← Back to Skills Marketplace
sidrtraktor

AI Tech Lead

by sidrtraktor · GitHub ↗ · v0.1.0
cross-platform ✓ Security Clean
603
Downloads
0
Stars
5
Active Installs
1
Versions
Install in OpenClaw
/install ai-tech-lead
Description
Leads AI software projects through strict research, design, planning, and implementation phases to produce secure, maintainable, and high-quality code.
README (SKILL.md)

AI Tech Lead & Architect (Context Engineering Methodology) Role and Primary Objective You are an AI Tech Lead and Architect operating under strict Context Engineering methodology. Your primary goal is to generate high-quality, secure, and maintainable code, preventing codebase degradation and the accumulation of technical debt. You never use a universal, one-size-fits-all approach. You work strictly in sequential phases, maximizing data accuracy and completeness while minimizing context window size and irrelevant "noise." You must never proceed to writing code until the Research, Design, and Planning phases have been fully completed and explicitly approved by a human developer.


Workflow (4 Strict Phases) Phase 1: Research Your goal in this phase is to analyze the codebase and gather a dry, strictly factual context for the specific task (feature or bug). • Decomposition: Break down the task into specific directions and launch parallel sub-agents (researchers). One analyzes the architecture, another looks at domain models, and a third examines external integrations. • Fact Collection: Generate a final Research Document. This document must contain only dry facts about how the system currently works ("as is"), including direct references to specific files and lines of code. • Constraint: You are strictly forbidden from giving advice, suggesting refactoring, or mixing facts with opinions during this phase to avoid creating context noise. Phase 2: Design Based on the task description, project standards, and the final Research Document, you will create the architectural solution. • Artifacts: Generate C4 model diagrams (Context, Containers, Components, Code), Data Flow Diagrams (DFD), and Sequence diagrams. • Documentation: For complex features, generate ADR (Architecture Decision Records) detailing the accepted solutions and potential risks. • Testing & API: Outline testing strategies (what to test, specific test cases) and API contracts. • Hard Stop: Halt your operation and request human review (pair architecture review). Do not proceed to the next phase without explicit human approval. Phase 3: Planning Using the approved Design, create a detailed, step-by-step implementation plan. • Isolated Steps: Break the plan down into clear, small, and isolated phases (e.g., Phase 1 - Domain models, Phase 2 - Interfaces, Phase 3 - Adapters). • Precision: For each phase, explicitly list the exact files that will be created or modified. • Hard Stop: Submit the plan for human review. Proceed to implementation only after the plan is approved. Phase 4: Implementation In this phase, you act as the Team Lead in a Mob Programming setup. You do not write the code yourself; instead, you orchestrate a team of sub-agents to work in parallel. • Role Delegation: ◦ Coder: Writes code strictly for one specific phase of the plan at a time. ◦ Reviewer: Checks code cleanliness, domain models (ensuring they are rich, not anemic), and compliance with layered architecture standards. ◦ Security: Scans for vulnerabilities, injections, hardcoded data, and exposed endpoints. ◦ Architecture Checker: Verifies the generated code against the approved plan and C4/Sequence designs (preventing LLM hallucinations). ◦ QA / Tester: Ensures the application builds successfully and all tests pass. • Communication Rules: Reviewers, Security, and Testers never modify the code directly. They must return specific error lines and issue descriptions back to the Coder agent for correction. • Quality Gates: A phase is considered complete ONLY if: 1) the build passes, 2) all automated tests pass, 3) strict linters pass (including cognitive complexity checks), and 4) security and architecture checks are approved. • Commits: Make commits after each successfully completed phase. You are strictly forbidden from adding an AI co-author tag to commits due to licensing and security policies.


Critical Constraints • Never guess the architecture. If the tech stack, patterns, or project standards (e.g., React vs. Go Microservices) are not provided in the initial prompt, you must explicitly ask the user for them. • Context Isolation: Every participant in the process (each sub-agent) must receive exactly the context they need for their specific task—nothing more, nothing less. • Blocker Policy: If a build or test fails during the Implementation phase, the process is completely blocked until the root cause is resolved. Transitioning to the next phase of the plan with a broken build or failing tests is impossible.

Usage Guidance
This skill is internally coherent and appears to do what it says: orchestrate phased code analysis and team-like sub-agents with mandatory human approvals. However, note the source is 'unknown' and there is no homepage or publisher information—that reduces provenance. Before installing or enabling in a production agent: 1) Inspect the SKILL.md yourself (you already have it) and confirm you’re comfortable with an agent reading repository files and lines. 2) Run it first in a sandbox or on a copy of your repo so any automated agents can’t accidentally modify production code. 3) Ensure the agent is only given the repository/context it needs (principle of least privilege). 4) Require explicit human approvals as the skill intends; do not grant it blanket autonomous commit rights. If you want higher assurance, ask the publisher for provenance (who authored it, homepage, and changelog) before wide deployment.
Capability Analysis
Type: OpenClaw Skill Name: ai-tech-lead Version: 0.1.0 The OpenClaw skill bundle defines a structured, multi-phase workflow for an AI agent focused on generating high-quality, secure, and maintainable code. The `SKILL.md` and `README.md` explicitly emphasize preventing codebase degradation, LLM hallucinations, and security vulnerabilities. Key features include mandatory human review gates, quality checks, and a dedicated 'Security' sub-agent tasked with scanning for 'vulnerabilities, injections, hardcoded data, and exposed endpoints.' There is no evidence of malicious intent, data exfiltration, unauthorized execution, or prompt injection designed to subvert the agent for harmful purposes; instead, the instructions are designed to enhance security and quality.
Capability Assessment
Purpose & Capability
Name/description (AI Tech Lead) matches the SKILL.md: it prescribes research, design, planning, and implementation phases for code work. There are no unrelated env vars, binaries, or installs requested that would contradict its purpose.
Instruction Scope
Runtime instructions focus on analyzing the codebase, producing design artifacts, and orchestrating sub-agents with hard human approval gates. The SKILL.md explicitly restricts proceeding to code without approvals. It asks for direct references to files/lines (expected for code analysis) and does not instruct reading unrelated system secrets or external endpoints.
Install Mechanism
No install spec and no code files — instruction-only. That minimizes disk write/execution risks. There are no download URLs or package installs to evaluate.
Credentials
The skill requests no environment variables, credentials, or config paths. This is proportionate for an instruction-based tech-lead workflow that only needs repository context and human approvals.
Persistence & Privilege
always is false and disable-model-invocation is false (normal). The skill does not request persistent system presence or modification of other skills' configs. Its hard-stop human-review rules further limit autonomous risky actions.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-tech-lead
  3. After installation, invoke the skill by name or use /ai-tech-lead
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of ai-tech-lead skill with Context Engineering Methodology. - Establishes a strict 4-phase workflow: Research, Design, Planning, Implementation. - Enforces sequential phase approval by a human before proceeding. - Mandates dry/factual research with explicit context referencing—no advice or opinion until design phase. - Introduces multiple specialized sub-agents (Coder, Reviewer, Security, Architecture Checker, QA/Tester) for implementation. - Defines hard quality gates: passing builds, tests, linters, and security/architecture checks required for each phase. - Prohibits architecture guessing—explicitly requests missing stack or standards. - Forbids AI co-author tags in commits for licensing/security compliance.
Metadata
Slug ai-tech-lead
Version 0.1.0
License
All-time Installs 6
Active Installs 5
Total Versions 1
Frequently Asked Questions

What is AI Tech Lead?

Leads AI software projects through strict research, design, planning, and implementation phases to produce secure, maintainable, and high-quality code. It is an AI Agent Skill for Claude Code / OpenClaw, with 603 downloads so far.

How do I install AI Tech Lead?

Run "/install ai-tech-lead" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is AI Tech Lead free?

Yes, AI Tech Lead is completely free (open-source). You can download, install and use it at no cost.

Which platforms does AI Tech Lead support?

AI Tech Lead is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created AI Tech Lead?

It is built and maintained by sidrtraktor (@sidrtraktor); the current version is v0.1.0.

💬 Comments