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/jonathan-vella/apex-accelerator/pythongit clone --depth 1 https://github.com/jonathan-vella/apex-acceleratorWhat 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 | $0.00559 | $0.00559 |
| Opus 5 | $0.00280 | $0.00280 |
| Sonnet 5 | $0.00112 | $0.00112 |
| Haiku 4.5 | $0.00056 | $0.00056 |
Grade A, and why
apex-accelerator python.instructions.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 2d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Guidelines
Instructions for writing clean, consistent Python in this repository. Target Python 3.14 (latest stable) with Ruff for linting and formatting.
Project Context
Python is used for two purposes in this repo:
- Architecture diagrams —
diagramslibrary scripts inagent-output/and.github/skills/ - Utility scripts — validation tooling and diagram verification
Style & Formatting
- Formatter: Ruff (
ruff format) — double quotes, space indentation - Linter: Ruff with rules: E, W, F, I, B, C4, UP, SIM
- Line length: 120 characters (matches project-wide setting)
- Imports: sorted by isort rules via Ruff — stdlib, third-party, first-party
- Quotes: double quotes for strings
- Type hints: use for function signatures;
pyproject.tomlsetsbasictype checking
Package Management
- Use
uv(Astral) as the package manager — installed in devcontainer - Root dependencies in
requirements.txt:diagrams,matplotlib,pillow,pytest,ruff
Diagram Scripts
Follow the existing pattern for architecture diagram generation:
"""Brief description of what the diagram shows."""
from diagrams import Cluster, Diagram
from diagrams.azure.compute import AppServices
from diagrams.azure.network import FrontDoors
with Diagram("Diagram Title", show=False, filename="output-name", direction="TB"):
with Cluster("Resource Group"):
# Resources...
pass
- Always set
show=Falseto prevent auto-opening - Use
direction="TB"(top-to-bottom) for consistency - Group resources in
Clusterblocks matching Azure resource groups - Set explicit
filenameparameter to control output location
Conventions
- Use
snake_casefor functions, variables, and modules - Use
PascalCasefor classes - Use
UPPER_SNAKE_CASEfor constants - Prefer f-strings over
.format()or%formatting - Use pathlib
Pathfor new code — existing scripts may useos.path - Use context managers (
with) for file and network operations
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.
- 2d ago First seen · 69 lines · 559 tokens per session scan A 33b12b828b3e
apex-accelerator python.instructions.md is an instructions file published in the GitHub repository jonathan-vella/apex-accelerator (50 stars, last pushed 5d ago), licensed MIT. It adds 559 tokens to every session, about $0.0028 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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