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 instructions/simonchoi034/image-gen-mcp/agents-mdgit clone --depth 1 https://github.com/simonChoi034/image-gen-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/simonchoi034/image-gen-mcp/agents-md)<a href="https://agentmods.dev/instructions/simonchoi034/image-gen-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/simonchoi034/image-gen-mcp/agents-md.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.01067 | $0.01067 |
| Opus 5 | $0.00534 | $0.00534 |
| Sonnet 5 | $0.00213 | $0.00213 |
| Haiku 4.5 | $0.00107 | $0.00107 |
Grade A, and why
image-gen-mcp AGENTS.md 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 5d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
Project Structure & Module Organization
src/— main package (MCP server). Key modules:main.py(entry),schema.py(Pydantic I/O),engines/(providers: OpenAI, Gemini, Vertex/Imagen, DALL·E),utils/,shard/(enums/constants),settings.py(env config).tests/— pytest tests (add new tests here).scripts/— helper workflows (feature scaffolding, plan checks).templates/,docs/,memory/— specs, docs, and working notes.
Build, Test, and Development Commands
- Run server:
python -m src.main - With uv:
uv run python -m src.main(installs/uses lockfile). - Lint:
ruff check .Format:black . - Type-check:
pyright - Tests:
pytest -q - Feature scaffolding:
./scripts/create-new-feature.sh "image upscaling"
Coding Style & Naming Conventions
- Python 3.12, 4-space indent, type hints required for new/changed code.
- Modules:
lower_snake_case.py; Classes:CapWords; Functions/vars:snake_case. - Pydantic v2 models in
schema.pydefine the public contract; keep field names and enums stable. - Run
ruffandblackbefore pushing; keep diffs minimal and cohesive.
No inline imports
- Do not use function-local (inline) imports anywhere in the codebase. All imports must appear at module top-level.
- Exception: imports guarded by
if TYPE_CHECKING:for typing-only imports are allowed.
Rationale: top-level imports avoid surprising runtime side-effects, make static analysis and packaging reliable, and ensure dependency graphs are clear. If an import causes circular import issues, prefer one of:
- move type-only imports into a
if TYPE_CHECKING:block, or - refactor module boundaries (e.g., move shared types into
schema.py), or - use lazy-loading helpers in a single, well-documented location (
engines/factory.pyuses lazy import resolution via_load_engine_class).
Example (bad):
def my_tool():
# avoid this
from .schema import ImageToolStructured
...
Example (good):
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.
- 5d ago First seen · 90 lines · 1,067 tokens per session scan A 0fda10763e34
image-gen-mcp AGENTS.md is an instructions file published in the GitHub repository simonChoi034/image-gen-mcp (17 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 1,067 tokens to every session, about $0.0053 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.
Other instructions, from other repositories
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nano-banana-image-skill CLAUDE.md
Instructions for Emily2040/nano-banana-image-skill, covering nano banana image skill, start here, repo intent, editing rules and preferred answer shape.
nano-banana-image-skill GEMINI.md
Instructions for Emily2040/nano-banana-image-skill, covering nano banana image skill, additional context, guidance and gemini api/runtime notes.
gpt-image-1-mcp CLAUDE.md
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