Results for “specs”

Weed the Allium garden. Find where Allium specifications and implementation code have diverged, and help resolve the divergences. Use when the user wants to check spec-code alignment, compare specs against implementation, audit for spec drift or violations, sync specs with code or code with specs, or verify whether the implementation matches what the spec says.

Builds and governs design systems with a three-layer token architecture, component specs, and design-to-code handoff, including slide generation.

Tend the Allium garden. Use when the user wants to write, edit, update, add to, improve, clarify, refine, restructure, fix or migrate Allium specs. Covers adding entities, rules, triggers, surfaces and contracts, fixing syntax or validation errors, renaming or refactoring within specs, migrating specs to a new language version, and translating requirements into well-formed specifications. Pushes back on vague requirements.

Ink terminal renderer for json-render that turns JSON specs into interactive terminal UIs. Use when working with @json-render/ink, building terminal UIs from JSON, creating terminal component catalogs, or rendering AI-generated specs in the terminal.

Guides users through a structured workflow for co-authoring documentation, proposals, technical specs, and decision docs across three stages: context gathering, iterative refinement, and reader testing.

Use for high-throughput implementation tasks from clear specs and acceptance criteria.

Mines SpecStory coding histories from any agent into a persistent corpus, surfaces reproducible workflows with corroborated evidence, and interactively forges chosen ones into skills installed across agent harnesses.

Fornece dimensões, proporções, áreas seguras, tipografia mínima e contagem de slides por plataforma, consultando uma tabela de referência. Use quando precisar de especificações de formato para mídias sociais.

Interactive feature development workflow from idea to implementation. Creates requirements (EARS format), design documents, and implementation task lists. Use when creating feature specs, requirements documents, design documents, or implementation plans. Triggered by "kiro" or references to .kiro/specs/ directory.

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

Co-author structured documents (specs, PRDs, RFCs) through a 3-stage workflow: context gathering, drafting, and reader testing. Use when writing proposals, technical specs, or similar structured content. Repo decision-memory system (INDEX.md, rejected alternatives, agent rules) → docs-adr.

Convert plans/specs into independently-grabbable vertical slice issues (HITL or AFK classification)

Regenerate `docs/roadmap.md` for a repo from all specs under `docs/specs/` + their frontmatter (status, tags, pr). Produces Now/Next/Recently-shipped sections. Run after creating / shipping a spec, or at the start of a new session for orientation.

Write product-facing technical specs that clarify decisions, constraints, dependencies, and rollout.

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.

Captures a baseline snapshot of a Jetson device's module model, L4T version, kernel, OS version, and power mode for performance testing or verification.

Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.

Expert en design system markdown specs (73+ design systems as markdown, tokens, components)

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Build Meta/Instagram ad campaigns with audience targeting, ad copy, and creative specs

spec-ship

Convierte especificaciones aprobadas en un plan técnico detallado, documentando decisiones de arquitectura, flujo de datos y cambios de archivos.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

PHOENIX v2.0 — Contribution Velocity Scanner (Hardened). Same battle-tested signal. Fixed trade counter. DSL plugin exits.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Fine-tune large language models by training less than 1% of parameters using LoRA, QLoRA, and 25+ adapter methods, enabling efficient adaptation on limited GPU memory.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Decodes ams-OSRAM MPN encoding patterns, including product families, package suffixes, and series extraction rules, with guidance for the AMSHandler.