/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",
Claude Code
$ npx -y skills add shep-ai/shep --skill shep-kit-new-feature-fast --agent claude-code
Codex
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a codex -y
Cursor
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a cursor -y
Gemini CLI
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a gemini-cli -y
GitHub Copilot
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a github-copilot -y
OpenCode
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a opencode -y
Windsurf
$ npx skills add shep-ai/shep --skill shep-kit-new-feature-fast -a windsurf -y
SKILL.md
shep-kit-new-feature-fast.SKILL.mdname: 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
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** —
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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.

