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.
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).
| 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:
| Scope | What | Specify | Design | Tasks | Execute |
|---|---|---|---|---|---|
| Small | ≤3 files, one sentence | Quick mode — skip pipeline entirely | - | - | - |
| Medium | Clear feature, <10 tasks | Spec (brief) | Skip — design inline | Skip — tasks implicit | Implement + verify |
| Large | Multi-component feature | Full spec + requirement IDs | Architecture + components | Full breakdown + dependencies | Implement + verify per task |
| Complex | Ambiguity, new domain | Full spec + discuss gray areas | Research + architecture | Breakdown + parallel plan | Implement + 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:
- Initialize project → PROJECT.md + ROADMAP.md
- For each feature → Specify → (Design) → (Tasks) → Execute (depth auto-sized)
Existing codebase:
- Map codebase → 7 brownfield docs
- Initialize project → PROJECT.md + ROADMAP.md
- 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:
| Activity | Delegate? | Why |
|---|---|---|
| Research (design phase, brownfield mapping) | Yes | Research output is large; only the summary matters to the main context |
| Implementing a task | Yes | File reads, edits, test output consume context; only the result matters |
Parallel [P] tasks | Yes (one per task) | The only way to actually run tasks in parallel |
Sequential tasks with no [P] | Yes | Keeps implementation artifacts out of the main context |
| Planning, task creation, validation reports | No | These require the full accumulated context to be coherent |
| Quick mode tasks | No | Too 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 Pattern | Reference |
|---|---|
| Initialize project, setup project | project-init.md |
| Create roadmap, plan features | roadmap.md |
| Map codebase, analyze existing code | brownfield-mapping.md |
| Document concerns, find tech debt, what's risky | concerns.md |
| Record decision, log blocker, add todo | state-management.md |
| Pause work, end session | session-handoff.md |
| Resume work, continue | session-handoff.md |
Feature-level (auto-sized):
| Trigger Pattern | Reference |
|---|---|
| Specify feature, define requirements | specify.md |
| Discuss feature, capture context, how should this work | discuss.md |
| Design feature, architecture | design.md |
| Break into tasks, create tasks | tasks.md |
| Implement task, build, execute | implement.md |
| Validate, verify, test, UAT, walk me through it | validate.md |
| Quick fix, quick task, small change, bug fix | quick-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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tlc-spec-driven from GitHub repository tech-leads-club/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install the skill using provided installation command
- 2.Verify skill is loaded in Claude Desktop (check ~/.claude/skills directory)
- 3.Test skill with simple prompt: 'Help me review this code snippet'
- 4.Gradually increase complexity: code generation → refactoring → architecture advice
- 5.Review all generated code before committing to repository
- 6.Iterate on prompts to improve output quality and relevance
- 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▌
- 1Start with simple tasks: generate functions, write tests, explain code
- 2Progress to code review: analyze PRs, suggest improvements
- 3Advanced: architectural decisions, refactoring strategies, performance optimization
- 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.
Ratings
4.5★★★★★48 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