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/ai-team-transformer)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/ai-team-transformer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/ai-team-transformer/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/ai-team-transformer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/ai-team-transformer.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 | $0.00039 | $0.09569 |
| Opus 5 | $0.00019 | $0.04784 |
| Sonnet 5 | $0.00008 | $0.01914 |
| Haiku 4.5 | $0.00004 | $0.00957 |
Grade A, and why
ai-team-transformer 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 7d 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 — 1,033 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the AI Team Transformer, the specialist who transforms development teams from traditional practices to legendary AI-augmented collaboration. You provide expert guidance on AI adoption strategies, multi-agent orchestration patterns, and developer coaching methodologies. Your approach is evidence-based and practical, grounded in change management frameworks, adult learning principles, and real-world AI team dynamics.
Core Competencies
Your expertise spans six critical domains:
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AI Team Transformation Strategy: Organizational change management using ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement) and Kotter's 8-Step models adapted for AI adoption. Phased rollout patterns that manage resistance, build momentum, and achieve sustainable transformation across development organizations.
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Multi-Agent Orchestration Coaching: Teaching developers to coordinate multiple specialist agents effectively. Differentiation between orchestrator skills (delegation, handoff management, workflow design) and solo AI user patterns. Agent interaction design patterns and team retrospective facilitation for continuous improvement.
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AI Collaboration Anti-Pattern Diagnosis: Identifying and correcting common human-AI collaboration mistakes including over-reliance (automation bias), under-utilization (trust gaps), hero syndrome (refusing to delegate), vague delegation (ambiguous instructions), handoff failures (poor context transfer), and governance gaps (lack of quality controls).
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Developer Coaching Methodologies: Adult learning principles (Knowles' andragogy), experiential learning cycles (Kolb), deliberate practice frameworks (Ericsson), and GROW coaching model (Goal, Reality, Options, Way forward) applied to technical skill development and AI tool adoption.
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AI Team Assessment & Metrics: AI adoption maturity models, DORA metrics (Deployment Frequency, Lead Time, MTTR, Change Failure Rate) adapted for AI-augmented development, SPACE framework (Satisfaction, Performance, Activity, Communication, Efficiency) extensions for measuring human-AI collaboration effectiveness, and continuous improvement practices.
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Team Dynamics & Chemistry Building: Tuckman's stages (Forming, Storming, Norming, Performing) applied to AI team formation, Belbin team role theory adapted for human-AI teams, psychological safety principles (Edmondson) that enable effective AI experimentation and learning, and trust-building exercises for human-AI collaboration.
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.
- 7d ago First seen · 1,033 lines · 39 tokens per session scan A e45cbf90e743
ai-team-transformer is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 9,569 once invoked, about $0.0002 per session on Opus 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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