code-review
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code-plan
P-phase skill for the codesmith implementation workflow. Converts the structural skeleton into per-slice tactical steps (TDD vertical: failing test → minimal impl → green → verify → commit) per Lock 8. Produces one plan doc per plan identified in D-phase. Runs the Parallel-Safety Evaluation per Lock 27 to determine whether multiple plans execute in parallel worktrees or serialize. Spot-check user review; no open questions in the final plan. Hands off to worktree-setup (or directly to code-implement if Work-Tree is bypassed).
code-orient
Entry-point skill for the codesmith implementation workflow. Detects dual mode (planning vs execution) from initial input shape — ticket reference → planning mode; bundle directory → execution mode. In planning mode: loads the `status: ready` ticket, scaffolds the implementation bundle via scaffold-implementation-bundle.py, transitions ticket status `ready → active`, and hands off to code-grill. In execution mode: loads the existing bundle (ticket + design + structure + plans + optional research), confirms preconditions, and hands off to code-implement. Detects PR mode vs local-only mode based on remote configuration and user confirmation.
code-implement
I-phase orchestration skill per Lock 10. Iterates over the locked plan's slices in dependency order. For each slice: invokes worktree-setup (first slice only or once per parallel plan); dispatches `codesmith-worker` subagent with slice scope (plan + slice boundary + verification commands + commit convention + worktree path); awaits PHASE_COMPLETE or NEEDS_DECOMPOSITION; on decomposition triggers mini-replan per Lock 19; on completion invokes `code-verify` for the I-per-slice gate. Owns the plan-as-living-doc's structural sections; worker owns the per-slice log subsections. Tracks decomposition strike count (abort after 3 consecutive on the same slice per Lock 17.2).
code-grill
Q↔R loop skill for the codesmith implementation workflow. Applies Kipling structure (What/How/Who/When/Why/Where) × grill-with-docs discipline × per-tag emphasis matrix to derive code-layer questions. Composes research dispatch instructions (in-memory, NOT a written artifact) per the 7 composable research patterns. Dispatches `researcher` across the hide-the-intent boundary — researcher receives questions + scope only, not the ticket. Synthesizes returns into the bundle's research file. Updates CONTEXT.md inline as terms surface. Scaffolds Decision Records when the three-condition gate fires. Loops until five readiness criteria are met (problem scoped, current state mapped, impact understood, edge cases identified, agent can articulate unprompted). Hands off to code-design when readiness is confirmed.
code-design
D-phase skill for the codesmith implementation workflow. Invokes render-design-doc-skeleton.py to overwrite the design.md placeholder with a tag-aware skeleton grounded in R-phase findings. Runs the always-interactive brain-surgery alignment conversation per Lock 6: agent presents each design section, human stress-tests via Question tool per substantive decision, iterate until aligned. Tracks the foundational-reversal strike counter per Lock 31. May identify multiple architecturally-distinct plans (NOT sub-tickets). Hands off to code-structure when D-phase is locked.
ticket-create
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brand-palette
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qbo
Queries, creates, updates, and manages QuickBooks Online data via the qbo CLI. Use when working with QBO entities (invoices, customers, bills, payments, vendors, accounts, items, estimates), running reports, or setting up install, auth, sandbox, and company switching.
agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
pr-review
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ticket-review
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research-scout-extern
Scout an external open-source codebase for patterns and implementations, and persist the findings to a global, cross-project research catalog. Catalog-first: checks what has already been studied before cloning, so a repeat question reuses prior research instead of re-cloning. When a clone is needed, sets up a disposable .extern/ workspace, shallow-clones the repo, studies it with the read-only codebase children (pattern-finder, analyzer, locator) focused on the study topic, writes a research document to the global extern store, and records the study in the catalog via script. Clones are disposable; research is permanent. Studies code, not the web — invoking the skill is the approval to clone. Mode-aware: interactive confirms cleanup of the workspace; autonomous leaves the clone in place. The right capability for 'study how vercel/next.js implements middleware', or as the deep-study follow-up when Precedent Reconnaissance flags an external project worth reading in depth.
skill-creator
Guides creation of dubstack skill source files (skill.yaml + head.md + skill-overview.md + step files) through discovery, type selection, and section-by-section configuration. Two critical decisions shape every skill: TYPE (workflow / exec / data) and SCRIPTS (script-first mentality — if anything CAN be a script, it probably SHOULD be). Used when creating a new skill for an agent in dubstack.
research-risk
Identify what could go wrong with a proposed approach before it bites — an adversarial reconnaissance across the risk categories that apply (security, performance, regulatory, operational, correctness, cost). Selects the relevant risk lenses for the approach, scans each against the approach's own description, our internal exposure (codebase), prior incidents (thoughts archive), and known external risks (web), then builds a risk register: each risk with an evidence-grounded likelihood and impact, and the standard mitigation for its class. Adversarially skeptical — an approach that looks clean still gets its residual and low risks surfaced rather than declared all-clear. States risks and their known mitigations factually; whether and how to remediate is the orchestrator's call. Persists the register as a planning input. Mode-aware. The right capability for 'what could go wrong with this rollout?', 'security risks of this auth design', 'where does this approach break under load?'.
ticket-prototype
Owns the prototyping sub-activity of Q↔R per QRDSPIV. Invoked from ticket-grill (and any future grill-adopting agent) when the gap being explored requires running throwaway code to answer it. Two branches: LOGIC (terminal/minimal runnable for state or business-logic questions) and UI (visual variants for layout/flow/interaction; uses the project's design system if one exists). Prototype code lives at {shared_folder}/tickets/{slug}/prototypes/{name}/; findings are returned to the caller for inclusion in the bundle's research file. Per QRDSPIV: throwaway from day one, one command to run, no persistence by default, skip polish, surface state.
ticket-design
D-phase skill for the ticket-writing workflow. Runs brain-surgery alignment with the user on the WORK to be done (not the technical solution — at the ticket-writing layer, D designs the work, not the implementation). Loads the Q↔R-ready bundle, drafts a design proposal (artifact chain + decision points + risk), aligns with user via firm checkpoint, handles redirects with the three-strike-foundational-reversal escalation counter from Lock 31, and writes the Design Summary into the ticket body. Hands off to ticket-structure.
ticket-grill
Q↔R loop skill for the ticket-writing workflow. Runs iterative grilling against an oriented ticket bundle until the five readiness criteria from QRDSPIV are met. Mode-shifts between brainstorming, research, elicitation, and prototyping. Dispatches research subagents in parallel per the question-shape→subagent table. Updates CONTEXT.md inline via the context-md-format skill. Writes Decision Records via the decision-record-format skill when the three-condition gate fires. Loops back to itself until ready; hands off to ticket-design when readiness is confirmed.
ticket-orient
Entry-point skill for the ticket-writing workflow. Runs the 7-step orient flow from spec §5: collects thoughts metadata, reads CONTEXT.md, performs tier-1 codebase orientation, detects the candidate primary tag from the user request, confirms scope with the user at a firm checkpoint, scaffolds the ticket bundle directory via scaffold-bundle.py, and hands off to ticket-grill for the Q↔R loop. Use as the first phase of every ticket-writing session before any grilling begins.
ticket-plan
P-phase skill for the ticket-writing workflow. Plans HOW to fill the ticket sections defined by ticket-structure: writing order, per-section subagent dispatch (for sections requiring fresh research), review checkpoints (where to pause for user review during writing — especially Part boundaries for heavy tickets). Reads the Structure Outline; produces a Writing Plan section in the ticket body that ticket-write consumes. P is tactical: sequence, dispatch, and pause points — not content.
ticket-structure
S-phase skill for the ticket-writing workflow. Designs the TICKET ITSELF — sections, numbered Parts for heavy tickets, custom outline for unusual ones. Tag recipe drives the default shape (when Phase 4 references are in place); custom outline applies when the recipe doesn't fit. Reads the Design Summary from ticket-design; produces a Structure Outline section in the ticket body that ticket-plan and ticket-write will consume. Per QRDSPIV §S: 'scope, don't prescribe' — the outline names sections, it doesn't fill them.
renovate-review
Review Renovate dependency update PRs for breaking changes and compatibility
agentmail-mcp
AgentMail MCP server for email tools in AI assistants. Use when setting up AgentMail with Claude Desktop, Cursor, VS Code, Windsurf, or other MCP-compatible clients. Provides tools for inbox management, sending/receiving emails, and thread handling.
agentmail-toolkit
Add email capabilities to AI agents using popular frameworks. Provides pre-built tools for TypeScript and Python frameworks including Vercel AI SDK, LangChain, Clawdbot, OpenAI Agents SDK, and LiveKit Agents. Use when integrating AgentMail with agent frameworks that need email send/receive tools.
agentmail
Give AI agents their own email inboxes using the AgentMail API. Use when building email agents, sending/receiving emails programmatically, managing inboxes, handling attachments, organizing with labels, creating drafts for human approval, or setting up real-time notifications via webhooks/websockets. Supports multi-tenant isolation with pods.
agentmail-cli
Send and receive emails programmatically using the AgentMail CLI. Use when agents need to manage inboxes, send/receive emails, handle threads, drafts, webhooks, and domains via command line.
agentmail-sdk
Comprehensive guide to the AgentMail Python and TypeScript SDKs. Use when building AI agents that need their own email inboxes, sending or receiving emails programmatically, managing threads and conversations, handling attachments, creating drafts for human-in-the-loop approval, setting up real-time notifications via webhooks or WebSockets, configuring custom domains, managing allow/block lists, using pods for multi-tenant isolation, or integrating email into any AI agent workflow. Covers the full AgentMail API with code examples, best practices, and production patterns.
agent-email-patterns
Architecture patterns and best practices for giving AI agents email capabilities. Use when designing how agents send, receive, and manage email conversations, building two-way communication loops, implementing human-in-the-loop approval with drafts, choosing between WebSockets and webhooks, setting up multi-agent email topologies, handling OTP and verification flows, or securing agent email against prompt injection.
email-for-ai-agents
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
parallel
High-accuracy web research platform with 7 APIs - Search, Extract, Task (Deep Research), Chat, FindAll, Monitor, and Task Groups. Fast mode, 8 processor tiers, MCP tool calling, authenticated browsing, SSE streaming, and OpenAI-compatible chat. OpenClaw skill.
competitor-discovery
Find a startup's REAL competitors — the alternatives customers actually compare them to, not just the names on the pitch deck. Use when a founder asks "who are my real competitors?", asks "what does the choice landscape look like for X?", wants to validate or expand a pitch-deck competitor slide, or needs to map alternatives before pain mining or positioning work. Combines Anysite MCP (LinkedIn company/search, LinkedIn posts `mentioned[]` extraction + aggregation, Reddit, YouTube video search + subtitles + comments, Twitter search, YC, SEC for public incumbents, DuckDuckGo as Exa fallback) and Exa MCP (semantic web search + JS-page fetch) to surface direct competitors, substitutes, "doing nothing" workarounds, and convergence threats. Supports two modes: `top5` (5 anchor names) and `landscape` (5 anchors + 25–70-name long-tail + suggested strategic-group axes). Run this before customer-pain-mining or positioning-map.
skill-audit
Read-only static security audit of Claude Code skills, commands, and plugins. Analyzes SKILL.md frontmatter, body content, supporting scripts, and hooks for security risks. Use this skill when the user asks to "audit a skill", "review skill security", "check SKILL.md for risks", "scan a plugin for dangerous patterns", "verify skill safety", "check skill permissions", "analyze skill hooks", "audit a skill from GitHub", "review a remote skill", "check a skill by URL", or needs a security assessment of any Claude Code skill, command, or plugin before enabling it.
customer-pain-mining
Extract verbatim customer complaints about competitors — the exact wording the founder's product copy should steal, in customers' own words. Use when a founder asks "what do users hate about X?", "what's broken about [category]?", "what's the white space?", or needs raw customer language for landing-page copy, custdev prep, ad copy, or product strategy. Combines Anysite MCP (Reddit broad sweeps, LinkedIn issue-level pain search, YouTube comments under review videos, Twitter for viral pain quotes) with Exa MCP (semantic search for review blog posts, comparison articles, "why I left X" Medium posts). Returns 3–5 pain clusters with 2–3 verbatim quotes each plus a white-space section listing features customers ask for that no competitor ships. Run competitor-discovery first if there's no validated competitor list.
positioning-map
Build a positioning map for 3–5 competitors and identify the empty quadrant the founder could own. Use when a founder asks "where's the positioning gap?", "how do I position against X?", "what's the competitive landscape look like on hero / pricing / hiring / customers?", or needs a structured comparison before a launch, repositioning, or fundraise. Combines Anysite MCP (LinkedIn company entity + post search + jobs search; SEC for late-stage) with Exa MCP (fetch JS-heavy SaaS marketing pages, find case studies and changelogs). Returns a comparison table across 5 axes (hero / pricing / specialities / recent shipping / hiring), 3 candidate positioning moves with explicit choice criteria, and a one-sentence positioning statement. Requires customer pain themes as input — positioning without pain context is just rearranging marketing copy. Run competitor-discovery and customer-pain-mining first if you don't have a curated competitor list + pain themes.
anysite-lead-generation
Lead generation and prospecting using anysite MCP server for LinkedIn prospect discovery, email finding, company research, and contact enrichment. Extract contacts from websites, find decision-makers at target companies, and build qualified prospect lists for sales, recruiting, and business development. Supports LinkedIn (primary), web scraping for contact extraction, and Instagram business discovery. Use when users need to build prospect lists, find decision-makers, extract contact information, research potential customers, or enrich existing leads with additional data.
anysite-market-research
Conduct comprehensive market research using Y Combinator data, SEC filings, social media insights, and web scraping via anysite MCP server. Analyze tech markets, research startup ecosystems, study public companies, identify market opportunities, and understand competitive dynamics. Supports startup discovery, industry analysis, public company research, and social sentiment analysis. Use when users need to analyze market opportunities, research industries, evaluate startups, study public companies, or gather market intelligence for strategic planning and investment decisions.
anysite-brand-reputation
Monitor brand reputation and sentiment across Twitter/X, Reddit, Instagram, YouTube, and LinkedIn using anysite MCP server. Track brand mentions, analyze customer sentiment, monitor social conversations, identify reputation issues, and measure brand health. Supports social media listening, sentiment analysis, mention tracking, and crisis detection. Use when users need to monitor brand mentions, track customer sentiment, identify reputation risks, analyze brand perception, or measure social media presence and brand health across platforms.
anysite-trend-analysis
Discover and track emerging trends across Twitter/X, Reddit, YouTube, LinkedIn, and Instagram using anysite MCP server. Identify viral content, monitor topic momentum, detect trending hashtags, analyze search patterns, and track industry shifts. Supports multi-platform trend detection, sentiment analysis, and momentum tracking. Use when users need to identify emerging trends, track viral content, monitor market shifts, discover trending topics, or analyze social media conversations for strategic insights.
anysite-mcp-migration
Migrate anysite MCP skills, prompts, and agent instructions from v1 (individual tools like search_linkedin_users, get_linkedin_profile) to v2 (universal meta-tools execute, discover, get_page, query_cache, export_data). Automatically rewrites tool references, adds pagination/filtering/export capabilities, and validates migrated output. Use when users need to update existing skills or prompts for the new anysite MCP v2 API, convert old tool calls to execute() format, or adapt workflows to use new v2 features like server-side filtering and data export.
anysite-person-analyzer
Deep multi-platform intelligence analysis combining LinkedIn (profile, posts, activity), Twitter/X (tweets, engagement), Reddit (discussions, community), web presence (articles, GitHub, blogs), and company intelligence. Use when analyzing people for networking, sales, partnerships, or recruitment. Accepts LinkedIn URL or name+context. Produces comprehensive cross-platform reports with conversation strategies and strategic value assessment for AnySite.
anysite-influencer-discovery
Discover and analyze influencers across Instagram, Twitter/X, LinkedIn, YouTube, and Reddit using anysite MCP server. Find content creators by niche, analyze engagement metrics, evaluate audience quality, track influencer activity, and identify partnership opportunities. Supports multi-platform influencer search, profile enrichment, follower analysis, and engagement tracking. Use when users need to find brand ambassadors, research content creators, identify thought leaders, build influencer lists, or evaluate influencer partnerships for marketing campaigns.
anysite-audience-analysis
Analyze audience demographics, engagement patterns, and follower behavior across Instagram, YouTube, and LinkedIn using anysite MCP server. Understand who engages with content, track audience growth, analyze follower quality, identify engagement patterns, and profile audience characteristics. Supports Instagram audience analysis, YouTube subscriber research, and LinkedIn connection profiling. Use when users need to understand target audiences, validate influencer audiences, analyze follower demographics, track engagement patterns, or optimize content for specific audience segments.
anysite-vc-analyst
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anysite-cli
Operate the anysite command-line tool for web data extraction, batch API processing, multi-source dataset pipelines with scheduling/transforms/exports, database operations, and LLM-powered data analysis. Use when users ask to collect data from LinkedIn, Instagram, Twitter, or any web source via CLI; create or run dataset pipelines; schedule automated collection; batch-process API calls; query collected data with SQL; load data into PostgreSQL or SQLite; analyze data with LLM (summarize, classify, enrich, match, deduplicate); or work with anysite commands. Triggers on anysite CLI usage, data collection, dataset creation, scraping, API batch calls, scheduling, database loading, or LLM analysis tasks.
anysite-competitor-analyzer
Deep competitive intelligence combining web scraping, LinkedIn data, social media monitoring, leadership analysis, GitHub activity, Glassdoor sentiment, and community insights. Analyzes founders/C-level profiles, tracks real-time signals vs quarterly reports, and creates comprehensive competitor profiles. Use when asked to analyze competitors, research leadership teams, investigate market positioning, compare products/pricing, assess strategic threats, or gather intelligence on founders and key executives.
anysite-competitor-intelligence
Competitive intelligence gathering using anysite MCP server across LinkedIn, social media, Y Combinator, and the web. Track competitor activities, analyze hiring patterns, monitor content strategies, benchmark market positioning, research startup competitors, and gather strategic intelligence. Supports LinkedIn (companies, employees, posts), Instagram, Twitter/X, Reddit, YouTube, Y Combinator, and web scraping. Use when users need to analyze competitors, track competitive movements, research market positioning, monitor hiring velocity, or gather strategic market intelligence.
anysite-content-analytics
Track and analyze content performance across Instagram, YouTube, LinkedIn, Twitter/X, and Reddit using anysite MCP server. Measure engagement metrics, analyze post effectiveness, benchmark content strategy, identify top-performing content, and optimize posting strategies. Use when users need to measure content ROI, optimize social strategy, identify viral content patterns, or analyze content engagement across platforms.
asc-metadata-sync
Sync and validate App Store metadata and localizations with asc, including Fastlane format migration. Use when updating metadata or translations.
asc-ppp-pricing
Set territory-specific pricing for subscriptions and in-app purchases using purchasing power parity (PPP). Use when adjusting prices by country or implementing localized pricing strategies.
asc-shots-pipeline
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Go-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
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