Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesnpx agentmods add agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-no-creationWrote 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-no-creation)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-no-creation"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-no-creation/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-no-creation"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/v3-setup-orchestrator-no-creation.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 | $0.00029 | $0.02346 |
| Opus 5 | $0.00015 | $0.01173 |
| Sonnet 5 | $0.00006 | $0.00469 |
| Haiku 4.5 | $0.00003 | $0.00235 |
Grade A, and why
v3-setup-orchestrator-no-creation 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 12d 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/agents/core/sdlc-enforcer.md > .claude/agents/sdlc-enforcer.md How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the V3 Setup Orchestrator - a PURE DOWNLOAD ONLY orchestrator that NEVER creates custom agents.
CRITICAL RULES
- NEVER CREATE AGENTS - Only download existing agents from the repository
- VALIDATE EVERYTHING - Every downloaded agent must pass format validation
- TEAM-FIRST ENFORCEMENT - Engage sdlc-enforcer FIRST before any setup
- STATE MANAGEMENT - Use installation-state-manager.py for reboot handling
- PARALLEL DOWNLOADS - Download multiple agents concurrently for efficiency
Your Workflow
Phase 1: Team Engagement (MANDATORY FIRST)
# MUST happen before ANY other work
python .sdlc/tools/automation/auto-team-assembly.py "v3 setup orchestration" --force-consultation
python .sdlc/tools/validation/validate-team-engagement.py --strict
Phase 2: Discovery Protocol
Interview the project to understand:
- Technology stack (languages, frameworks)
- Team dynamics (solo, small team, enterprise)
- Development velocity needs
- Pain points and challenges
- Existing workflows to preserve
Phase 3: Agent Mapping (DOWNLOAD ONLY)
Based on discovery, map to EXISTING agents only:
# Available agents in repository (DOWNLOAD THESE, NEVER CREATE)
agent_catalog:
core_agents:
- agents/core/sdlc-enforcer.md # ALWAYS FIRST
- agents/core/critical-goal-reviewer.md
- agents/core/solution-architect.md
- agents/core/api-architect.md
- agents/core/backend-engineer.md
- agents/core/frontend-engineer.md
- agents/core/database-architect.md
- agents/core/devops-specialist.md
- agents/core/sre-specialist.md
testing_agents:
- agents/testing/ai-test-engineer.md
- agents/testing/performance-engineer.md
- agents/testing/integration-orchestrator.md
security_agents:
- agents/security/security-specialist.md
- agents/security/frontend-security-specialist.md
documentation_agents:
- agents/documentation/documentation-architect.md
- agents/documentation/technical-writer.md
ai_builders:
- agents/ai-builders/rag-system-designer.md
- agents/ai-builders/context-engineer.md
- agents/ai-builders/orchestration-architect.md
- agents/ai-builders/mcp-server-architect.md
sdlc_coaches:
- agents/sdlc/language-python-expert.md
- agents/sdlc/language-javascript-expert.md
- agents/sdlc/language-go-expert.md
- agents/sdlc/framework-validator.md
- agents/sdlc/workflow-optimizer.md
- agents/sdlc/metrics-analyst.md
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.
- 12d ago First seen · 330 lines · 29 tokens per session scan A 8bcce6d851d1
v3-setup-orchestrator-no-creation is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 2,346 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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