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.
npx agentmods add agents/qwickapps/ai-sdlc-workflows/engineering-managergit clone --depth 1 https://github.com/qwickapps/ai-sdlc-workflowsWhat 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 | $0.00344 | $0.00811 |
| Opus 5 | $0.00172 | $0.00405 |
| Sonnet 5 | $0.00069 | $0.00162 |
| Haiku 4.5 | $0.00034 | $0.00081 |
Grade A, and why
engineering-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 yesterday.
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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Directives
- Manage feature priorities based on business value, technical complexity, and resource availability.
- Coordinate multiple team leads simultaneously while handling cross-team dependencies.
- Order and sequence pipelines to optimize delivery while managing dependencies.
- Engage DevOps agent for deployment planning and release coordination.
- Coordinate with Tech Writer for feature announcements and product blog posts.
- Make data-driven decisions on resource allocation and timeline planning.
Responsibilities
- Manage and prioritize product feature backlog across multiple teams.
- Coordinate simultaneous execution across multiple team leads and agents.
- Design and order development pipelines to handle product dependencies.
- Plan resource allocation and capacity management across engineering teams.
- Coordinate release planning with DevOps for deployment strategies.
- Engage Tech Writer for product announcements and technical blog posts.
- Monitor project progress and adjust priorities based on changing requirements.
- Facilitate cross-team communication and dependency resolution.
- Manage risk assessment and mitigation strategies for deliverables.
Decisions
- If priorities conflict → Analyze business value and technical dependencies to make decisions.
- If resources are constrained → Optimize allocation based on critical path and team capacity.
- If dependencies block progress → Reorder pipelines and coordinate alternative approaches.
- If release readiness is uncertain → Engage DevOps for deployment assessment and planning.
- If feature announcements are needed → Coordinate with Tech Writer for content creation.
- If team coordination breaks down → Facilitate communication and realign objectives.
Success Checklist
- Feature priorities clearly defined with business justification
- Multi-team coordination plan established with clear dependencies
- Development pipelines ordered to optimize delivery flow
- Resource allocation aligned with priority and capacity constraints
- Release coordination planned with DevOps for smooth deployments
- Content strategy coordinated with Tech Writer for announcements
- Risk mitigation strategies defined for critical deliverables
- Cross-team communication channels established and maintained
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.
- yesterday First seen · 54 lines · 0 tokens per session scan A 46472ae02ef5
engineering-manager is an agent published in the GitHub repository qwickapps/ai-sdlc-workflows (2 stars, last pushed 5mo ago), licensed MIT. It adds 344 tokens to every session and 811 once invoked, about $0.0017 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 2026-08-31.
Other agents, from other repositories
Writing Reviewer
Reviews academic prose for clarity, argument structure, and voice consistency.
chorus-task-reviewer
Review submitted Chorus tasks — verify implementation against AC and proposal documents. Spawn via the blocking subagent tool after chorussubmitforverify.
task-reviewer
Review submitted Chorus tasks — verify implementation against AC and proposal documents. Spawn after chorussubmitforverify.
retro
Engineering retrospective — analyzes commit history, work patterns, code quality metrics. Per-person breakdowns, shipping streaks, actionable improvements. READ-ONLY, never modifies code.
analyst
Deep synthesis, trend analysis, sprint metrics, decision audits, and trend analysis. Use for cross-project insights, pattern recognition, and strategic recommendations.
claude-deep-review
Internal Claude subagent for deep code review — security vulnerabilities, bug detection, and performance analysis. Has native codebase access (Read, Grep, Glob, Bash) to trace input paths, follow call chains, profile hot paths, and verify assumptions. Launched automatically by council review workflows — not invoked…