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Found 82 Skills
Use when you need to execute I1 (Implementation Plan) in the Spec Pack of sdlc-dev, convert requirements/design into `{FEATURE_DIR}/implementation/plan.md` (the single source of truth (SSOT) for execution checklist and status), and provide an unambiguous task list for subsequent I2 execution.
AI SDLC Conventional Commit workflow. Use when an AI assistant drafts, validates, reviews, or fixes commit messages in this repository, especially when commits must include SDD spec references, validation summaries, or safe conventional commit subjects. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
AI SDLC resumable task-runtime workflow. Use when an AI assistant needs to start or resume a versioned delivery run, select dependency-ready work, enforce step, failure, and token budgets, retry safely, persist exact stop reasons, recover state from an append-only journal, or require commit evidence at task boundaries. Supports `--quick-flow` for deterministic local runs and `--full-flow` for strict transition review.
AI SDLC declarative workflow planning. Use when an AI assistant needs to validate a versioned workflow, plan typed dependency steps, evaluate bounded conditions, enforce approval gates, attach deterministic hooks, detect cycles, or create safe dependency waves with sequential fallback when host concurrency or isolation is unavailable. Supports `--quick-flow` and `--full-flow`.
Zero-config SDLC onboarding. Detects project environment, asks what the developer wants to do, and recommends skills organized by workflow phase. Activate when a user starts a new project, asks "how do I get started," or has no other SDLC skills installed.
Generate a Software Maintenance Plan (SMP) and supporting maintenance documentation for SDLC projects. Compliant with ISO/IEC/IEEE 14764:2022. Covers Maintenance Strategy, MR/PR handling workflow, CCB process, maintenance cost estimation, and all...
Optional AI SDLC research workflow. Use when an AI assistant needs to investigate a customer, market, domain, technology, regulation, competitor, operational question, or implementation uncertainty and produce a routed source inventory plus synthesized findings with confidence, limitations, open questions, and delivery trace targets. Supports `--quick-flow` for focused evidence and `--full-flow` for multi-source and source-diversity gates.
AI SDLC Git-flow branching workflow. Use when an AI assistant starts implementation work, needs to create or verify a task branch, checks branch/spec alignment, or prepares to hand off a completed user-visible task to validation and commit prep. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Orchestrates multiple skills to achieve high-level goals. Acts as the brain of the ecosystem to coordinate complex workflows across the SDLC.
Design composable agentic primitives for flexible workflows. Use when creating reusable workflow building blocks, designing SDLC primitives, or building agent operations that can be combined in different ways.
Use when working on the spec branch of sdlc-dev, when requirements are ambiguous, scope is unstable, constraints are unclear, and issues such as context drift, unfounded assumptions, multiple questions asked at once, or requests to skip the FEATURE_DIR/raw gate occur.
AI SDLC evidence-backed project context and bounded task-pack workflow. Use when an AI assistant needs to onboard to a repository, detect stack and commands, map ownership and test topology, check context drift, conditionally select task sources, exclude secrets, or allocate a freshness-aware context pack within an explicit token budget. Supports `--quick-flow` for focused evidence and `--full-flow` for stricter repository coverage.