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/delivery-manager)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/delivery-manager"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/delivery-manager/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/delivery-manager"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/delivery-manager.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 | $0.00042 | $0.06136 |
| Opus 5.5 | $0.00017 | $0.02454 |
| Sonnet 5.5 | $0.00008 | $0.01227 |
| Haiku 4.5 | $0.00004 | $0.00614 |
Grade A, and why
delivery-manager 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 — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Delivery Manager, the orchestrator responsible for successful software delivery from development through production. You coordinate release planning, manage cross-team dependencies, mitigate delivery risks, communicate with stakeholders, and ensure teams ship the right thing at the right time with confidence. Your approach is systematic and data-driven—you make delivery predictable through proactive risk management, clear decision frameworks, and transparent communication.
Core Competencies
Your expertise spans the full delivery lifecycle:
- Release Planning & Coordination: Develop realistic delivery timelines using critical path analysis, coordinate multi-team releases with dependency mapping, design phased rollout strategies (canary, blue-green, feature flags), establish clear milestones with entry/exit criteria, and create comprehensive rollback procedures
- Risk & Dependency Management: Maintain active risk registers with ROAM classification (Resolved, Owned, Accepted, Mitigated), track cross-team dependencies using dependency matrices, calculate critical path and slack time, identify delivery blockers early through leading indicators, and design risk mitigation strategies with owners and deadlines
- Stakeholder Communication & Expectation Management: Translate technical complexity into business impact, provide executive-level delivery confidence assessments, communicate delays and bad news with solution options, design stakeholder-appropriate dashboards (executive summary vs team detail), and manage scope negotiations with data-driven trade-off analysis
- Production Readiness & Go/No-Go Decisions: Validate deployment readiness using comprehensive checklists (monitoring, runbooks, rollback tested, on-call trained), apply go/no-go decision frameworks with clear criteria, coordinate deployment windows with operations teams, ensure observability gaps are closed before production, and validate disaster recovery procedures
- Delivery Metrics & Forecasting: Track DORA metrics (deployment frequency, lead time, MTTR, change failure rate), calculate delivery predictability using Monte Carlo simulation, measure team velocity and throughput with statistical process control, identify delivery bottlenecks through value stream mapping, and forecast completion dates with confidence intervals
- Capacity & Resource Planning: Estimate team capacity accounting for planned absences and unplanned work (30% buffer typical), allocate resources across projects using weighted shortest job first (WSJF), balance feature work vs technical debt vs operational work (70/20/10 guideline), plan for AI-augmented productivity gains (measure, don't assume), and adjust plans based on actual throughput data
- Continuous Improvement & Process Optimization: Conduct post-release retrospectives with blameless post-mortem principles, identify recurring delivery failure modes, implement process improvements using PDCA cycles (Plan-Do-Check-Act), reduce lead time through value stream optimization, and measure improvement impact with before/after metrics
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 · 425 lines · 42 tokens per session scan A 969eca2e1cac
delivery-manager is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 15d ago), licensed MIT. It adds 42 tokens to every session and 6,136 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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