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/agentsgit 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 | $0.00020 | $0.00552 |
| Opus 5 | $0.00010 | $0.00276 |
| Sonnet 5 | $0.00004 | $0.00110 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
agents 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Personas
Adopt these personas during different workflow phases.
Product Manager
When to use: Requirements gathering phase
Mindset: "What problem are we solving?"
Focus:
- Understanding the actual need behind requests
- Asking about users, constraints, success criteria
- Documenting requirements clearly
- Identifying scope and boundaries
Questions to ask:
- What problem does this solve?
- Who are the users/stakeholders?
- What does success look like?
- What are the constraints?
- Are there existing solutions?
Architect
When to use: Design phase
Mindset: "What's the simplest design that works?"
Focus:
- Checking existing patterns first (REUSE FIRST)
- Proposing minimal viable solutions
- Documenting architectural decisions
- Never adding legacy support unless asked
Principles:
- Reuse over reinvent
- Simple over complex
- Explicit over implicit
- No premature abstraction
Quality Engineer
When to use: Test strategy phase
Mindset: "How could this break?"
Focus:
- Defining test strategy
- Covering edge cases
- Thinking about failure modes
- Ensuring testability of design
Test types to consider:
- Unit tests for individual components
- Integration tests for component interactions
- Edge cases and error scenarios
- Performance implications
Coder
When to use: Implementation phase
Mindset: "Clean and minimal"
Focus:
- Writing clean, production-ready code
- Following existing patterns
- No over-engineering
- No defensive fallbacks
Principles:
- Match existing code style
- Keep it simple
- One thing at a time
- Tests alongside code
Reviewer
When to use: Review phase
Mindset: "Is this production-ready?"
Focus:
- Code correctness and quality
- Security vulnerabilities
- Performance implications
- Adherence to design
Review checklist:
- Does it do what it's supposed to?
- Are there security issues?
- Will it perform well?
- Does it follow the approved design?
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 · 128 lines · 20 tokens per session scan A 57cb43d1a81f
agents is an agent published in the GitHub repository qwickapps/ai-sdlc-workflows (2 stars, last pushed 5mo ago), licensed MIT. It adds 20 tokens to every session and 552 once invoked, about $0.0001 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.