisites, draft channel-ready copy, and dispatch only after requirements are met and user confirmation is clear. If required recipient/contact data is missing, ask the user to provide it or search it first.
Use when the user is torn over a directional decision — first judge whether the direction is right, then consider how to do it well. Typical triggers: "I'm torn about whether to do XX", "I've worked on it a long time but results are poor", "is the direction right", "is it worth persisting", "is this a direction problem or an execution problem". Core framework: "do the right things" (direction judgment, the way) vs "do things right" (execution efficiency, the technique); the two cannot be reversed. Not for: purely execution-level problems (the right direction is already set and only methods are needed), or trivial everyday matters.
Gate 0 of the development cycle. Executes code implementation using the appropriate specialized backend agent based on task content and project language. Handles TDD, coverage, docker-compose/local runtime, basic health/observability verification, and delivery verification inside one backend-owned gate.
Troubleshoot Agent Jobs running in ROCK sandboxes with rockcli (rc) and produce diagnostic reports. Use when the user provides a sandbox or experiment ID, wants to check a job's status, logs, reward and execution trace, or needs to diagnose job failures, zero rewards, agent crashes and similar issues.
Research mountain routes and generate comprehensive route beta reports for North American peaks, aggregating weather forecasts, avalanche conditions, daylight windows, trip reports, and access info from PeakBagger, SummitPost, WTA, AllTrails, and regional avalanche centers. Use when planning a climb, hike, or scramble, or when asked for route beta, trail conditions, peak research, or mountaineering trip planning.
Check whether an argument's premises actually support its conclusion, and identify logical fallacies. Use when reviewing a design decision, technical proposal, product argument, or any reasoning where the conclusion needs to hold up under scrutiny. TRIGGERS: 'validate this argument', 'does this logic hold', 'check my reasoning', 'is this a fallacy', 'find the flaw in this', any proposal or decision where the reasoning chain matters. Pairs well with logic-consistency-check when a full document needs scrutiny.
Identify the right influence approach and construct it for the context. What actually changes minds and drives action. Triggers: 'how do I persuade', 'how do I change their mind', 'what would convince them', 'how do I get buy-in', 'how do I influence', 'influence strategy', 'how do I pitch this', 'what makes a persuasive case', 'how do I get them to say yes'.
Design a sales compensation plan (OTE & pay mix, quotas & ramp, commission mechanics, and retention-aligned incentives) and produce a Sales Comp Plan Pack. Use for AE/SDR/AM comp plans, early sales hires, and GTM scaling. Category: Sales & GTM.
Mailgun (Sinch) platform help — developer-first transactional email API and SMTP relay with inbound routing, webhooks, and Mailgun Optimize deliverability tools. Use when Mailgun emails not delivering, DNS records (DKIM/SPF) failing verification, inbound routing not catching messages, webhooks not firing, templates rendering wrong, Mailgun Optimize showing poor inbox placement, or API calls returning errors. Do NOT use for general email deliverability strategy (use /sales-deliverability), cross-platform email marketing (use /sales-email-marketing), or email open/click tracking strategy (use /sales-email-tracking).
Design and audit B2B sales qualification and discovery workflows covering ICP fit, buyer pain, urgency, authority, budget, decision process, procurement path, champion strength, technical fit, risk, no-fit disqualification, mutual action plans, CRM hygiene, and handoff to solution engineering or customer success. Use when agents need to decide whether an opportunity is real and what must be learned next.
ZoomInfo platform help — SalesOS, MarketingOS, OperationsOS, Copilot, API, integrations. Use when ZoomInfo searches aren't returning the right prospects, enrichment data is incomplete or stale, intent signals aren't surfacing target accounts, Engage sequences have low reply rates, advertising audiences aren't matching your ICP, FormComplete isn't auto-filling leads, data orchestration rules aren't firing, or Copilot recommendations seem off. Do NOT use for general enrichment strategy (use /sales-enrich), general intent strategy (use /sales-intent), general cadence design (use /sales-cadence), general data hygiene strategy (use /sales-data-hygiene), or general B2B advertising strategy (use /sales-b2b-advertising).
CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.
Evidence-based assessment of whether an SAP API/interface usage scenario aligns with the SAP API Policy (v.4.2026a). Use whenever someone asks whether a way of calling SAP is allowed or compliant—for example, Published API vs internal/private/"confidential" API status, "Documented Use", whether a third-party tool / iPaaS / middleware / RPA bot / AI agent / MCP server may call SAP APIs, agentic or generative-AI access to SAP, bulk data extraction or replication into a lake/warehouse, custom Z/Y OData or RFC/BAPI wrappers and Clean Core, ADT/developer-tooling boundaries, ODP-RFC and other "not permitted" interfaces, partner Integration Certification, or RISE integration remediation. Trigger even when the policy is not named, e.g. "are we allowed to…", "is it compliant to…", "can we connect X to SAP…", "will this break under the new API policy". Produces a sourced technical assessment with a confidence level—explicitly NOT legal advice and NOT a final SAP compliance decision.
Static application security testing (SAST) using Semgrep for vulnerability detection, security code review, and secure coding guidance with OWASP and CWE framework mapping. Use when: (1) scanning code for security vulnerabilities across multiple languages, (2) performing security code reviews with pattern-based detection, (3) integrating SAST checks into CI/CD pipelines, (4) providing remediation guidance with OWASP Top 10 and CWE mappings, (5) creating custom security rules for organization-specific patterns, or (6) analyzing dependencies for known vulnerabilities
Build, review, debug, migrate, or plan Schema.org JSON-LD with Google schema-dts TypeScript types. Use for schema-dts, schema-dts-gen, schema-dts-lib, WithContext, Graph, IdReference, MergeLeafTypes, ProductLeaf and other *Leaf types, WithActionConstraints, implements PersonLeaf classes, JSON-LD script injection, structured data, Organization, WebSite, Product, Offer, FAQPage, Article, BreadcrumbList, react-schemaorg, schema-dts-gen 2.0.1 security fix, and Schema.org v30 typing.
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
Use when the user has scientific data (or a prompt alluding to scientific data) and wants a publication-quality figure made from it. A generator drafts and renders a figure that lands a frozen communication goal; an adversarial critic critiques it hard and grades it 1-5 per axis against a fixed rubric (message, aesthetic, clarity, integrity, and a conditional domain-completeness axis), aggregates to 0-100, and decides pass; the generator revises against the critic's findings until the grade clears a threshold or the budget is hit. Both roles may consult the literature (Semantic Scholar + arXiv) to verify domain content (e.g. a pathway figure's gene set, or a benchmark's reported numbers), and conform to / grade against a named journal's figure spec fetched via web search. Not for writing a paper or analyzing a dataset, and not for editing an existing finished image — this renders a figure from data/brief and iterates on it.
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.