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Found 82 Skills
Portable AI SDLC shared-helper runtime. Use when an AI assistant installs, verifies, diagnoses, or repairs project-scoped AI SDLC skills whose deterministic scripts depend on shared state, artifact, context, path, TOON, migration, or index modules. This is an installation dependency, not a lifecycle entry point.
AI SDLC context-aware navigation workflow. Use when an AI assistant needs to determine what to do next, select the right installed skill, start or resume a feature, explain blockers, inspect available capabilities, or provide evidence-backed required and optional next actions from repository state. Supports `--quick-flow` for compact guidance and `--full-flow` for stricter context verification.
Shared PR opener for the auto pipeline — commits the worktree, pushes, reuses an existing PR or opens a ready (non-draft) PR against the configured base branch with the unified body template, applies the full SDLC label set with rationale comments, and for issue-driven runs hands the issue back and releases the lock. Emits the `PR:`/`Issue:` chaining reference lines.
AI SDLC host adapter and capability negotiation workflow. Use when an AI assistant needs to validate a host adapter manifest, map portable workflow operations to host-native operations, negotiate capabilities and limits, select deterministic semantic-preserving fallbacks, or explain why a host cannot run a plan. Supports `--quick-flow` and `--full-flow`.
Threat modeling methodologies (STRIDE, DREAD), attack trees, threat modeling as code, and integration with SDLC for proactive security design
AI SDLC approvals, sandbox, and command rule workflow. Use when an AI assistant needs to decide whether to request escalated permissions, explain sandbox failures, propose prefix_rule approvals, avoid unsafe command patterns, or document why a command was or was not rerun outside the sandbox. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
AI SDLC security testing workflow. Use when an AI assistant is asked for OWASP review, security testing, abuse-case analysis, authz/authn review, input validation review, secret exposure review, or security-focused validation of a diff, endpoint, workflow, or subsystem. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Optional AI SDLC evidence-council workflow. Use when an AI assistant needs to review a high-impact topic through several explicit perspectives, orchestrate simulated lenses or truly independent reviewer executions, and synthesize evidence-backed agreements, conflicts, proposals, owners, and unresolved questions without allowing panel members to rewrite authoritative artifacts. Supports `--quick-flow` for labeled simulated review and `--full-flow` for stricter panel and evidence coverage.
AI SDLC change-impact and lifecycle recovery workflow. Use when a requirement, acceptance criterion, decision, API contract, risk assumption, or other traced source changed after downstream artifacts were created and an AI assistant must identify stale artifacts, affected lifecycle stages, and evidence-backed reopen or revalidation actions without silently rewriting authoritative state. Supports `--quick-flow` for focused trace scanning and `--full-flow` for strict state and source-evidence gates.
AI SDLC commit preparation workflow. Use when an AI assistant is asked to commit repository changes, prepare an auditable commit message, stage files safely, include SDD traceability, verify branch/spec alignment, or verify the working tree before committing. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
AI SDLC business analysis workflow. Use when an AI assistant needs to frame a feature or change before implementation, derive actors, workflows, business rules, assumptions, acceptance criteria, and richer spec context for requirements and design. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
AI SDLC repository spec-driven development workflow. Use when an AI assistant receives a medium or large feature, refactor, API change, architecture change, provider integration change, or any request that must follow requirements, design, test cases, QA planning, tasks, implementation, and validation. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.