Skip to content
Development
Skill

/shep-kit-new-feature-fast

Fast-track feature creation that collapses new-feature, research, and planning into a single autonomous pass. Produces all spec YAMLs (spec, research, plan, tasks, feature) in one go with minimal user interaction. Triggers include "quick feature", "fast feature", "rapid spec",

From plugin
shep
28119 skills10 agents

Claude Code

$ npx -y skills add shep-ai/shep --skill shep-kit-new-feature-fast --agent claude-code

SKILL.md

shep-kit-new-feature-fast.SKILL.md
name: shep-kit:new-feature-fast
description: Fast-track feature creation that collapses new-feature, research, and planning into a single autonomous pass. Produces all spec YAMLs (spec, research, plan, tasks, feature) in one go with minimal user interaction. Triggers include "quick feature", "fast feature", "rapid spec", or explicit /shep-kit:new-feature-fast invocation. Part of the Shep autonomous SDLC platform — https://shep.bot
metadata:
  version: '1.0.0'
  author: Shep AI (https://shep.bot)
  homepage: https://shep.bot
  repository: https://github.com/shep-ai/shep

Fast-Track Feature Specification

Collapse the full `new-feature → research → plan` pipeline into a single autonomous pass. Produces **all the same YAML artifacts** as the full pipeline but with minimal user interaction (0-2 clarifying questions max).

**Full workflow guide:** [docs/development/spec-driven-workflow.md](../../../docs/development/spec-driven-workflow.md)

When to Use

  • Feature scope is well-understood from the one-liner description
  • You want to skip the multi-step interactive flow
  • You want to get to implementation quickly

When NOT to Use

  • Highly ambiguous features requiring extensive discovery
  • Features with many open questions or unknowns
  • When you specifically need deep library evaluation with benchmarks

Input

The user provides a feature description inline:

/shep-kit:new-feature-fast add unit tests to backend services

If no description is provided, ask for a one-liner description. That is the **only** mandatory question.

Workflow

Phase 1: Scaffold

1.1 Derive Feature Name

Extract a kebab-case feature name from the user's description. Do NOT ask — infer it.

Examples:

  • "add unit tests to backend" → `backend-unit-tests`
  • "implement dark mode toggle" → `dark-mode-toggle`
  • "fix memory leak in agent runner" → `agent-runner-memory-fix`

1.2 Create Branch & Directory

# Determine next spec number
NEXT_NUM=$(ls -d specs/[0-9][0-9][0-9]-* 2>/dev/null | sort | tail -1 | grep -oP '^\d{3}' | xargs -I{} printf "%03d" $(({} + 1)))
# If no specs exist, use 001
[ -z "$NEXT_NUM" ] && NEXT_NUM="001"

FEATURE_NAME="<derived-kebab-case>"

# Create branch from main
git checkout main && git pull
git checkout -b "feat/${NEXT_NUM}-${FEATURE_NAME}"

# Run scaffolding (creates all template files)
.claude/skills/shep-kit-new-feature/scripts/init-feature.sh "$NEXT_NUM" "$FEATURE_NAME"

This reuses the existing init script — same templates, same directory structure.

Phase 2: Deep Analysis (Silent)

Before writing any YAML, perform thorough codebase analysis. This is the foundation for ALL subsequent YAML files. Do NOT show analysis to user — go straight to writing.

Analyze:

  • **Architecture**: Read `CLAUDE.md`, `AGENTS.md`, key source directories
  • **Existing specs**: Read `specs/*/spec.yaml` for dependencies and landscape
  • **Affected code**: Identify files, modules, patterns relevant to the feature
  • **Testing patterns**: Check existing test structure, frameworks, conventions
  • **Domain models**: Check TypeSpec definitions if relevant (`tsp/`)

Clarifying Questions (0-2 max)

After analysis, if something is **genuinely ambiguous** and would lead to a fundamentally different spec, ask **at most 1-2 quick questions**. Use `AskUserQuestion` with concrete options inferred from the codebase.

**Do NOT ask about:**

  • Things you can infer from codebase analysis
  • Style/naming preferences (follow existing conventions)
  • Technology choices that are already decided by the stack

**Examples of valid questions:**

  • "Should this cover only unit tests or also integration tests?" (scope ambiguity)
  • "Should this target the agent system or the CLI layer?" (area ambiguity)

Phase 3: Write All YAMLs

Write all YAML files in sequence. Each file should be concise but **complete** — no placeholder values, no `{{TEMPLATE}}` markers, no `TBD` entries.

3.1 Write `spec.yaml`

Fill in ALL fields with real values:

  • `name`, `number`, `branch`, `oneLiner`, `summary` — from user input + analysis
  • `phase`: `Requirements`
  • `sizeEstimate` — inferred from scope (S/M/L/XL)
  • `relatedFeatures` — from scanning existing specs
  • `technologies` — from codebase analysis
  • `openQuestions: []` — **MUST be empty** (resolve everything inline)
  • `content` section with:
  • **Problem Statement** — concise, from user description
  • **Success Criteria** — 3-6 measurable criteria
  • **Affected Areas** — table with area, impact level, reasoning
  • **Dependencies** — on other features or external systems
  • **Size Estimate** — with reasoning

3.2 Write `research.yaml`

Fill in ALL fields:

  • `summary` — one-line research summary
  • `decisions[]` — each with `title`, `chosen`, `rejected[]`, `rationale`
  • `technologies` — libraries/tools involved
  • `openQuestions: []` — **MUST be empty**
  • `content` section with:
  • **Technology Decisions** — each decision with options considered, chosen, rationale
  • **Library Analysis** — table if new libraries are involved (skip if using existing stack only)
  • **Security Considerations** — or "No security implications identified"
  • **Performance Implications** — or "No performance implications identified"

**Keep it focused:** If the feature uses only existing stack/patterns, the research section can be brief. Don't fabricate decisions where none exist.

3.3 Write `plan.yaml`

Fill in ALL fields:

  • `phases[]` — each with `id`, `name`, `parallel`, `taskIds`
  • `filesToCreate[]` — new files with paths
  • `filesToModify[]` — existing files to change
  • `openQuestions: []` — **MUST be empty**
  • `content` section with:
  • **Architecture Overview** — ASCII diagram or description of how components connect
  • **Implementation Strategy** — high-level walkthrough of phases and their ordering rationale
  • **Files to Create/Modify** — tables
  • **Testing Strategy (TDD: Tests FIRST)** — what tests at each layer
  • **Risk Mitigation** —
Read more
@shep-ai
Ships withshep

Ship features 10x faster. Built In Auto: Memory, K8S Agent & Security (SDD+SDLC) . 😇

28119 skills10 agents
Get the whole plugin

Stars on GitHub

Momentum

stars in total
281
Star history of shep-ai/shep on GitHub, a chart by star-history.com

Other skills on shep.

architecture-reviewer

architecture-reviewer

Use when making architectural decisions, planning features, designing new components, reviewing PRs, or validating that proposed…

@shep-ai@shep-aiInstall Skill
cross-validate-artifacts

cross-validate-artifac…

Cross-validate documentation and artifacts across the codebase for consistency, conflicts, and contradictions. Use when users ask…

@shep-ai@shep-aiInstall Skill
mermaid-diagrams

mermaid-diagrams

Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document…

@shep-ai@shep-aiInstall Skill
react-flow

react-flow

React Flow (@xyflow/react) for workflow visualization with custom nodes and edges. Use when building graph visualizations,…

@shep-ai@shep-aiInstall Skill
shadcn-ui

shadcn-ui

Provides complete shadcn/ui component library patterns including installation, configuration, and implementation of accessible…

@shep-ai@shep-aiInstall Skill
shep-kit-commit-pr

shep-kit-commit-pr

Use when ready to commit, push, and create a PR with CI verification. Triggers include "commit and pr", "push pr", "create pr",…

@shep-ai@shep-aiInstall Skill

More skills from other plugins.

brainstorming
Auto-invoked

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…

@obra@obraInstall Skill
ask-matt

ask-matt

Ask which skill or flow fits your situation. A router over the skills in this repo.

skills2w309k
@mattpocock@mattpocockInstall Skill
accessibility
Auto-invoked

accessibility

Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels,…

ecc2w293k
@affaan-m@affaan-mInstall Skill
compiler-commit

compiler-commit

Use when you want to verify compiler changes and commit with the correct convention. Runs tests, lint, and format, then commits…

react5d252k
@facebook@facebookInstall Skill

Questions about shep-kit-new-feature-fast.

Which coding agents can use shep-kit-new-feature-fast?
shep-kit-new-feature-fast installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.