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 skills/nelson820125/iforgeai/python-engineernpx skills add nelson820125/iforgeai --skill python-engineergit clone --depth 1 https://github.com/nelson820125/iforgeaiWhat 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 | $0.00082 | $0.03303 |
| Opus 5 | $0.00041 | $0.01651 |
| Sonnet 5 | $0.00016 | $0.00661 |
| Haiku 4.5 | $0.00008 | $0.00330 |
Grade A, and why
python-engineer 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output Language Rule
Read output_language from .ai/context/workflow-config.md. Write ALL deliverables and code comments in that language. If the file is absent or the field is unset, default to en-US.
DB Approach Rule
Read db_approach from .ai/context/workflow-config.md before starting any database-related implementation:
database-first(default when unset): The authoritative schema is defined in.ai/temp/db-init.sqlproduced by the DBA. You must implement SQLAlchemy ORM models and repository code that matches this schema exactly. Do NOT usealembic upgrade headto initialise the database from scratch — the database is initialised from the DBA's SQL script. Alembic is used only for subsequent schema changes.code-first: You are responsible for driving the schema via Alembic migrations. Workflow:- Read
.ai/temp/db-design.md(DBA design document) as the reference for field types, constraints, indexes, and default values - Implement SQLAlchemy ORM models faithfully according to the design document
- Run
alembic revision --autogenerate -m "{description}"to generate the migration - Run
alembic upgrade headto apply it — this replacesdb-init.sql - Document each migration task in the WBS and work log with its revision ID and purpose
- Read
Phase Mode
This skill operates in two modes depending on how it is invoked:
| Mode | Trigger | Task | Output |
|---|---|---|---|
/contract |
digital-team Phase 5a |
Define full API contract schemas in api-contract.md |
.ai/temp/api-contract.md (fully detailed, ready for frontend review) |
/develop (default) |
digital-team Phase 6b, or standalone invocation |
Implement backend code based on api-contract.md + wbs.md |
Source code + work log |
Contract mode (/contract) rules:
- Read
.ai/temp/api-contract.md(architect's skeleton) and.ai/temp/wbs.md - Fill in Request schema (Pydantic models), Response schema, HTTP status codes, and validation rules for each endpoint
- Do NOT write implementation code in this mode — output is documentation only
- The completed contract is reviewed by the frontend engineer before development begins
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 · 248 lines · 0 tokens per session scan A e3fe2bd88ad0
python-engineer is a skill published in the GitHub repository nelson820125/iforgeai (8 stars, last pushed 4mo ago), licensed MIT. It adds 82 tokens to every session and 3,303 once invoked, about $0.0004 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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