Systematic approach to implementing new features in Rust memory systems

Implement feature steps using git worktrees, build and test adaptively, update implementation plan, and generate test plans. This skill should be used when ready to implement one or more steps from an implementation plan, automatically adapting to any framework, language, or project structure.

Develop features in isolated git worktrees with autonomous testing and iteration. Use when the user asks to develop a feature in a separate worktree, wants isolated feature development, or mentions working without affecting the current directory.

Merge feature branches into main using squash commits with comprehensive commit messages. Use this skill when the user requests to merge, ship, or integrate a feature branch into the main branch. The skill analyzes all commits in the feature branch to understand the full scope of changes, then creates a single squashed commit with a proper conventional commit message that summarizes the entire feature.

Creates phase-based feature plans with quality gates and incremental delivery structure. Use when planning features, organizing work, breaking down tasks, creating roadmaps, or structuring development strategy. Keywords: plan, planning, phases, breakdown, strategy, roadmap, organize, structure, outline.

Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.

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Reviews feature specifications for completeness, testability, and implementation readiness. Validates acceptance criteria, edge cases, and technical constraints. Use when reviewing feature specs before implementation or during sprint planning.

Creates focused feature specifications with user stories, acceptance criteria, and edge cases. Lighter than PRD, focuses on single feature implementation. Use when specifying individual features after PRD approval or for standalone feature work.

Feature specification and planning guidelines for software engineers. This skill should be used when writing PRDs, defining requirements, managing scope, prioritizing features, or handling change requests. Triggers on tasks involving feature planning, specification writing, stakeholder alignment, or scope management.

Guide iterative specification and CDC (Cahier Des Charges) creation through deep questioning, context analysis, and proactive proposals. Use this skill BEFORE feature-research to clarify requirements, identify prerequisites, define scope, and document complete specifications. Triggers when starting a new feature, task, bug fix, or refactoring and requirements need clarification.

Validate features_backlog.md for an idea against concept_summary.md and epics_backlog.md (writes report to ideas/<IDEA_ID>/runs and updates ideas/<IDEA_ID>/latest; optional patch if allowed)

Guide for building and maintaining ActivityPub/fediverse applications using Fedify. This skill should be used when users want to create federated social applications, implement ActivityPub protocols, build new features, test and debug federation, deploy to production, look up FEPs, or maintain existing Fedify applications.

Systematically gather, organize, and act on user feedback

Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.

Give effective feedback that promotes learning and growth. Covers written feedback on student work, rubric design, peer review facilitation, and constructive critique techniques. Triggers on grading, feedback, rubrics, peer review, or critique requests.

Use when receiving UAT feedback, bug reports, user testing results, stakeholder feedback, QA findings, or any batch of issues to investigate. Investigates each item BEFORE creating issues, classifies by type and priority, creates well-formed GitHub issues with proper project board integration.

Fetch GitHub issue details with AI analysis comment from github-actions bot, extracting structured data for architecture planning in WescoBar workflows

Use when user mentions a Jira issue key (e.g., PLAT-123) or needs context from Jira. Retrieves and formats issue details for PRISM agent workflows.

Fetches official documentation for external libraries and frameworks (React, Next.js, Prisma, FastAPI, Express, Tailwind, MongoDB, etc.) with 60-90% token savings via content-type filtering. Use this skill when implementing features using library APIs, debugging library-specific errors, troubleshooting configuration issues, installing or setting up frameworks, integrating third-party packages, upgrading between library versions, or looking up correct API patterns and best practices. Triggers automatically during coding work - fetch docs before writing library code to get correct patterns, not after guessing wrong.

MUST use this skill when user asks to check/review PR comments, address reviewer feedback, fix review issues, or respond to code review. Fetches unresolved review comments from GitHub PR for the current branch.

Extract content from the public notes website at notes.dsebastien.net. Use when fetching MoCs, notes, or any content from the Obsidian Publish site.

Fetches and processes GitHub pull requests waiting for review. Returns fully formatted markdown with PRs grouped by category (Feature/Bug, Chores, Dependency Updates) and sorted by age. Includes metadata like CI status, review status, size metrics, and viewing history. Use when user wants to see their PR review queue.

Sync and integrate Fever Partners API for plans, reviews, attendees, and venues. Use when implementing Fever data sync, debugging API issues, or building review/analytics features.

Example-based prompting techniques for in-context learning

Complete audio encoding and normalization system. PROACTIVELY activate for: (1) Audio codec selection (AAC, MP3, Opus, FLAC), (2) Loudness normalization (EBU R128, loudnorm), (3) Audio extraction from video, (4) Format conversion, (5) Volume adjustment and dynamics, (6) Noise reduction and EQ, (7) Channel operations (stereo/mono/surround), (8) Sample rate and bit depth conversion, (9) Audio fade in/out and crossfades, (10) Podcast and broadcast processing chains. Provides: Codec comparison tables, loudness standards reference, two-pass normalization scripts, professional mastering chains. Ensures: Broadcast-compliant audio with proper loudness and quality.

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.

Complete GPU-accelerated encoding/decoding system for FFmpeg 7.1 LTS and 8.0.1 (latest stable, released 2025-11-20). PROACTIVELY activate for: (1) NVIDIA NVENC/NVDEC encoding, (2) Intel Quick Sync Video (QSV), (3) AMD AMF encoding, (4) Apple VideoToolbox, (5) Linux VAAPI setup, (6) Vulkan Video 8.0 (FFv1, AV1, VP9, ProRes RAW), (7) VVC/H.266 hardware decoding (VAAPI/QSV), (8) GPU pipeline optimization with pad_cuda, (9) Docker GPU containers, (10) Performance benchmarking. Provides: Platform-specific commands, preset comparisons, quality tuning, full GPU pipeline examples, Vulkan compute codecs, VVC decoding, troubleshooting guides. Ensures: Maximum encoding speed with optimal quality using GPU acceleration.

Complete karaoke subtitle system and advanced animated text effects. PROACTIVELY activate for: (1) Karaoke-style highlighted lyrics, (2) ASS/SSA advanced subtitle styling, (3) Scrolling credits (horizontal/vertical), (4) Typewriter text animation, (5) Bouncing/moving text, (6) Text fade in/out effects, (7) Word-by-word text reveal, (8) Kinetic typography, (9) Lower thirds animation, (10) Countdown timers and dynamic text. Provides: ASS karaoke timing format, drawtext with time expressions, scrolling text patterns, text animation formulas, kinetic typography techniques, subtitle styling reference, multi-line animated text.

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.

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.

Authoritative Python-FFmpeg parameter integration reference ensuring type safety, accurate parameter mappings, and proper unit conversions. PROACTIVELY activate for: (1) ffmpeg-python library usage, (2) Python subprocess FFmpeg calls, (3) Caption/subtitle parameter mapping (drawtext, ASS), (4) Color format conversions (BGR, RGB, ABGR, ASS &HAABBGGRR), (5) Time unit conversions (seconds, centiseconds, milliseconds), (6) Type safety validation (int, float, string), (7) Coordinate systems, (8) Parameter range enforcement, (9) Frame pipe handling, (10) Error detection for type mismatches. Provides: Complete parameter type reference, color format conversion tables, time unit conversion formulas, validation patterns, working Python examples with proper typing.

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.

Complete live streaming and protocol system for FFmpeg 7.1 LTS and 8.0.1 (latest stable, released 2025-11-20). PROACTIVELY activate for: (1) RTMP streaming to Twitch/YouTube/Facebook, (2) HLS output and adaptive bitrate (ABR), (3) DASH streaming setup, (4) Low-latency streaming (LL-HLS, LL-DASH), (5) SRT protocol configuration, (6) WebRTC/WHIP sub-second latency (FFmpeg 8.0+), (7) Protocol conversion (RTMP to HLS), (8) Multi-destination streaming, (9) nginx-rtmp integration, (10) Docker streaming services. Provides: Platform-specific stream commands, ABR ladder examples, encryption setup, latency optimization, WHIP authentication, production patterns. Ensures: Reliable live streaming with optimal quality and latency.

Complete browser-based FFmpeg system. PROACTIVELY activate for: (1) ffmpeg.wasm setup and loading, (2) Browser video transcoding, (3) React/Vue/Next.js integration, (4) SharedArrayBuffer and COOP/COEP headers, (5) Multi-threaded ffmpeg-core-mt, (6) Cloudflare Workers limitations, (7) Custom ffmpeg.wasm builds, (8) Memory management and cleanup, (9) Progress tracking and UI, (10) IndexedDB core caching. Provides: Framework-specific examples, header configuration, common operation recipes, performance optimization, troubleshooting guides. Ensures: Client-side video processing without server dependencies.

Review code changes for FFP project standards including multi-tenant security, British English, architecture patterns, and SOLID principles. Use when reviewing PRs, checking branch changes, or auditing code quality.

Testing toolkit for the FHIR Writing Clinical Notes specification at connectathons. Use when the user needs to test FHIR DocumentReference write operations, validate conformance with the Writing Clinical Notes spec, or participate in a FHIR connectathon for clinical notes. Includes templates, OAuth helpers, and automated test scenarios for both provider-authored and patient-asserted notes.

Comprehensive FHIR (Fast Healthcare Interoperability Resources) software development assistant. Use when working with FHIR APIs, implementations, or healthcare data exchange. Supports FHIR R4, R4B, R5, Implementation Guides (IGs), validation, terminology, and SMART on FHIR. Ideal for building FHIR servers, clients, validators, or healthcare applications that need to process FHIR resources.

Use when writing or editing novels, short stories, or any fiction manuscript. Trigger on: 'write fiction', 'edit my novel', 'developmental edit', 'line edit', 'character voice', 'plot hole', 'brainstorm', or fiction writing tasks.

Validate field completeness, format correctness, and data quality for vehicle insurance CSV/Excel files.

Analyze Field Labs coaching transcription data, calculate session metrics, and generate daily summaries. Use for processing Fieldy voice transcriptions and creating coaching reports.

This skill processes files containing figlet tags and replaces them with ASCII art representations. It detects and preserves comment styles (forward slash forward slash, hash, double-dash, forward slash asterisk), automatically manages Node.js dependencies, and supports 400+ fonts (defaulting to the standard font). The skill should be used when a user requests converting marked text in a file to ASCII art using figlet tag syntax, or when they want to list available fonts.

figma-design-extraction

Extracts design tokens from Figma files and generates production-ready CSS, SCSS, JSON, TypeScript, and W3C DTCG format files using the Figma MCP server

Generate Figma mockups from wrangler specifications with hierarchical file structure and approval tracking

Extract design specifications from Figma designs using the Figma MCP server. Used during planning workflows to gather detailed design context for implementation.

Extract design specifications from Figma files using MCP integration.