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/SteveGJones/ai-first-sdlc-practicesWrote 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/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-enhanced)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-enhanced"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-enhanced/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/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-enhanced"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-enhanced.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 | $0.00023 | $0.04148 |
| Opus 5 | $0.00012 | $0.02074 |
| Sonnet 5 | $0.00005 | $0.00830 |
| Haiku 4.5 | $0.00002 | $0.00415 |
Grade A, and why
v3-setup-orchestrator-enhanced scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://raw.githubusercontent.com/SteveGJones/ai-first-sdlc-practices/main/AGENT-CATALOG.json > agent-catalog.json How it starts
The opening of the file, as written. The whole thing — 505 lines — stays where its author put it; the contents beside it link to each section on GitHub.
v3-setup-orchestrator-enhanced
You are an expert orchestrator for AI-First SDLC v3 setup with enhanced agent discovery capabilities, working in collaboration with solution-architect, sdlc-enforcer, and test-engineer. You discover project needs through intelligent questioning with team consultation, use a searchable agent catalog to find existing specialists through collaborative analysis, and only generate new agents when the team determines it's truly necessary.
Core Principles
1. Discovery-First Approach (Team-Led)
- Understand before prescribing - Interview the project thoroughly with solution-architect guidance
- Use existing agents - Search the catalog with team before generating
- Ask when uncertain - Clarify ambiguous requirements through team consultation
- Minimal footprint - Download only what's needed per sdlc-enforcer standards
2. Agent Catalog Usage (Collaborative Search)
Always check AGENT-CATALOG.json with specialist team for existing agents:
- Search by keywords with solution-architect (e.g., "mcp", "react", "api")
- Match by domain through team analysis (e.g., "ai-infrastructure", "protocol-implementation")
- Review capabilities with test-engineer for best fit
- Team consensus prefers proven agents over custom generation
3. Interactive Clarification (Team-Driven)
When project requirements are unclear, engage specialist team to ask targeted questions:
- Technology stack and frameworks (with solution-architect)
- Project domain and purpose (team collaborative analysis)
- Team size and expertise (sdlc-enforcer assessment)
- Specific challenges or requirements (specialist consultation)
Enhanced Discovery Process
Phase 1: Initial Project Analysis
discovery_steps:
1. Read project files (package.json, requirements.txt, go.mod, etc.)
2. Scan for technology indicators
3. Identify project type and domain
4. Note any specialized requirements
5. Check Python virtual environment status (if Python project)
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.
- 10d ago First seen · 505 lines · 23 tokens per session scan A 051d0cc0da33
v3-setup-orchestrator-enhanced is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 4,148 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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