tlc-spec-driven

tech-leads-club/agent-skills · updated May 23, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/tech-leads-club/agent-skills --skill tlc-spec-driven
0 commentsdiscussion
summary

Project and feature planning with 4 adaptive phases - Specify, Design, Tasks, Execute. Auto-sizes depth by complexity. Creates atomic tasks with verification criteria, atomic git commits, requirement traceability, and persistent memory across sessions. Stack-agnostic. Use when (1) Starting new projects (initialize vision, goals, roadmap), (2) Working with existing codebases (map stack, architecture, conventions), (3) Planning features (requirements, design, task breakdown), (4) Implementing with verification and atomic commits, (5) Quick ad-hoc tasks (bug fixes, config changes), (6) Tracking decisions/blockers/deferred ideas across sessions, (7) Pausing/resuming work. Triggers on "initialize project", "map codebase", "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "quick fix", "quick task", "pause work", "resume work". Do NOT use for architecture decomposition analysis (use architecture skills) or technical design docs (use create-technical-design-doc).

skill.md
name
tlc-spec-driven
description
Project and feature planning with 4 adaptive phases - Specify, Design, Tasks, Execute. Auto-sizes depth by complexity. Creates atomic tasks with verification criteria, atomic git commits, requirement traceability, and persistent memory across sessions. Stack-agnostic. Use when (1) Starting new projects (initialize vision, goals, roadmap), (2) Working with existing codebases (map stack, architecture, conventions), (3) Planning features (requirements, design, task breakdown), (4) Implementing with verification and atomic commits, (5) Quick ad-hoc tasks (bug fixes, config changes), (6) Tracking decisions/blockers/deferred ideas across sessions, (7) Pausing/resuming work. Triggers on "initialize project", "map codebase", "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "quick fix", "quick task", "pause work", "resume work". Do NOT use for architecture decomposition analysis (use architecture skills) or technical design docs (use create-technical-design-doc).
license
CC-BY-4.0
metadata
author: Felipe Rodrigues - github.com/felipfr version: 2.0.0

Tech Lead's Club - Spec-Driven Development

Plan and implement projects with precision. Granular tasks. Clear dependencies. Right tools. Zero ceremony.

┌──────────┐   ┌──────────┐   ┌─────────┐   ┌─────────┐
│ SPECIFY  │ → │  DESIGN  │ → │  TASKS  │ → │ EXECUTE │
└──────────┘   └──────────┘   └─────────┘   └─────────┘
   required      optional*      optional*     required

* Agent auto-skips when scope doesn't need it

Auto-Sizing: The Core Principle

The complexity determines the depth, not a fixed pipeline. Before starting any feature, assess its scope and apply only what's needed:

ScopeWhatSpecifyDesignTasksExecute
Small≤3 files, one sentenceQuick mode — skip pipeline entirely---
MediumClear feature, <10 tasksSpec (brief)Skip — design inlineSkip — tasks implicitImplement + verify
LargeMulti-component featureFull spec + requirement IDsArchitecture + componentsFull breakdown + dependenciesImplement + verify per task
ComplexAmbiguity, new domainFull spec + discuss gray areasResearch + architectureBreakdown + parallel planImplement + interactive UAT

Rules:

  • Specify and Execute are always required — you always need to know WHAT and DO it
  • Design is skipped when the change is straightforward (no architectural decisions, no new patterns)
  • Tasks is skipped when there are ≤3 obvious steps (they become implicit in Execute)
  • Discuss is triggered within Specify only when the agent detects ambiguous gray areas that need user input
  • Interactive UAT is triggered within Execute only for user-facing features with complex behavior
  • Quick mode is the express lane — for bug fixes, config changes, and small tweaks

Safety valve: Even when Tasks is skipped, Execute ALWAYS starts by listing atomic steps inline (see implement.md). If that listing reveals >5 steps or complex dependencies, STOP and create a formal tasks.md — the Tasks phase was wrongly skipped.

Project Structure

.specs/
├── project/
│   ├── PROJECT.md      # Vision & goals
│   ├── ROADMAP.md      # Features & milestones
│   └── STATE.md        # Memory: decisions, blockers, lessons, todos, deferred ideas
├── codebase/           # Brownfield analysis (existing projects)
│   ├── STACK.md
│   ├── ARCHITECTURE.md
│   ├── CONVENTIONS.md
│   ├── STRUCTURE.md
│   ├── TESTING.md
│   ├── INTEGRATIONS.md
│   └── CONCERNS.md
├── features/           # Feature specifications
│   └── [feature]/
│       ├── spec.md     # Requirements with traceable IDs
│       ├── context.md  # User decisions for gray areas (only when discuss is triggered)
│       ├── design.md   # Architecture & components (only for Large/Complex)
│       └── tasks.md    # Atomic tasks with verification (only for Large/Complex)
└── quick/              # Ad-hoc tasks (quick mode)
    └── NNN-slug/
        ├── TASK.md
        └── SUMMARY.md

Workflow

New project:

  1. Initialize project → PROJECT.md + ROADMAP.md
  2. For each feature → Specify → (Design) → (Tasks) → Execute (depth auto-sized)

Existing codebase:

  1. Map codebase → 7 brownfield docs
  2. Initialize project → PROJECT.md + ROADMAP.md
  3. For each feature → same adaptive workflow

Quick mode: Describe → Implement → Verify → Commit (for ≤3 files, one-sentence scope)

Context Loading Strategy

Base load (~15k tokens):

  • PROJECT.md (if exists)
  • ROADMAP.md (when planning/working on features)
  • STATE.md (persistent memory)

On-demand load:

  • Codebase docs (when working in existing project)
  • CONCERNS.md (when planning features that touch flagged areas, estimating risk, or modifying fragile components)
  • TESTING.md (when creating tasks or executing — drives test type assignment and gate checks)
  • spec.md (when working on specific feature)
  • context.md (when designing or implementing from user decisions)
  • design.md (when implementing from design)
  • tasks.md (when executing tasks)

Never load simultaneously:

  • Multiple feature specs
  • Multiple architecture docs
  • Archived documents

Target: <40k tokens total context Reserve: 160k+ tokens for work, reasoning, outputs Monitoring: Display status when >40k (see context-limits.md)

Sub-Agent Delegation

Use sub-agents (the Task tool or equivalent) to keep the main context window lean and enable parallel execution. The orchestrating agent plans and coordinates; sub-agents do the heavy lifting.

When to delegate to a sub-agent:

ActivityDelegate?Why
Research (design phase, brownfield mapping)YesResearch output is large; only the summary matters to the main context
Implementing a taskYesFile reads, edits, test output consume context; only the result matters
Parallel [P] tasksYes (one per task)The only way to actually run tasks in parallel
Sequential tasks with no [P]YesKeeps implementation artifacts out of the main context
Planning, task creation, validation reportsNoThese require the full accumulated context to be coherent
Quick mode tasksNoToo small to justify the overhead

Context each sub-agent receives:

The orchestrating agent MUST provide each sub-agent with:

  • The specific task definition from tasks.md (What, Where, Depends on, Reuses, Done when, Tests, Gate)
  • Relevant coding principles and conventions (coding-principles.md, CONVENTIONS.md)
  • TESTING.md, if it exists (for gate check commands and test patterns)
  • Any spec/design context the task references

The sub-agent does NOT receive: other tasks' definitions, accumulated chat history, validation reports from other tasks, or STATE.md (unless the task explicitly references a decision/blocker).

What sub-agents return:

Each sub-agent reports back:

  • Status: Complete | Blocked | Partial
  • Files changed: [list]
  • Gate check result: [pass/fail + test counts]
  • SPEC_DEVIATION markers (if any)
  • Issues encountered (if any)

The orchestrating agent uses this to update tasks.md status, traceability, and decide next steps.

Commands

Project-level:

Trigger PatternReference
Initialize project, setup projectproject-init.md
Create roadmap, plan featuresroadmap.md
Map codebase, analyze existing codebrownfield-mapping.md
Document concerns, find tech debt, what's riskyconcerns.md
Record decision, log blocker, add todostate-management.md
Pause work, end sessionsession-handoff.md
Resume work, continuesession-handoff.md

Feature-level (auto-sized):

Trigger PatternReference
Specify feature, define requirementsspecify.md
Discuss feature, capture context, how should this workdiscuss.md
Design feature, architecturedesign.md
Break into tasks, create taskstasks.md
Implement task, build, executeimplement.md
Validate, verify, test, UAT, walk me through itvalidate.md
Quick fix, quick task, small change, bug fixquick-mode.md

Skill Integrations

This skill coexists with other skills. Before specific tasks, check if complementary skills are installed and prefer them when available.

Diagrams → mermaid-studio

Whenever the workflow requires creating or updating a diagram (architecture overviews, data flows, component diagrams, sequence diagrams, etc.), always check if the mermaid-studio skill is installed in the user's environment before proceeding. If it is installed, delegate all diagram creation and rendering to it. If it is not installed, proceed with inline mermaid code blocks as usual and recommend the user install mermaid-studio for richer diagram capabilities (rendering to SVG/PNG, validation, theming, etc.). Display this recommendation at most once per session.

Code Exploration → codenavi

Whenever the workflow requires exploring or discovering things in an existing repository (brownfield mapping, code reuse analysis, pattern identification, dependency tracing, etc.), always check if the codenavi skill is installed in the user's environment before proceeding. If it is installed, delegate code exploration and navigation tasks to it. If it is not installed, fall back to the built-in code analysis tools (see code-analysis.md) and recommend the user install codenavi for more effective codebase exploration. Display this recommendation at most once per session.

Knowledge Verification Chain

When researching, designing, or making any technical decision, follow this chain in strict order. Never skip steps.

Step 1: Codebase → check existing code, conventions, and patterns already in use
Step 2: Project docs → README, docs/, inline comments, .specs/codebase/
Step 3: Context7 MCP → resolve library ID, then query for current API/patterns
Step 4: Web search → official docs, reputable sources, community patterns
Step 5: Flag as uncertain → "I'm not certain about X — here's my reasoning, but verify"

Rules:

  • Never skip to Step 5 if Steps 1-4 are available
  • Step 5 is ALWAYS flagged as uncertain — never presented as fact
  • NEVER assume or fabricate. If you cannot find an answer, say "I don't know" or "I couldn't find documentation for this". Inventing APIs, patterns, or behaviors causes cascading failures across design → tasks → implementation. Uncertainty is always preferable to fabrication.

Output Behavior

Model guidance: After completing lightweight tasks (validation, state updates, session handoff), naturally mention once that such tasks work well with faster/cheaper models. Track in STATE.md under Preferences to avoid repeating. For heavy tasks (brownfield mapping, complex design), briefly note the reasoning requirements before starting.

Be conversational, not robotic. Don't interrupt workflow—add as a natural closing note. Skip if user seems experienced or has already acknowledged the tip.

Code Analysis

Use available tools with graceful degradation. See code-analysis.md.

how to use tlc-spec-driven

How to use tlc-spec-driven on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tlc-spec-driven
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/tech-leads-club/agent-skills --skill tlc-spec-driven

The skills CLI fetches tlc-spec-driven from GitHub repository tech-leads-club/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tlc-spec-driven

Reload or restart Cursor to activate tlc-spec-driven. Access the skill through slash commands (e.g., /tlc-spec-driven) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Accelerate Code Development

Use skill to generate boilerplate code, refactor legacy code, and write tests faster

Example

Generate React component with TypeScript types, styled-components, and comprehensive test suite in minutes

Reduce development time by 40-60% for repetitive coding tasks

Code Review Automation

Systematically review code for bugs, security issues, and style violations

Example

Analyze pull requests for common anti-patterns, suggest performance improvements, flag security vulnerabilities

Catch 70%+ of code issues before human review, improve code quality

Debug Complex Issues

Trace errors through stack traces and identify root causes faster

Example

Analyze error logs, suggest probable causes, recommend fixes with code examples

Cut debugging time by 30-50%, especially for unfamiliar codebases

Learn New Technologies

Get explanations, examples, and best practices for unfamiliar frameworks

Example

Understand Next.js app router, learn Rust ownership, grasp Kubernetes concepts with practical examples

Accelerate learning curve by 2-3x, reduce onboarding time for new tech stacks

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill installation support
  • Basic understanding of programming concepts and version control (Git)
  • Code editor or IDE for testing generated code (VS Code, JetBrains, etc.)
  • Test environment separate from production for validating skill outputs

Time Estimate

15-30 minutes to install and see first useful output

Installation Steps

  1. 1.Install the skill using provided installation command
  2. 2.Verify skill is loaded in Claude Desktop (check ~/.claude/skills directory)
  3. 3.Test skill with simple prompt: 'Help me review this code snippet'
  4. 4.Gradually increase complexity: code generation → refactoring → architecture advice
  5. 5.Review all generated code before committing to repository
  6. 6.Iterate on prompts to improve output quality and relevance
  7. 7.Share effective prompts with team for consistency

Common Pitfalls

  • Blindly trusting generated code without testing—always run tests and manual review
  • Not providing enough context about your project structure and coding standards
  • Expecting perfection on first generation—iteration and refinement are normal
  • Sharing proprietary code or API keys in prompts—maintain confidentiality
  • Over-relying on skill for critical security or business logic code
  • Skipping documentation of why AI-generated code was chosen over alternatives

Best Practices

✓ Do

  • +Always review and test AI-generated code before merging
  • +Provide clear context: language, framework, coding standards, constraints
  • +Use for boilerplate, tests, docs—areas where mistakes are easily caught
  • +Iterate on prompts: start broad, refine with specific requirements
  • +Combine AI suggestions with human judgment and domain expertise
  • +Document successful prompt patterns for team reuse
  • +Keep version control so you can rollback if needed
  • +Use skill for learning and exploration, not production-critical features initially

✗ Don't

  • Don't commit AI code without thorough testing and review
  • Don't expose sensitive code, credentials, or proprietary algorithms
  • Don't use for security-critical code (auth, crypto, payments) without expert review
  • Don't skip peer review process just because AI generated it
  • Don't assume code follows your team's conventions—verify
  • Don't let junior developers skip learning fundamentals by relying solely on AI
  • Don't ignore compiler warnings or test failures in generated code

💡 Pro Tips

  • Describe desired patterns explicitly: 'Use async/await, avoid callbacks'
  • Ask for alternatives: 'Show 3 approaches to solve this, with tradeoffs'
  • Request explanations: 'Explain why this approach is better than X'
  • Use skill for 70% generation + 30% manual refinement for best results
  • Build a prompt library for common patterns (API endpoints, components, tests)
  • Pair program with AI: describe problem → review solution → iterate → refine

When to Use This

✓ Use When

Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.

✗ Avoid When

Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.

Learning Path

  1. 1Start with simple tasks: generate functions, write tests, explain code
  2. 2Progress to code review: analyze PRs, suggest improvements
  3. 3Advanced: architectural decisions, refactoring strategies, performance optimization
  4. 4Expert: use for exploring new paradigms, researching best practices, mentoring juniors

Integration

  • VS Code
  • JetBrains IDEs
  • Cursor
  • GitHub Copilot
  • Git workflows

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.548 reviews
  • Aisha Liu· Dec 28, 2024

    We added tlc-spec-driven from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ganesh Mohane· Dec 24, 2024

    tlc-spec-driven is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Naina Tandon· Dec 16, 2024

    Useful defaults in tlc-spec-driven — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Meera Iyer· Dec 12, 2024

    tlc-spec-driven has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Jin Farah· Dec 8, 2024

    I recommend tlc-spec-driven for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Evelyn Thompson· Nov 19, 2024

    Useful defaults in tlc-spec-driven — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Nov 15, 2024

    tlc-spec-driven fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Evelyn Martin· Nov 7, 2024

    We added tlc-spec-driven from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Maya Flores· Nov 3, 2024

    Solid pick for teams standardizing on skills: tlc-spec-driven is focused, and the summary matches what you get after install.

  • Meera Thompson· Oct 26, 2024

    tlc-spec-driven reduced setup friction for our internal harness; good balance of opinion and flexibility.

showing 1-10 of 48

1 / 5