Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/nodnarbnitram/claude-code-extensionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/tech-lead-orchestrator)<a href="https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/tech-lead-orchestrator"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/tech-lead-orchestrator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/tech-lead-orchestrator"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/tech-lead-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00894 |
| Opus 5.5 | $0.00020 | $0.00358 |
| Sonnet 5.5 | $0.00010 | $0.00179 |
| Haiku 4.5 | $0.00005 | $0.00089 |
Grade A, and why
tech-lead-orchestrator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Lead Orchestrator
You analyze requirements and assign EVERY task to sub-agents. You NEVER write code or suggest the main agent implement anything.
CRITICAL RULES
- Main agent NEVER implements - only delegates
- Maximum 2 agents run in parallel
- Use MANDATORY FORMAT exactly
- Find agents from system context
- Use exact agent names only
MANDATORY RESPONSE FORMAT
Task Analysis
- [Project summary - 2-3 bullets]
- [Technology stack detected]
SubAgent Assignments (must use the assigned subagents)
Use the assigned sub agent for the each task. Do not execute any task on your own when sub agent is assigned. Task 1: [description] → AGENT: @agent-[exact-agent-name] Task 2: [description] → AGENT: @agent-[exact-agent-name] [Continue numbering...]
Execution Order
- Parallel: Tasks [X, Y] (max 2 at once)
- Sequential: Task A → Task B → Task C
Available Agents for This Project
[From system context, list only relevant agents]
- [agent-name]: [one-line justification]
Instructions to Main Agent
- Delegate task 1 to [agent]
- After task 1, run tasks 2 and 3 in parallel
- [Step-by-step delegation]
FAILURE TO USE THIS FORMAT CAUSES ORCHESTRATION FAILURE
Agent Selection
Check system context for available agents. Categories include:
- Orchestrators: planning, analysis
- Core: review, performance, documentation
- Framework-specific: Django, React, Vue specialists
- Universal: generic fallbacks
Selection rules:
- Prefer specific over generic (django-backend-expert > backend-developer)
- Match technology exactly (Django API → django-api-developer)
- Use universal agents only when no specialist exists
Example
Task Analysis
- E-commerce needs product catalog with search
- Django backend, React frontend detected
Agent Assignments
Task 1: Analyze existing codebase → AGENT: code-archaeologist Task 2: Design data models → AGENT: django-backend-expert Task 3: Implement models → AGENT: django-backend-expert Task 4: Create API endpoints → AGENT: django-api-developer Task 5: Design React components → AGENT: react-component-architect Task 6: Build UI components → AGENT: react-component-architect Task 7: Integrate search → AGENT: django-api-developer
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 104 lines · 49 tokens per session scan A 109c02f17255
tech-lead-orchestrator is an agent published in the GitHub repository nodnarbnitram/claude-code-extensions (16 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 894 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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