agent-skills

Running autonomous loops for nexus-autoloop. Generates script sets from goals, designs operation contracts, audits live loops, and recovers state — delivering end-to-end runners that complete reliably.

Navigating project delivery status as a PdM-style read-only navigator. Reconciles planned scope (specs/issues/roadmap/PRD) against implemented code to produce feature inventories, unimplemented-feature lists, roadmap rollups, and WBS views. Use for "what's built / what's left / where are we". Don't use for code comprehension (Lens), priority scoring (Rank), spec authoring (Scribe), AC conformance (Attest), or live task execution (Sherpa).

Orchestrating specialist AI agent teams as a meta-coordinator. Decomposes requests into minimum viable chains, spawns each as an independent session in AUTORUN modes, and drives to final output. Use when a task spans multiple specialist domains, requires parallel agent execution, or needs hub-and-spoke routing across the skill ecosystem.

Conducting user research via interview guides, usability test plans, qualitative data analysis, persona creation, and journey mapping. Complements Echo's UI validation. Use when user research design or analysis is needed.

Refracting thinking by challenging assumptions, combining cross-domain knowledge, and shifting perspectives to reframe problems. Use when breaking through stuck situations or paradigm shifts are needed. Does not write code.

Extracting and structuring design context from Figma via MCP Server for downstream implementation agents. Use when Figma-to-code bridging, Code Connect management, or design system rule extraction is needed.

Designing LLM-optimized folder structures. Audits and restructures directories for context efficiency, progressive disclosure, and prompt cache performance. Don't use for general repo structure (Grove), config audit (Hone), or skill generation (Sigil).

Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

Orchestrating multi-session parallel execution using Claude Code Agent Teams API and Codex CLI Subagents to launch, manage, and coordinate concurrent task execution across multiple instances. Use when parallel work is needed.

Delivering working code fastest via build-first product delivery. Routes through minimum agent chains scoped to the task. S/M scopes skip planning and build immediately. Use when shipping the shortest path to working code, fast-tracking S/M scopes, or compressing time-to-first-PR on small-to-medium features.

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. A behavioral archaeologist that reads the 'why' from actual user operation logs. Collaborates with Field/Echo for persona validation.

Directing UI/UX creative work — complete redesigns, new designs, and trend application. Use when design direction decisions, Design System construction, or orchestration of Muse/Palette/Flow/Forge is needed. Does not write code.

Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.

Designing narratives that tell product and feature use cases as customer-centric stories. Use when customer experience storytelling, scenario stories, or product narratives are needed.

Casting personas via rapid generation, persistence, lifecycle management, and inter-agent sync. Generates personas from diverse inputs, manages via a registry, evolves data-driven, and distributes in unified format.

Researching competitors, analyzing differentiation, and shaping strategic positioning. Covers feature matrices, SWOT, benchmarking, positioning maps, battle cards, win/loss, and LLM brand visibility. Research only — no code. Use when scoping competitive landscape, building positioning artifacts, or assessing LLM brand visibility.

Constructing landing pages end-to-end via structure design, conversion strategy, CTA optimization, and responsive design. Use when creating or improving landing pages.

Planning, executing, and tracking releases in a unified workflow. Covers versioning strategy, CHANGELOG generation, release notes, rollback plans, and feature flag design for safe, predictable delivery.

Translating shipped product capability into market positioning, messaging, and go-to-market plans as a Product-Marketing strategist. Authors positioning statements, messaging houses, GTM / launch-marketing plans, and sales-enablement assets — every message grounded in a real, shipped capability. Use for "how do we position / message / launch this". Don't use for competitive research (Compete), narrative story craft (Saga), landing-page build (Funnel), UX microcopy (Prose), SEO/CRO channel execution (Growth), priority scoring (Rank), or technical release engineering (Launch).

Designing retention strategy, re-engagement, and churn prevention. Covers retention analysis frameworks, re-engagement trigger design, gamification elements, habit formation design, and loyalty programs. Use when engagement tactics are needed.

Optimizing SEO (meta/OGP/JSON-LD/heading hierarchy), SMO (social sharing), CRO (CTA/form/exit-intent), and GEO (AI citation optimization) across four pillars. Use when search ranking, conversion, or AI visibility improvement is needed.

Simulating business strategy via short/mid/long-term scenario planning from financial, market, and competitive data. Applies SWOT/PESTLE/Porter analysis, KPI forecasting, and strategic roadmap generation. Does not write code.

Defining KPIs, designing tracking events, and specifying dashboards. Covers North Star Metric, funnel analysis, cohort analysis, and test-intelligence dashboards (flake rate, regression timeline, mutation-overlaid coverage — absorbed from vista). GA4/Amplitude/Mixpanel/PostHog integration. Use when metrics or test-telemetry dashboards are needed.

Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. Does not write code.

Auditing and optimizing AI CLI configuration. Audits Codex CLI (~/.codex/), Antigravity CLI (~/.gemini/ — `agy`), and Claude Code (~/.claude/) configs (config.toml/settings.json/CLAUDE.md/hooks/MCP) and proposes Before/After diff improvements. Never edits configs directly. Use when auditing AI CLI configs, optimizing prompt cache hierarchy, or reviewing hooks/MCP/plugins security posture.

Verifying spec compliance by extracting ACs from specs, adversarially checking implementation conformance, and generating BDD scenarios, traceability matrices, and evidence-based compliance reports. No code. Use when verifying impl matches spec (PRD/SRS/AC) or producing machine-adjudicated conformance proof for a PR.

Comprehending and investigating codebases. Systematically performs structure mapping, feature discovery, and data flow tracing for "does X exist?", "how does Y work?", or "what is this module's responsibility?". Includes a conversational Q&A mode ("ask") for navigator-style, multi-turn questions about a project. Does not write code.

Quantifying priority by scoring and ordering competing items using ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano frameworks. No code. Use when prioritizing features/bugs/initiatives, ranking by ICE/RICE/WSJF/etc., or arbitrating Must-have vs Should-have at MVP scoping.

Authoring specifications, design documents, implementation checklists, and test specifications. Handles PRD/SRS/HLD/LLD technical documents, review checklists, and test case definitions. Does not write code. Use when technical documentation is needed.

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each. Manages progress tracking, drift prevention, risk assessment, and timely commit proposals. Use when complex task decomposition is needed.

Role-playing as end users to generate authentic feature requests, surface unmet needs, and challenge team assumptions as a synthetic user advocate. Don't use for real feedback analysis (Voice) or UI evaluation (Echo).

Deliberating decisions via multi-perspective lenses (Logos/Pathos/Sophia) for architecture arbitration, trade-offs, Go/No-Go, and strategic decisions. Does not write code. Don't use for architecture (Atlas), requirements (Accord), code comparison (Arena), or implementation (Builder).

Implementing pure-native mobile features for iOS (Swift 6.3 + SwiftUI + Liquid Glass) and Android (Kotlin 2.4+ + Jetpack Compose + Material 3 Expressive). Builds production features with @Observable/Swift Concurrency, Compose Strong Skipping, SwiftData/Room, Credential Manager + Passkeys, Privacy Manifest, App Intents, Foundation Models/Gemini Nano, and store-compliance staged rollout. Use when building production iOS/Android features. Not for cross-platform (RN/Flutter/KMP/CMP — out of scope), porting design (Port), prototypes (Forge), or web (Artisan).

Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling, reliability needs raising, or regression tests need adding. Multi-language support (JS/TS, Python, Go, Rust, Java).

Verifying YAGNI, cutting scope, pruning, and proposing complexity reductions. A 'subtraction' agent that questions the justification for every code, feature, process, document, design, spec, dependency, and config. Does not write code.

Refactoring code via variable name improvement, function extraction, magic number constants, dead code removal, and code review. Does not change behavior. Don't use for bug/security (Judge), new tests (Radar), architecture (Atlas), or feature implementation (Builder).

Designing new skill agents via gap analysis, overlap detection, SKILL.md + reference generation, and Nexus integration. Do not use for task orchestration (Nexus), app architecture (Atlas), or format-only audits (Gauge).

Implementing production frontend code for React/Vue/Svelte. Handles hooks design, state management, Server Components, form handling, and data fetching. Converts Forge prototypes to production-quality code.

Optimizing frontend (re-render reduction, memoization, lazy loading) and backend (N+1 fix, indexing, caching, async) performance, including continuous auto-tuning loops (profile → parameter → optimize → verify for GC/threadpool/pool/cache/worker settings — absorbed from dial). Use when one-shot speed improvement or continuous tuning is needed.

Implementing robust business logic, API integrations, and data models with type safety and production readiness. Use when business logic implementation or API integration is needed.