
When --autonomous is active, skip user-facing elicitation rounds. The agent makes elicitation decisions from memory, codebase patterns, constitution principles, and prior cycle context. Every decision cites its source. Substantive decisions with no signal fall back to asking the user.
Integrates sage-memory into Sage workflows. Teaches the agent when to remember (store findings during work), when to recall (search memory at session start and task start), and how to learn (structured knowledge capture via sage learn). Use when the user mentions memory, remember, recall, learn, capture knowledge, onboard to codebase, or when starting any session where sage-memory MCP tools are available.
Automatic sub-agent code verification after quality gates pass. Independent context window. Checks spec-implementation alignment, test coverage, error handling, boundary conditions, and integration consistency. 60 seconds, code-only, advisory.
When --quality-locked is active, loop review/revise at each Quality Gate until findings reach a clean bar (no Critical, no Major, only cosmetic Minor) or the iteration cap (10) is reached. Uses a deterministic Python checker for classification and decision logic; agent runs the actual review and revision steps.
Automatic sub-agent review of spec, plan, ADR, root cause diagnosis, and fix plans after user approval. Independent context window. No self-bias. Catches the most expensive mistakes at the cheapest moment — before implementation begins.
Reviews code for quality, security, maintainability, and performance beyond spec compliance. Checks clean code practices, error handling, security vulnerabilities, and convention adherence. Use after implementation, or when the user says "review my code", "quality check", "security review", or "is this code good".
Integrates sage-memory into Sage workflows. Teaches the agent when to remember (store findings during work), when to recall (search memory at session start and task start), and how to learn (structured knowledge capture via sage learn). Use when the user mentions memory, remember, recall, learn, capture knowledge, onboard to codebase, or when starting any session where sage-memory MCP tools are available.
Typed knowledge graph stored in sage-memory. Use when creating or querying structured entities (Person, Project, Task, Event, Document), linking related objects, checking dependencies, planning multi-step actions as graph transformations, or when skills need to share structured state. Trigger on "remember that X is Y", "what do I know about", "link X to Y", "show dependencies", "what blocks X", entity CRUD, cross-skill data access, or any request involving structured relationships between things.
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the sage-memory skill — they share the same MCP backend but serve different purposes (sage-memory = codebase knowledge, sage-self-learning = agent mistakes and gotchas).
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the sage-memory skill — they share the same MCP backend but serve different purposes (sage-memory = codebase knowledge, sage-self-learning = agent mistakes and gotchas).
Typed knowledge graph stored in sage-memory. Use when creating or querying structured entities (Person, Project, Task, Event, Document), linking related objects, checking dependencies, planning multi-step actions as graph transformations, or when skills need to share structured state. Trigger on "remember that X is Y", "what do I know about", "link X to Y", "show dependencies", "what blocks X", entity CRUD, cross-skill data access, or any request involving structured relationships between things.
Parses workflow flags (--quality-locked, --autonomous) from $ARGUMENTS at the start of /build and /architect commands. Uses deterministic runtimes (Python primary, Bash fallback) with prose-rule fallback if neither runtime is available. Returns a strict JSON contract that the agent trusts unconditionally.
Sage process framework — constitution, routing, interaction zones, enforcement rules. Auto-loads to provide process enforcement, keyword routing, and structured interaction patterns for all Sage workflows. This is the always-on layer that ensures quality even when specific workflow skills are not loaded.
Activates when the user starts any substantial task: building, creating, redesigning, analyzing, researching, planning, fixing, improving, evaluating, writing, auditing — code, products, content, or strategy. Also activates when the user asks what to do next, says "continue," seems uncertain where to start, or begins a new session. This is Sage's intelligent process navigator.
Knowledge entries in sage-memory, Docs in .sage/docs/
Drives task-by-task execution from an approved plan with quality gates between each task. Reads the plan, finds the next incomplete task, dispatches implementation, validates, updates progress, and continues. Use after a plan is approved and the user says "go", "start building", "execute the plan", or "implement the feature".
Root cause diagnosis with evidence, Reproducing test, Minimal patch
Structures elicitation output into a formal specification defining WHAT to build and WHY. Use after quick-elicit or deep-elicit completes, or when the user provides requirements and says "write a spec", "define requirements", "create a PRD", or "specify this feature".
Corrects the 13 most common API design mistakes agents make — grounded in Geewax, Amundsen, Ousterhout, Kleppmann, and Gough/Bryant
React Native patterns — New Architecture (Fabric/TurboModules), Expo, navigation, performance
Integration patterns for Next.js + Tailwind CSS + Prisma + Auth.js — the seams between frameworks
UX design process — research, evaluation, specification, design, and review woven into the development workflow.
--- name: "baas" description: "Corrects the most common BaaS architecture mistakes agents make — security rules as authorization, data modeling for queries, minimizing serverless functions, real-time by default, and client-direct access patterns. Applies to Firebase, Supabase, and similar platforms." version: "1.0.0" type: knowledge layer: domain requires: sage: ">=1.0.0" activates-when: detected: [firebase, @firebase/app, @supabase/supabase-js, supabase, firebase-admin, @angular/fire, react
Defines how Sage content is loaded into the agent's context window. This capability guides platform generators — it specifies what to inline, what to reference, and what to skip. Not a runtime skill for agents; a build-time strategy for generators.
Integrates sage-memory into Sage workflows. Teaches the agent when to remember (store findings during work), when to recall (search memory at session start and task start), and how to learn (structured knowledge capture via sage learn). Use when the user mentions memory, remember, recall, learn, capture knowledge, onboard to codebase, or when starting any session where sage-memory MCP tools are available.
Integrates sage-memory into Sage workflows. Teaches the agent when to remember (store findings during work), when to recall (search memory at session start and task start), and how to learn (structured knowledge capture via sage learn). Use when the user mentions memory, remember, recall, learn, capture knowledge, onboard to codebase, or when starting any session where sage-memory MCP tools are available.
Product management process — JTBD analysis, opportunity mapping, user interview design, and PRD writing. Discovery → Planning → Delivery.
React 18/19 patterns and anti-patterns — hooks discipline, component architecture, state management
Produces a Product Requirements Document (PRD) grounded in JTBD outcomes. Takes a JTBD analysis as input and transforms high-opportunity outcomes into structured, prioritized, testable requirements. Use when the user mentions PRD, product requirements, product spec, requirements document, or asks what to build based on a JTBD analysis. Also triggers when the user wants to define scope for an initiative, align a team on what to build, or translate customer needs into product requirements. Do NOT use for technical design documents, project plans, or feature specs that prescribe solutions.
Systematic techniques for breaking through when stuck. Activate when: the agent has tried 3+ approaches without resolution, complexity is spiraling with growing special cases, a test keeps failing after multiple fix attempts, or the solution feels forced with no alternatives considered.
Product management process — JTBD analysis, opportunity mapping, user interview design, and PRD writing. Discovery → Planning → Delivery.
Provides specific, actionable guidance on what to do next based on current project state. Reads progress files and context to give one clear next step. Use when the user says "help", "what do I do", "sage help", "what's next", "I'm stuck", or "status".
Monitors implementation scope and prevents drift beyond the plan. Detects unrequested refactors, gold-plating, and "while I'm here" additions. Use when implementing tasks from a plan, writing code for a feature, or when the agent starts modifying files not listed in the current task.
Build, validate, and publish Sage skills — discover patterns from source material, draft skill files, validate quality.
Adversarial review verifying implementation matches its specification — checks completeness (nothing missing) and precision (nothing extra). Distrusts the implementer's self-report. Use after implementation, or when the user says "check against spec", "does this match requirements", or "verify the implementation".
Integration patterns for Flutter + Firebase + Riverpod — auth, Firestore, Cloud Functions, project structure
Four-phase debugging framework that finds root causes before attempting fixes. Use when investigating errors, debugging failures, fixing bugs, analyzing test failures, diagnosing unexpected behavior, examining stack traces, or when the user says "it's broken", "this doesn't work", or "why is this failing".
Enforces test-driven development: write failing test, write minimal code to pass, refactor. Mandatory for all implementation work. Use when writing any production code, implementing features, fixing bugs, refactoring, or when the user says "write code", "implement", "fix this", or "add a feature". Code written before its test is deleted.
Systematically uncovers customer jobs, pains, and gains using the Jobs-to-be-Done framework. Produces structured JTBD analyses with job performer definitions, job process maps, pains/gains, and desired outcome statements. Use when the user mentions jobs to be done, JTBD, customer jobs, unmet needs, pains and gains, value proposition canvas, switch interviews, outcome-driven innovation, desired outcomes, or asks why customers hire or fire a product. Also triggers when the user wants to understand what job a product solves, conduct customer discovery, reposition a product around needs, define unmet needs for a roadmap, analyze competitors through a jobs lens, or create messaging grounded in customer objectives. Do NOT use for general market sizing, feature prioritization without a customer-needs lens, or persona creation based on demographics alone.
Phase 4 of pack building. Generates pack files from observations and processed sources. Handles both community pack (full structure) and project overlay (overrides.md only) paths.
Systematic techniques for breaking through when stuck. Activate when: the agent has tried 3+ approaches without resolution, complexity is spiraling with growing special cases, a test keeps failing after multiple fix attempts, or the solution feels forced with no alternatives considered.
React Native patterns — New Architecture (Fabric/TurboModules), Expo, navigation, performance
Integration patterns for Flutter + Firebase + Riverpod — auth, Firestore, Cloud Functions, project structure
User research and context gathering — understands who users are, what they do, and why
Phase 1 of pack building. Identifies what pack to create, checks for existing packs, classifies the layer, and forks between community pack and project overlay paths. Triggers on: build a pack, create a pack, customize pack, make a skill pack.
Phase 3 of pack building. Runs test prompts WITHOUT the pack loaded to establish a baseline of agent failures. Records what the agent gets wrong as evidence for patterns and anti-patterns. Community pack path only — overlays skip this phase.
Phase 5 of pack building. Runs automated checks, re-runs test prompts WITH the pack loaded, and measures behavior change against the Phase 3 baseline. Determines if the pack earns its context tokens.
Produces a design brief from the evaluation that feeds directly into the specification and planning skills. Translates MUST keep / MAY change / SHOULD improve classifications into concrete design directions with user-confirmed decisions. Use after ux-evaluate when the user has confirmed the classifications, or when the user says "create the design brief", "what should the redesign look like", or "write the design direction".
Enriches feature specifications with UX requirements — error states, user flows, accessibility, five-planes analysis
Produces UX writing deliverables: voice and tone guides, microcopy for specific features, and content audits of existing product copy. Use when the user needs to define a product's voice, write interface copy for a feature, audit existing microcopy quality, or establish UX writing guidelines. Also triggers when the user says "write the button labels," "what should the error message say," "create a voice and tone guide," "audit the product copy," "write microcopy for this flow," or "the copy doesn't sound right." Do NOT use for marketing copy, blog posts, landing page content, or email campaigns — those are marketing writing, not UX writing.
Configure Sage preset and project settings. Switch between base, startup, enterprise, or opensource constitution presets. Use when the user says "configure sage", "change preset", or "sage settings".
Phase 1 of pack building. Identifies what pack to create, checks for existing packs, classifies the layer, and forks between community pack and project overlay paths. Triggers on: build a pack, create a pack, customize pack, make a skill pack.
Phase 3 of pack building. Runs test prompts WITHOUT the pack loaded to establish a baseline of agent failures. Records what the agent gets wrong as evidence for patterns and anti-patterns. Community pack path only — overlays skip this phase.
Phase 2 of pack building. Gathers sources (docs, blogs, issues, user context) and filters them through the judgment-not-knowledge lens. Extracts only information that corrects agent mistakes — discards documentation summaries.
Phase 2 of pack building. Gathers sources (docs, blogs, issues, user context) and filters them through the judgment-not-knowledge lens. Extracts only information that corrects agent mistakes — discards documentation summaries.
Project state summary computed from artifacts
UX design process — research, evaluation, specification, design, and review woven into the development workflow.
Audit findings, Evaluation report, Severity scores
Corrects the 13 most common API design mistakes agents make — grounded in Geewax, Amundsen, Ousterhout, Kleppmann, and Gough/Bryant
Seven universal coding principles applied during implementation. Language-agnostic quality standards that shape every line of code as it's written. Loaded by build-loop before each task. Not a review checklist — a mindset active during implementation.
Architecture Decision Records, System spec, Milestone plan
Autonomous iteration toward a measurable outcome. Use when the user wants to optimize a numeric metric through repeated modify-verify cycles — reduce bundle size, increase test coverage, improve query time, lower readability score. Not for exploratory research, subjective judgment, or tasks without a verification command.
--- name: "baas" description: "Corrects the most common BaaS architecture mistakes agents make — security rules as authorization, data modeling for queries, minimizing serverless functions, real-time by default, and client-direct access patterns. Applies to Firebase, Supabase, and similar platforms." version: "1.0.0" type: knowledge layer: domain requires: sage: ">=1.0.0" activates-when: detected: [firebase, @firebase/app, @supabase/supabase-js, supabase, firebase-admin, @angular/fire, react
Quick browser smoke test via Lightpanda MCP. Checks if the primary affected route renders, has no JS errors, and contains expected elements. Advisory only — never blocks. Invisible when Lightpanda is not available or no frontend files in diff.
Comprehensive requirements elicitation through Socratic conversation, producing a product brief with problem, users, scope, and success criteria in ~10 minutes. Use when starting a new product, designing a system from scratch, planning a major redesign, or when the user says "I want to build", "new project", or "help me plan this product".
Session resumption with context
UX brief, Feature spec, Content/copy, PRD
Quick design quality scan of changed frontend files. Checks for hardcoded colors (when design system exists), missing interactive states, and AI slop indicators. Code-only — does not use Lightpanda. Advisory only — never blocks. Invisible when no frontend files in diff.
Design review report with findings, severity, and fix/manual classification
Flutter patterns — widget architecture, state management, Impeller renderer, platform-adaptive design
Implements a single task from the plan using TDD discipline. Reads the task spec, writes tests first, writes minimal passing code, refactors, and commits. Use when executing a planned task, writing code for a feature, or when the user says "implement this", "write the code", "build this task", or "execute the plan".
Typed knowledge graph stored in sage-memory. Use when creating or querying structured entities (Person, Project, Task, Event, Document), linking related objects, checking dependencies, planning multi-step actions as graph transformations, or when skills need to share structured state. Trigger on "remember that X is Y", "what do I know about", "link X to Y", "show dependencies", "what blocks X", entity CRUD, cross-skill data access, or any request involving structured relationships between things. Also trigger when the memory skill is active and the agent needs typed structure beyond flat prose.
Universal mobile development principles — offline-first, 60fps, touch, battery, platform patterns
Next.js 14/15 App Router patterns — server components, data fetching, caching, server actions
Next.js 14/15 App Router patterns — server components, data fetching, caching, server actions
QA report with bugs, severity, and fix classification
Universal mobile development principles — offline-first, 60fps, touch, battery, platform patterns
React 18/19 patterns and anti-patterns — hooks discipline, component architecture, state management
Cycle review, Learnings with prevention rules, Next-cycle seeds
Research findings, Need analysis, Opportunity map
Independent artifact review via sub-agent delegation. Evaluates completeness, consistency, and quality with severity classification.
Start here. Sage reads project state, routes via keywords, classifies intent, and guides you to the right workflow.
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the memory skill — they share sage-memory but serve different purposes (memory = codebase knowledge, self-learning = agent mistakes and gotchas).
Captures agent mistakes, corrections, and discovered gotchas so they are not repeated. Use when: (1) a command or operation fails unexpectedly, (2) the user corrects the agent, (3) the agent discovers non-obvious behavior through debugging, (4) an API or tool behaves differently than expected, (5) a better approach is found for a recurring task. Also searches past learnings before starting tasks to avoid known pitfalls. Activate alongside the memory skill — they share sage-memory but serve different purposes (memory = codebase knowledge, self-learning = agent mistakes and gotchas). Also trigger on "sage review" or "review learnings" to curate and improve the learning database.
Preserves and restores context across agent sessions using plan file checkboxes as source of truth. Use when starting a new session, resuming previous work, ending a session, or when the user says "continue from last time", "what was I doing", or "save progress".
Build, validate, and publish Sage skills — discover patterns from source material, draft skill files, validate quality.
--- name: "stack-nextjs-supabase" description: "Integration seams for Next.js App Router + Supabase: dual-client auth (browser/server), middleware token refresh, RLS policies, typed queries, and real-time subscriptions" version: "1.0.0" type: composite layer: stack requires: sage: ">=1.0.0" skills: - "web" - "baas" - "nextjs" activates-when: detected: [next, @supabase/supabase-js, @supabase/ssr] tags: [next,@supabase/supabase-js,@supabase/ssr] --- # stack-nextjs-supabase **La
--- name: "stack-nextjs-supabase" description: "Integration seams for Next.js App Router + Supabase: dual-client auth (browser/server), middleware token refresh, RLS policies, typed queries, and real-time subscriptions" version: "1.0.0" type: composite layer: stack requires: sage: ">=1.0.0" skills: - "web" - "baas" - "nextjs" activates-when: detected: [next, @supabase/supabase-js, @supabase/ssr] tags: [next,@supabase/supabase-js,@supabase/ssr] --- # stack-nextjs-supabase **La
Integration patterns for Expo + React Navigation + Zustand + MMKV + TanStack Query
Integration patterns for Expo + React Navigation + Zustand + MMKV + TanStack Query
Designs complete user interview research packages: research brief, screener, interview guide, and analysis framework. Supports discovery interviews, switch interviews, contextual inquiry, and evaluative interviews. Use when the user needs to validate JTBD hypotheses, test a concept with users, understand switching behavior, or observe how users interact with a product. Also triggers when the user says "I need to talk to users," "help me plan user interviews," "write an interview guide," "I need to validate this assumption," or "design a research study." Do NOT use for quantitative research (surveys, A/B tests) or for conducting the research itself.
Designs complete user interview research packages: research brief, screener, interview guide, and analysis framework. Supports discovery interviews, switch interviews, contextual inquiry, and evaluative interviews. Use when the user needs to validate JTBD hypotheses, test a concept with users, understand switching behavior, or observe how users interact with a product. Also triggers when the user says "I need to talk to users," "help me plan user interviews," "write an interview guide," "I need to validate this assumption," or "design a research study." Do NOT use for quantitative research (surveys, A/B tests) or for conducting the research itself.
Reverse-engineers the current design system from screenshots or a live URL. Extracts colors, typography, spacing, component patterns, and layout structure. Use when redesigning an existing page, auditing a current design, or when the user says "audit this design", "what's the current design system", "analyze this page", or provides a URL and says "redesign".
Reverse-engineers the current design system from screenshots or a live URL. Extracts colors, typography, spacing, component patterns, and layout structure. Use when redesigning an existing page, auditing a current design, or when the user says "audit this design", "what's the current design system", "analyze this page", or provides a URL and says "redesign".
Produces a design brief from the evaluation that feeds directly into the specification and planning skills. Translates MUST keep / MAY change / SHOULD improve classifications into concrete design directions with user-confirmed decisions. Use after ux-evaluate when the user has confirmed the classifications, or when the user says "create the design brief", "what should the redesign look like", or "write the design direction".
User research and context gathering — understands who users are, what they do, and why
Compares current design system against category benchmarks to produce a structured gap analysis. Classifies every design element as MUST keep (brand identity), SHOULD keep (working patterns), MAY change (style updates), or SHOULD improve (gaps vs. category). Use after ux-audit and ux-research complete, or when the user says "evaluate this design", "what should we change", "gap analysis", or "compare against competitors".
Evaluates implementation against usability heuristics — Nielsen's 10, Norman's principles, Krug's laws
Evaluates implementation against usability heuristics — Nielsen's 10, Norman's principles, Krug's laws
Adds UX-specific tasks to the development plan — usability testing, review checkpoints, design validation
Adds UX-specific tasks to the development plan — usability testing, review checkpoints, design validation
Researches design patterns and best practices from category leaders for a specific product type. Analyzes competitor homepages, landing pages, or app screens to identify industry conventions and opportunities for differentiation. Use when redesigning and the user says "research competitors", "what do others do", "best practices for this type of page", or during a redesign task where category context would improve decisions.
Researches design patterns and best practices from category leaders for a specific product type. Analyzes competitor homepages, landing pages, or app screens to identify industry conventions and opportunities for differentiation. Use when redesigning and the user says "research competitors", "what do others do", "best practices for this type of page", or during a redesign task where category context would improve decisions.
Enriches feature specifications with UX requirements — error states, user flows, accessibility, five-planes analysis
Produces UX writing deliverables: voice and tone guides, microcopy for specific features, and content audits of existing product copy. Use when the user needs to define a product's voice, write interface copy for a feature, audit existing microcopy quality, or establish UX writing guidelines. Also triggers when the user says "write the button labels," "what should the error message say," "create a voice and tone guide," "audit the product copy," "write microcopy for this flow," or "the copy doesn't sound right." Do NOT use for marketing copy, blog posts, landing page content, or email campaigns — those are marketing writing, not UX writing.
Evaluates UI implementation by analyzing screenshots at multiple breakpoints against the spec, design conventions, and web standards. Checks layout, hierarchy, responsiveness, spacing, and accessibility. Use after implementing UI components, after a redesign, when the user says "how does it look", "check the design", "visual review", or "review the UI".
Universal web development principles — accessibility (WCAG 2.2), performance (Core Web Vitals), security headers (OWASP), SEO, responsive design, error UX, and loading states. Framework-agnostic.
Universal web development principles — accessibility (WCAG 2.2), performance (Core Web Vitals), security headers (OWASP), SEO, responsive design, error UX, and loading states. Framework-agnostic.
Guided specification through 4 focused question rounds in ~3 minutes. Round 0 challenges problem framing before solutioning. Produces framing, intent, boundaries, and acceptance criteria. Use when the user wants to add a feature, build something new, implement functionality, create a component, or says "add", "build", "create", "implement". Do not use when a detailed spec already exists.
Typed knowledge graph stored in sage-memory. Use when creating or querying structured entities (Person, Project, Task, Event, Document), linking related objects, checking dependencies, planning multi-step actions as graph transformations, or when skills need to share structured state. Trigger on "remember that X is Y", "what do I know about", "link X to Y", "show dependencies", "what blocks X", entity CRUD, cross-skill data access, or any request involving structured relationships between things. Also trigger when the memory skill is active and the agent needs typed structure beyond flat prose.
Produces an opportunity map that assesses discovered customer needs against product capabilities, determines which to pursue, and sequences them. Takes any discovery output (JTBD analysis, research findings, lean canvas) as input and applies inside-out assessment to produce pursue/monitor/defer decisions. Use when the user asks what to focus on, what to build next, which opportunities to prioritize, or how to sequence product work. Also triggers when the user says "help me decide what to pursue" or "we have too many opportunities, help us focus." Do NOT use for detailed requirements (that's PRD) or for understanding customer needs (that's discovery).
Integration patterns for Next.js + Tailwind CSS + Prisma + Auth.js — the seams between frameworks
Produces a Product Requirements Document (PRD) grounded in JTBD outcomes. Takes a JTBD analysis as input and transforms high-opportunity outcomes into structured, prioritized, testable requirements. Use when the user mentions PRD, product requirements, product spec, requirements document, or asks what to build based on a JTBD analysis. Also triggers when the user wants to define scope for an initiative, align a team on what to build, or translate customer needs into product requirements. Do NOT use for technical design documents, project plans, or feature specs that prescribe solutions.
Phase 5 of pack building. Runs automated checks, re-runs test prompts WITH the pack loaded, and measures behavior change against the Phase 3 baseline. Determines if the pack earns its context tokens.
Creates implementation plans from specifications by breaking work into small tasks (2-5 min each) with exact file paths, tests, and dependency ordering. Use after a spec is approved and before implementation begins, or when the user says "create a plan", "break this down", "how should we build this", or "task breakdown".
Brief (medium+ tasks), Spec, Implementation plan
Compares current design system against category benchmarks to produce a structured gap analysis. Classifies every design element as MUST keep (brand identity), SHOULD keep (working patterns), MAY change (style updates), or SHOULD improve (gaps vs. category). Use after ux-audit and ux-research complete, or when the user says "evaluate this design", "what should we change", "gap analysis", or "compare against competitors".
Produces an opportunity map that assesses discovered customer needs against product capabilities, determines which to pursue, and sequences them. Takes any discovery output (JTBD analysis, research findings, lean canvas) as input and applies inside-out assessment to produce pursue/monitor/defer decisions. Use when the user asks what to focus on, what to build next, which opportunities to prioritize, or how to sequence product work. Also triggers when the user says "help me decide what to pursue" or "we have too many opportunities, help us focus." Do NOT use for detailed requirements (that's PRD) or for understanding customer needs (that's discovery).
Phase 4 of pack building. Generates pack files from observations and processed sources. Handles both community pack (full structure) and project overlay (overrides.md only) paths.
Systematically uncovers customer jobs, pains, and gains using the Jobs-to-be-Done framework. Produces structured JTBD analyses with job performer definitions, job process maps, pains/gains, and desired outcome statements. Use when the user mentions jobs to be done, JTBD, customer jobs, unmet needs, pains and gains, value proposition canvas, switch interviews, outcome-driven innovation, desired outcomes, or asks why customers hire or fire a product. Also triggers when the user wants to understand what job a product solves, conduct customer discovery, reposition a product around needs, define unmet needs for a roadmap, analyze competitors through a jobs lens, or create messaging grounded in customer objectives. Do NOT use for general market sizing, feature prioritization without a customer-needs lens, or persona creation based on demographics alone.
Flutter patterns — widget architecture, state management, Impeller renderer, platform-adaptive design
First-run project setup that detects tech stack, selects quality packs, and generates .sage/ directory with CLAUDE.md. For new projects, guides technology selection. Use when no .sage/ directory exists, when the user says "set up sage", "initialize", "get started", or when starting a brand new project from scratch.
Scans relevant codebase areas to understand existing patterns, conventions, dependencies, and architecture before making changes. Use at the start of any feature or fix, when entering an unfamiliar codebase area, or when the user says "scan the project", "what patterns are used", or "analyze the codebase".
Verifies implementations and fixes actually work by executing commands and observing real results. Never trusts claims. Use as the final quality check after implementation, after a bug fix, or when the user asks "is it done", "does it work", "verify this", or "confirm the fix".
Guides tool selection across three layers: local scripts (zero context cost), MCP proxy (minimal context), and subagent dispatch (isolated context). Ensures the agent uses the cheapest effective layer and never pollutes the main context window with raw tool output. Use when the agent needs external information, current documentation, database access, or multi-step research.