govkit-skill-name

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Actualizado3 de julio de 2026 a las 14:49

One clear sentence on what this skill does AND when Claude should use it. Include the trigger words a user would actually type (e.g. "readiness score", "spec review"), because this description is the only thing Claude sees when deciding whether to load the skill.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

Accelerated-Innovation

Accelerated-Innovation/govkit-plugins

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Basado en la clasificación ocupacional SOC

Otras ocupaciones informáticasOcupaciones informáticas y matemáticas·SOC 15-1299

SKILL.md

readonly

name	govkit-skill-name
description	One clear sentence on what this skill does AND when Claude should use it. Include the trigger words a user would actually type (e.g. "readiness score", "spec review"), because this description is the only thing Claude sees when deciding whether to load the skill.

GovKit Skill Name

Purpose

What this skill helps a team do, in one or two sentences.

Instructions

Step-by-step guidance Claude follows when this skill is active.

Examples

Example request 1
Example request 2

Guardrails

What this skill must never do.

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Review generated feature specs (Gherkin, NFRs, evaluation criteria) before GovKit execution. Use for refinement conversations where Product, QA, and Engineering improve a draft together before AI-assisted coding starts. Tool-agnostic; works with any generator (e.g. Aha!) and any tracker (e.g. Azure DevOps, Jira). Trigger whenever the user mentions Gherkin, acceptance criteria, feature refinement, 3 Amigos, Draft 0, Development Token, spec review, PM pre-review, QA evidence review, or asks to review, score, or rewrite a feature spec before coding begins — even if they don't name GovKit explicitly.

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govkit-synthetic-data

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Generate synthetic test data for a GovKit feature package using Python Faker — a seeded, repeatable generator script plus committed data files derived from the feature's Gherkin scenarios. Use whenever the user asks to "generate test data", "create synthetic data", "seed data", "fixtures", "fake data", "sample records", or "load-test data" for a feature, work item, or acceptance.feature — even if they don't say GovKit or Faker. Also use after a feature package passes readiness and the team needs data to exercise the scenarios. Works for any target stack; only the generator itself is Python.

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Accelerated-Innovation/govkit-plugins

Emit structured Tier 1 metric events (NDJSON) from a GovKit-governed repository's exhaust — feature packages, .govkit/marker.json, eval_criteria.yaml, CI gate runs, PR exports, and git history. Use this skill whenever the user wants to compute, emit, export, or audit delivery/quality metrics from a GovKit repo: spec completeness scores, gate-readiness audits, metric event streams for an aggregator, velocity/quality pair inputs, or "which features aren't gate-ready." Trigger on mentions of GovKit metrics, metric events, telemetry emission, feature package audit, spec completeness, or AIPOS Tier 1 metrics — even if the user doesn't name this skill.

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