Results for “spec”

Turns the current conversation into a spec and publishes it to the project issue tracker, with no interview. Use when the user says "write a spec", "create a PRD", or wants the discussion captured as a spec.

Create developer handoff specifications with measurements, behaviors, assets, and edge cases.

Turn vague intent into a precise, executable spec in five phases. (gstack)

Turn vague intent into a precise, executable spec in five phases. (gstack)

Turn vague intent into a precise, executable spec in five phases. (gstack)

Use when a carrier asks how to spec a new tractor — engine choice (X15 vs DD16 vs PACCAR MX), transmission (manual vs automated manual vs automatic), axle ratio, sleeper size, fuel tanks, aerodynamic options, electronic features, warranty, fleet-spec vs premium-spec tradeoffs, and how spec choices affect resale value.

Generates spectrograms and multi-panel audio feature visualizations from audio files via a command-line tool.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Cenit IO - REST API Specification API skill. Use when working with Cenit IO - REST API Specification for setup. Covers 40 endpoints.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Audit specs against codebase — find unimplemented features, diverged implementations, and undocumented code

Creates structured specifications before writing code to clarify requirements, surface assumptions, and define success criteria, reducing rework and miscommunication.

Update an existing specification file for a solution, optimized for Generative AI consumption based on new requirements or code changes.

Automatically stages and commits all changes after a Spec Kit command completes, using configurable event-based hooks.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Creates a GitHub issue from a specification file using a feature request template, checking for existing issues first.

Convert Notion specs into implementation plans, tasks, and progress tracking with linked Notion pages.

Creates detailed component specifications for design systems, covering anatomy, variants, states, accessibility, and usage guidelines.

Guides a structured software requirements specification workflow from topic intake through brainstorming, spec writing, planning, review, SRS generation, validation, and improvement reporting.

Convert Notion product specs into linked implementation plans, tasks, and progress tracking with structured workflows.

Structures product requirements into a single-page spec with problem, success metrics, scope, user stories, design notes, and rollout plan.

Explores a project's real stack and enriches Guild configuration files with detected architecture, conventions, and agent-specific context.

Manages spec-driven development with OpenSpec. Use when creating change proposals, planning features, implementing from specs, or archiving completed changes. Triggers on "proposal", "change", or "spec" with "create", "plan", "make", or "help".

Archive merged feature specs into project memory with provenance, sweep discovery, and gated cleanup

Perform a post-implementation retrospective analysis measuring spec adherence, implementation deviations, and lessons learned.

Creates GitHub Issues for unimplemented requirements from specification files, checking the codebase and existing issues to avoid duplicates.

Create a committed per-feature spec under `docs/specs/YYYY-MM-DD-<slug>/{spec,tasks}.md` with frontmatter (status, created, owner, pr, tags). Promotes an ephemeral `.claude/plans/<name>.md` into a persistent, searchable artifact. Use when starting a feature that's larger than a one-shot PR.

Creates specs before coding. Use when starting a new project, feature, or significant change and no specification exists yet. Use when requirements are unclear, ambiguous, or only exist as a vague idea.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Comprehensive code review using specialized agents — orchestrates code, comments, tests, errors, types, and simplify agents sequentially.

Feature specification creation from codebase research. Produces requirements, acceptance criteria, architecture decisions, implementation plans, and risk analysis.

Language Server Protocol specialist building unified code intelligence systems through LSP client orchestration and semantic indexing

Create GitHub Issue for feature request from specification file using feature_request.yml template.

Reduce per-token latency on Jetson vLLM servers by appending speculative decoding configuration, with guidance on when to enable and how to benchmark the improvement.

Creates a formal, AI-optimized specification for an existing GitHub Actions CI/CD workflow, focusing on behavior and requirements rather than implementation details.

Convenes a council of specialized agents to debate important decisions, synthesizing independent perspectives into a structured recommendation and optional spec document.