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docmind-ai-llm: Instructions file for Codex

AGENTS.md

docmind-ai-llm AGENTS.md is an instructions file for Codex, OpenCode from BjornMelin/docmind-ai-llm. It costs 5,757 tokens per session, scanned A, original, MIT.

A set of project instructions for DocMind AI, a Streamlit application for working with documents. It describes the app’s pages, document processing, search, agents, storage, telemetry, and developer commands.

In plain words
What is it for?
Use it when developing or maintaining the document chat app, including uploads, OCR, PDF processing, search, analytics, agent workflows, persistence, settings, and checks.
Why use it?
It gives coding agents a map of the codebase and identifies the documents and architecture decisions that should guide changes. It also records the standard setup, run, and verification commands.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md.

This is BjornMelin/docmind-ai-llm's own configuration. It tells Codex and OpenCode how to work on docmind-ai-llm itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything docmind-ai-llm configures →

not rated 155repo 2mo ago A scan 5,757 tokens original MIT
View source ↗ BjornMelin/docmind-ai-llm
Reuse

Borrowing it

Nothing to install: this file belongs to BjornMelin/docmind-ai-llm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/BjornMelin/docmind-ai-llm/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/BjornMelin/docmind-ai-llm

Made for: Codex, OpenCode.

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Per session 5,757 This file is loaded in full into every session.
When invoked 5,757 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.05757 $0.05757
Opus 5.5 $0.02303 $0.02303
Sonnet 5.5 $0.01151 $0.01151
Haiku 4.5 $0.00576 $0.00576

Measured 2mo ago against content hash a1c3cd70a375, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

docmind-ai-llm 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 2mo 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.

AGENTS.md · 397 lines

How it starts

The opening of the file, as written. The whole thing — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DocMind AI: Agent instructions

Purpose

Repo guardrails for contributors/automation. Keep aligned with code, pyproject.toml, and docs under docs/specs/ + docs/developers/adrs/.

Layout

  • app.py: Streamlit entrypoint
  • src/app.py: Streamlit app module (imported by app.py)
  • src/pages/: UI pages (chat/documents/analytics/settings)
  • src/config/: settings + integration wiring
  • src/processing/: ingestion, OCR, PDF page exports
  • src/retrieval/: router, hybrid retrieval, reranking, GraphRAG helpers
  • src/agents/: LangGraph coordinator (graph-native supervisor via StateGraph)
  • src/persistence/: snapshots, hashing, locking, chat DB
  • src/telemetry/ + src/utils/telemetry.py: OTEL + JSONL events
  • src/prompting/templates/: bundled prompt templates and presets
  • docs/specs/ + docs/developers/adrs/: specs/ADRs (source-of-truth docs)
  • scripts/ + tools/: benchmarks, health checks, documentation gates, and model pull

Quick commands with uv

  • Setup: uv sync && cp .env.example .env
  • Run: uv run streamlit run app.py (or ./scripts/run_app.sh)
  • Env: prefer uv run ... (uses the project env, typically .venv).
  • Verify (batch): after a batch of edits, run lint/type on touched paths + focused tests.
    • Format (all): uv run ruff format .
    • Lint (all): uv run ruff check .
    • Type (paths): uv run pyright --threads 4 <paths>
    • Tools-only (when tools/ changed): uv run pyright --threads 4 tools
    • Tests (focused): uv run pytest <tests/...> -vv --no-cov (or -k <expr> for a narrow slice)
  • Verify (final): before finishing the task/prompt, run non-mutating full lint/type checks, then full tests: uv run ruff format --check . && uv run ruff check . && uv run pyright --threads 4 && uv run pytest tests/unit tests/integration -q --no-cov
  • Tests (unit): uv run pytest tests/unit -q --no-cov
  • Tests (integration): uv run pytest tests/integration -q --no-cov
  • Tests (fast/full): uv run pytest tests/unit tests/integration -q --no-cov
  • Tests (GPU): uv run pytest -m requires_gpu --no-cov
  • Coverage: uv run pytest tests/unit tests/integration -q --cov=src --cov-branch --cov-report=term-missing --cov-report=html:htmlcov --cov-report=xml:coverage.xml --cov-report=json:coverage.json --cov-fail-under=80 --junitxml=junit.xml
  • Coverage report: uv run python scripts/check_coverage.py --collect --report --html
  • Parser benchmark: uv run python scripts/benchmark_parsing.py --generate-minimal-fixtures --output cache/benchmarks/parsing/results.json
  • GPU check: uv run python scripts/test_gpu.py --quick
  • Prefetch default retrieval and Docling layout models: uv run python tools/models/pull.py --all --cache_dir ./models_cache --parser-defaults --parser-cache-dir ./cache/models; RapidOCR models come from its locked wheel.
  • spaCy model (opt): uv run python -m spacy download en_core_web_sm
  • Review triage: uv run python scripts/analyze_github_reviews.py --json-file <path> (or set DOCMIND_REVIEW_JSON)

Read the full file on GitHub · 397 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. 2mo ago First seen · 397 lines · 5,757 tokens per session scan A a1c3cd70a375

Subscribe to this mod's changes

docmind-ai-llm AGENTS.md is an instructions file published in the GitHub repository BjornMelin/docmind-ai-llm (155 stars, last pushed 2mo ago), licensed MIT. It adds 5,757 tokens to every session, about $0.0230 per session on Opus 5.5. 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.

Data and AIDocumentsLangChainai-agentsdocument-analysishybrid-searchllama-cpplocal-llm
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