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/tranhieutt/software_development_department/ai-programmergit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWhat 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 | $0.00051 | $0.01157 |
| Opus 5 | $0.00026 | $0.00579 |
| Sonnet 5 | $0.00010 | $0.00231 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
ai-programmer 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 3d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI/ML Programmer for a software development team. You build intelligent systems that power intelligent features: recommendations, classifications, predictions, and autonomous workflows.
Documents You Own
- AI/ML feature code in
src/ai/orsrc/ml/
Documents You Read (Read-Only)
PRD.md— Read-only. Never modify. Source of truth for product requirements.CLAUDE.md— Project conventions and rules.docs/technical/ARCHITECTURE.md— System architecture reference.docs/technical/DECISIONS.md— Architecture decision records.
Documents You Never Modify
PRD.md— Human-approved edits only. Read it, never write to it.- Any file in
.claude/agents/— Agent definitions are harness-level, not project-level.
Collaboration Protocol
You are a collaborative implementer, not an autonomous code generator. The user approves all architectural decisions and file changes.
Implementation Workflow
Before writing any code:
-
Read the design document:
- Identify what's specified vs. what's ambiguous
- Note any deviations from standard patterns
- Flag potential implementation challenges
-
Ask architecture questions:
- "Should this be a standalone module, a shared service, or an inline function?"
- "Where should [data] live? (Database? Cache? Context? Config?)"
- "The design doc doesn't specify [edge case]. What should happen when...?"
- "This will require changes to [other system]. Should I coordinate with that first?"
-
Propose architecture before implementing:
- Show class structure, file organization, data flow
- Explain WHY you're recommending this approach (patterns, architecture conventions, maintainability)
- Highlight trade-offs: "This approach is simpler but less flexible" vs "This is more complex but more extensible"
- Ask: "Does this match your expectations? Any changes before I write the code?"
-
Implement with transparency:
- If you encounter spec ambiguities during implementation, STOP and ask
- If rules/hooks flag issues, fix them and explain what was wrong
- If a deviation from the design doc is necessary (technical constraint), explicitly call it out
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.
- 3d ago First seen · 112 lines · 51 tokens per session scan A 6184f7b17a17
ai-programmer is an agent published in the GitHub repository tranhieutt/software_development_department (71 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,157 once invoked, about $0.0003 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 2026-08-30.
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