DocsGPT AGENTS.md

Project instructions for developing DocsGPT, covering its entry points, architecture, and local setup checks. They recommend red-green TDD, a cycle of writing a failing test, making it pass, and then improving the code.

In plain words
What is it for?
They help contributors read the right project guidance, reuse existing Python, PostgreSQL, and Redis setups, and decide when MongoDB is actually needed.
Why use it?
They help prevent unnecessary environment changes and make development follow the project's existing prerequisites and workflow.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/arc53/docsgpt/agents-md
Clone the repo
git clone --depth 1 https://github.com/arc53/DocsGPT

Made for: Codex, OpenCode.

Per session 2,175 This file is loaded in full into every session.
When invoked 2,175 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.02175 $0.02175
Opus 5 $0.01087 $0.01087
Sonnet 5 $0.00435 $0.00435
Haiku 4.5 $0.00217 $0.00217

Measured yesterday against content hash 5454678efb68, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

DocsGPT 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 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.

AGENTS.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

  • Read CONTRIBUTING.md before making non-trivial changes.
  • For day-to-day development and feature work, follow the development-environment workflow rather than defaulting to setup.sh / setup.ps1.
  • Avoid using the setup scripts during normal feature work unless the user explicitly asks for them. Users configure .env usually.
  • Try to follow red/green TDD

Check existing dev prerequisites first

For feature work, do not assume the environment needs to be recreated.

  • Check whether the user already has a Python virtual environment such as venv/ or .venv/.
  • Check whether Postgres is already running and reachable via POSTGRES_URI (the canonical user-data store).
  • Check whether Redis is already running.
  • Reuse what is already working. Do not stop or recreate Postgres, Redis, or the Python environment unless the task is environment setup or troubleshooting.

MongoDB is not required for the default install. It is only needed if the user opts into the Mongo vector-store backend (VECTOR_STORE=mongodb) or is running the one-shot scripts/db/backfill.py to migrate existing user data from the legacy Mongo-based install. In those cases, pymongo is available as an optional extra, not a core dependency.

Normal local development commands

Use these commands once the dev prerequisites above are satisfied.

Backend

source .venv/bin/activate  # macOS/Linux
uv pip install -r application/requirements.txt  # or: pip install -r application/requirements.txt

Run the API. For local dev, prefer the ASGI entrypoint under uvicorn — it serves the whole app, matches production, and hot-reloads:

uvicorn application.asgi:asgi_app --host 0.0.0.0 --port 7091 --reload

flask --app application/app.py run --host=0.0.0.0 --port=7091 is a faster inner loop (quick startup, the Werkzeug interactive debugger), but it serves only the WSGI Flask app and omits the routes mounted on the ASGI shell in application/asgi.py:

Read the full file on GitHub · 212 lines

Changes

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.

  1. yesterday First seen · 212 lines · 2,175 tokens per session scan A 5454678efb68

Subscribe to this mod's changes

DocsGPT AGENTS.md is an instructions file published in the GitHub repository arc53/DocsGPT (18,230 stars, last pushed 3d ago), licensed MIT. It adds 2,175 tokens to every session, about $0.0109 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.