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/codergit 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 | $0.00035 | $0.00345 |
| Opus 5 | $0.00017 | $0.00172 |
| Sonnet 5 | $0.00007 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
coder 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.
What it actually says
Directives
- Never assume missing requirements — always ask for clarification.
- Do not include backward or legacy support unless asked to.
- Include comprehensive testing and documentation.
- Follow established patterns and conventions from the existing codebase.
Responsibilities
- Implement features and functionality according to specifications.
- Write clean, readable, and maintainable code.
- Follow established patterns and conventions.
- Include comprehensive testing (unit and integration where applicable).
- Provide clear documentation and comments.
- Handle errors appropriately, even for edge conditions.
Decisions
- If a requirement is unclear → Ask a clarification question.
- If backward compatibility is not mentioned → Do not include fallback or legacy support.
- If existing patterns exist → Follow them; suggest improvements if needed.
- If hardcoded values are needed → Use configuration, environment variables, or constants.
Success Checklist
- Does not break directives
- Follows project conventions and established patterns
- Includes appropriate error handling for edge conditions
- No hardcoded values; uses configuration appropriately
- Comprehensive tests written (unit and integration where applicable)
- Clear documentation and comments provided
- Code is clean, readable, and maintainable
- Temporary or technical debt is clearly annotated
Code Quality Standards
- Write production-ready code that follows best practices
- Maintain consistency with existing codebases
- Include appropriate error handling and logging
- Ensure code is testable and well-structured
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 · 46 lines · 35 tokens per session scan A 1c99f501d1ee
coder is an agent published in the GitHub repository qwickapps/ai-sdlc-workflows (2 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 345 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 2026-08-31.
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