ffmpeg-modal-containers
Complete Modal.com FFmpeg deployment system for serverless video processing. PROACTIVELY activate for: (1) Modal.com FFmpeg container setup, (2) GPU-accelerated video encoding on Modal (NVIDIA, NVENC), (3) Parallel video processing with Modal map/starmap, (4) Volume mounting for large video files, (5) CPU vs GPU container cost optimization, (6) apt_install/pip_install for FFmpeg, (7) Python subprocess FFmpeg patterns, (8) Batch video transcoding at scale, (9) Modal pricing for video workloads, (10) Audio/video processing with Whisper. Provides: Image configuration examples, GPU container patterns, parallel processing code, volume usage, cost comparisons, production-ready FFmpeg deployments. Ensures: Efficient, scalable video processing on Modal serverless infrastructure.
ffmpeg-noise-reduction
Complete FFmpeg noise reduction and denoising for video and audio. PROACTIVELY activate for: (1) Video denoising (nlmeans, hqdn3d, vaguedenoiser), (2) Hardware-accelerated denoising (nlmeans_opencl, nlmeans_vulkan), (3) Audio noise reduction (afftdn, anlmdn), (4) Film grain removal, (5) Low-light footage enhancement, (6) Compression artifact removal, (7) Broadcast noise reduction, (8) Clean audio recordings. Provides: Denoising filters, parameter tuning, hardware acceleration, quality/speed tradeoffs.
ffmpeg-opencv-integration
Complete FFmpeg + OpenCV + Python integration guide for video processing pipelines. PROACTIVELY activate for: (1) FFmpeg to OpenCV frame handoff, (2) cv2.VideoCapture vs ffmpeg subprocess, (3) BGR/RGB color format conversion gotchas, (4) Frame dimension order img[y,x] vs img[x,y], (5) ffmpegcv GPU-accelerated video I/O, (6) VidGear multi-threaded streaming, (7) Decord batch video loading for ML, (8) PyAV frame-level processing, (9) Audio stream preservation with video filters, (10) Memory-efficient frame generators, (11) OpenCV + FFmpeg + Modal parallel processing, (12) Pipe frames between FFmpeg and OpenCV. Provides: Color format conversion patterns, coordinate system gotchas, library selection guide, memory management, subprocess pipe patterns, GPU-accelerated alternatives to cv2.VideoCapture. Ensures: Correct integration between FFmpeg and OpenCV without color/coordinate bugs. See also: ffmpeg-python-integration-reference for type-safe parameter mappings.
ffmpeg-cicd-runners
Complete CI/CD video processing system. PROACTIVELY activate for: (1) GitHub Actions FFmpeg setup, (2) GitLab CI video pipelines, (3) Jenkins declarative pipelines, (4) FFmpeg caching strategies, (5) Windows runner workarounds, (6) GPU-enabled self-hosted runners, (7) Matrix builds for multi-format, (8) Artifact upload/download, (9) Video validation workflows, (10) BtbN/FFmpeg-Builds integration. Provides: YAML workflow examples, Docker container patterns, caching configuration, platform-specific solutions, debugging guides. Ensures: Fast, reliable video processing in CI/CD pipelines.
ffmpeg-kinetic-captions
Expert kinetic typography and animated caption system for viral video. PROACTIVELY activate for: (1) Kinetic captions with word-grow highlighting, (2) Karaoke-style progressive fill with scale animation, (3) Word bounce/pop effects (CapCut-style), (4) Spring physics text animation, (5) Shake/tremor emphasis effects, (6) Typewriter character reveal, (7) Multi-color karaoke transitions, (8) Elastic overshoot animations, (9) Word-by-word caption reveal, (10) TikTok/Shorts/Reels viral caption styles, (11) Kinetic typography for music videos, (12) Impact text slam effects, (13) Breathing/pulse text animation, (14) Color sweep highlighting, (15) Animated lower thirds. Provides: Complete ASS animation tag reference, word-grow karaoke formulas, spring physics parameters, platform-specific timing profiles, Python generation scripts, production-ready templates, and viral caption best practices for 2025-2026.
ffmpeg-pyav-integration
Complete PyAV (Python FFmpeg bindings) integration guide. PROACTIVELY activate for: (1) PyAV installation on Ubuntu/Windows/macOS, (2) Building PyAV against custom FFmpeg, (3) FFmpeg 7.0/8.0+ compatibility, (4) av.open() video/audio decoding, (5) VideoFrame/AudioFrame NumPy conversion, (6) Filter graph processing, (7) Video encoding with H.264/H.265/AV1, (8) Seeking and keyframe extraction, (9) RTSP/network streaming with PyAV, (10) Memory management and thread safety, (11) Error handling with FFmpegError, (12) Subtitle extraction, (13) Container manipulation and remuxing, (14) Performance optimization and threading. Provides: Complete PyAV API patterns, installation guides for all Ubuntu versions, FFmpeg 8.0+ compatibility matrix, type-safe examples, memory management best practices, filter graph examples, encoding/decoding patterns.
ffmpeg-shapes-graphics
Complete shape and graphics overlay system. PROACTIVELY activate for: (1) Drawing rectangles (drawbox), (2) Grid overlays (drawgrid), (3) Circles and ellipses (geq), (4) Image watermarks and logos, (5) Lower third graphics, (6) Progress bars and indicators, (7) Animated overlays, (8) Blend modes and compositing, (9) Safe area guides, (10) Generated patterns (gradients, noise, checkerboards). Provides: Filter syntax reference, position expressions, color format guide, blend mode examples, performance optimization tips. Ensures: Professional graphics overlays and visual effects on video.
ffmpeg-stabilization-360
Complete FFmpeg video stabilization and 360/VR video processing. PROACTIVELY activate for: (1) Video stabilization (deshake, vidstab), (2) Hardware-accelerated stabilization (deshake_opencl), (3) 360/VR video transforms (v360), (4) Perspective correction (perspective), (5) Ken Burns/zoom-pan effects (zoompan), (6) Lens distortion correction (lenscorrection, lensfun), (7) Action camera footage, (8) Drone video processing, (9) VR headset formats. Provides: Stabilization workflows, 360 projection conversions, motion effects, lens correction.
ffmpeg-cloudflare-containers
Complete Cloudflare Container FFmpeg system. PROACTIVELY activate for: (1) Cloudflare Containers setup, (2) Native FFmpeg at edge, (3) GPU-accelerated containers, (4) Durable Objects integration, (5) R2 storage for video files, (6) Container autoscaling, (7) Streaming large files, (8) Workers + Containers architecture, (9) Live streaming relay at edge, (10) Container vs Workers comparison. Provides: Dockerfile examples, Worker code, container configuration, GPU setup, R2 integration, production patterns. Ensures: Native FFmpeg performance at Cloudflare edge with full GPU support.
clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
bug-fix
Bug-fix pipeline. Dual RCA (Sonnet+Opus) -> Consolidation -> Codex Validation -> Implementation -> Code Review.
multi-ai
Start the multi-AI pipeline. Plan -> Review -> Implement (loop until reviews approve). Codex final gate.
building-multiagent-systems
This skill should be used when designing or implementing systems with multiple AI agents that coordinate to accomplish tasks. Triggers on "multi-agent", "orchestrator", "sub-agent", "coordination", "delegation", "parallel agents", "sequential pipeline", "fan-out", "map-reduce", "spawn agents", "agent hierarchy".
critical-perspective
Engage in critical thinking by questioning assumptions, exploring alternative perspectives, and uncovering latent topics in conversations. Use when discussions could benefit from deeper exploration, when identifying blind spots, or when broadening understanding through respectful challenge and curiosity-driven inquiry.
ontology-generator
Generate comprehensive ontological knowledge graphs in [[wikilinks]] syntax for InfraNodus visualization. Use when the user requests to create an ontology, extract entities and relationships from text, or generate knowledge graph structures. Handles both topic-based ontology generation and entity extraction from existing text. Output is formatted for direct paste into InfraNodus.com for network visualization and AI-powered gap analysis.
writing-assistant
Refine texts in any language: perfect grammar and spelling, paraphrase ideas, avoid AI detection while maintaining authentic voice. Detects grammatical patterns that signal cognitive states or structural issues—acting as a sensory system for deeper strategic insights.
cognitive-variability
Guide conversations through dynamic shifts between zoom levels (scale) and connecting/exploring (intent) to unlock creative breakthroughs and prevent rigid thinking. Use when the user is stuck or needs to develop an idea or when there is a sense of too much repetition or dispersion. Takes the user through several stages of thinking — from idea genesis, to development, to questioning, to disruptive thinking. Identifies structural gaps between idea clusters as spaces for innovation. Tracks temporal dwelling patterns and manages cognitive energy expended. Uses playfulness for difficult transitions from chaos to clarity. Maximum creative potential lives in gaps and dissipative states. Apply for complex analysis, brainstorming, being stuck, breakthroughs, decision paralysis, group facilitation, breaking repetitive patterns, or when grammatical patterns reveal cognitive issues.
seo-analysis
Comprehensive SEO analysis skill for content optimization. Use when the user asks to perform SEO analysis, keyword research, content gap analysis, search intent analysis, or wants to optimize content for search engines. Covers topic-based keyword research (informational supply and search demand), website/document analysis, and actionable SEO recommendations. Works best with InfraNodus MCP tools for real Google data access.
prd-v07-implementation-loop
Execute implementation within EPICs following test-first development, continuous SoT updates, and code traceability during PRD v0.7 Build Execution. Triggers on requests to start building, implement an epic, begin coding, or when user asks "start building", "implement epic", "coding", "development", "build execution", "implementation", "write code". Consumes EPIC- (context), TEST- (acceptance criteria). Updates existing IDs and creates code. Outputs working code with @implements traceability tags.
prd-v09-launch-metrics
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prd-v09-feedback-loop-setup
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prd-v08-runbook-creation
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prd-v08-release-planning
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prd-v07-test-planning
Define test cases BEFORE implementation, ensuring every API, business rule, and user journey has verifiable acceptance criteria during PRD v0.7 Build Execution. Triggers on requests to define tests, plan test coverage, create test cases, or when user asks "define tests", "test planning", "what to test?", "test cases", "test coverage", "TEST-", "test-first". Consumes EPIC- (scope), API-, DBT-, BR-, UJ-. Outputs TEST- entries with Given-When-Then format. Feeds v0.7 Implementation Loop.
prd-v07-epic-scoping
Transform v0.6 specifications into context-window-sized work packages (EPICs) during PRD v0.7 Build Execution. Triggers on requests to create epics, scope work, break down implementation, or when user asks "create epics", "scope work", "break down work", "context window sizing", "what to build first?", "implementation planning", "epic breakdown". Consumes API-, DBT-, FEA-, ARC-. Outputs EPIC- entries with objectives, ID references, dependencies, and context windows. Feeds v0.7 Test Planning.
prd-v09-gtm-strategy
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prd-v06-architecture-design
Define how system components connect, establishing boundaries, patterns, and integration approaches during PRD v0.6 Architecture. Triggers on requests to design architecture, create system design, define component relationships, or when user asks "design architecture", "system design", "how do components connect?", "architecture decisions", "technical architecture", "system overview". Consumes TECH- (stack selections), RISK- (constraints), FEA- (features). Outputs ARC- entries documenting architecture decisions with rationale. Feeds v0.6 Technical Specification.
prd-v06-technical-specification
Define implementation contracts (APIs and data models) that developers will build against during PRD v0.6 Architecture. Triggers on requests to define APIs, design database schema, create data models, or when user asks "define APIs", "data model", "database schema", "API contracts", "technical spec", "endpoint design", "schema design". Consumes ARC- (architecture), TECH- (Build items), UJ- (flows), SCR- (screens). Outputs API- entries for endpoints and DBT- entries for data models. Feeds v0.7 Build Execution.
prd-v03-outcome-definition
Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages.
prd-v08-monitoring-setup
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prd-v01-problem-framing
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prd-vXX-skill-name
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prd-v02-product-type-classification
Classify product approach into one of six types (Clone, Unbundle, Undercut, Slice, Wrapper, Innovation) based on competitive landscape. Triggers on PRD v0.2 work after competitive analysis, or when user asks "what type of product should we build?", "should we clone or innovate?", "is this a fast-follow opportunity?", "how should we position against competitors?", "clone vs undercut", "unbundle vs slice", or requests help choosing product strategy. Outputs BR- entries for product type classification and inherited GTM constraints.
prd-v03-pricing-model
Select and validate pricing model for PRD v0.3 Commercial Model. Triggers on requests to set pricing, choose monetization model, design pricing tiers, validate willingness to pay, or when user asks "how much should we charge?", "what pricing model?", "freemium vs paid?", "how to structure tiers?", "price point?". Consumes Competitive Landscape (CFD-) and Product Type (BR-) from v0.2. Outputs BR- entries for pricing rules, tier boundaries, and competitive positioning.
prd-v04-persona-definition
Synthesize behavioral personas from prior stage evidence for journey mapping and marketing during PRD v0.4 User Journeys. Triggers on requests to define personas, create user profiles, identify target users, or when user asks "who are our users?", "define personas", "user profiles", "target users", "persona creation", "who uses this product?". Consumes CFD- (v0.1-v0.3), BR- (targeting from v0.3 Moat), FEA- (v0.3 Feature Value Planning). Outputs PER- entries with behavioral profiles and feature relationships. Feeds v0.4 User Journey Mapping.
prd-v02-competitive-landscape-mapping
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prd-v05-technical-stack-selection
Determine technologies needed to build the product, making build/buy/integrate decisions during PRD v0.5 Red Team Review. Triggers on requests to select tech stack, evaluate technologies, make build vs. buy decisions, or when user asks "what technologies?", "select tech stack", "build or buy?", "technical decisions", "what tools do we need?", "evaluate solutions". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and trade-offs. Feeds v0.6 Architecture Design.
prd-v01-user-value-articulation
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prd-v04-user-journey-mapping
Map user missions from trigger to value moment, organizing features into coherent paths during PRD v0.4 User Journeys. Triggers on requests to map user journeys, define user flows, describe how users accomplish goals, or when user asks "map user journeys", "define user flows", "user missions", "how do users accomplish X?", "journey mapping", "what steps do users take?", "pain to value flow". Consumes PER- (Persona Definition), FEA- (Feature Value Planning), KPI- (Outcome Definition). Outputs UJ- entries with step flows, pain points, and value moments. Feeds v0.4 Screen Flow Definition.
prd-v04-screen-flow-definition
Connect user journeys to screens, defining the UI structure and navigation paths during PRD v0.4 User Journeys. Triggers on requests to define screens, design screen flows, map UI structure, plan navigation, or when user asks "what screens do we need?", "define screens", "screen flow", "UI structure", "information architecture", "navigation design", "wireframe planning". Consumes UJ- (User Journey Mapping), FEA- (Feature Value Planning), BR- (constraints). Outputs SCR- entries for screens and DES- entries for design system elements. Feeds v0.5 Red Team Review.
prd-v05-risk-discovery-interview
Surface risks through guided questioning, helping users consider pivots, constraints, and prioritization during PRD v0.5 Red Team Review. Triggers on requests to identify risks, stress-test the idea, perform red team review, or when user asks "what could go wrong?", "identify risks", "red team", "risk assessment", "challenge assumptions", "stress test the idea". Consumes all prior IDs (CFD-, BR-, FEA-, PER-, UJ-, SCR-) as interview context. Outputs RISK- entries with owner decisions and mitigations. Feeds v0.5 Technical Stack Selection.
prd-v03-moat-definition
Assess competitor defensibility and define our own moat strategy during PRD v0.3 Commercial Model. Triggers on requests to analyze competitor moats, define our defensibility, assess switching costs, identify vulnerabilities, find wedge opportunities, or when user asks "what's our moat?", "how defensible are they?", "where can we compete?", "switching costs?", "defensibility", "who to target". Consumes Competitive Landscape (v0.2) CFD- entries. Outputs CFD- entries for competitor moats and BR- entries for targeting rules and our defensibility strategy.
ghm-template-sync
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ghm-status-sync
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prd-v03-features-value-planning
Define and prioritize features with strategic traceability during PRD v0.3 Commercial Model. Triggers on requests to define features, prioritize capabilities, scope MVP, map features to pricing tiers, identify parity vs. delta features, or when user asks "what features do we build?", "what's in MVP?", "which features matter?", "feature priority", "parity features", "what's our delta?". Consumes KPI- (Outcome Definition), BR- (Pricing Model, Moat), and CFD- (Market Moat Analysis) from v0.3. Outputs FEA- entries with strategic traceability and BR-FEA- governance rules. Feeds v0.4 User Journeys.
ghm-sot-builder
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ghm-gate-check
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ghm-harvest
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ghm-id-register
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prd-v06-environment-setup
Document development environment requirements for team consistency and AI agent understanding during PRD v0.6 Architecture. Triggers on requests to define environment setup, document tooling, create dev setup guide, or when user asks "what tools do I need?", "environment setup", "dev environment", "CLI requirements", "project setup", "onboarding setup". Consumes TECH- (stack selections), ARC- (architecture decisions). Outputs ENV- entries for development, CI/CD, and infrastructure environments. Feeds v0.7 Build Execution.
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