agentic-dart AGENTS.md

A project guide for Agentic-DART, an autonomous digital-forensics and incident-response system. It explains the repository layout, recommended commands, and testing expectations for coding agents working on the project.

In plain words
What is it for?
Use it when modifying or reviewing Agentic-DART code, locating its audit, forensic-tool, agent, correlation, and playbook packages, or running the project's checks.
Why use it?
It gives an unfamiliar coding agent the project context and operating rules needed to work safely. It also points to the required test and verification process.

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/juwon1405/agentic-dart/agents-md
Clone the repo
git clone --depth 1 https://github.com/Juwon1405/agentic-dart

Made for: Codex, OpenCode.

Per session 1,035 This file is loaded in full into every session.
When invoked 1,035 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 $0.01035 $0.01035
Opus 5 $0.00517 $0.00517
Sonnet 5 $0.00207 $0.00207
Haiku 4.5 $0.00103 $0.00103

Measured 3d ago against content hash a7ddde1cd1a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentic-dart 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 3d 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 · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Agent Guide for Agentic-DART

Concise operational guide for Codex and other coding agents. For the full rationale see CLAUDE.md. When the two overlap, both are authoritative; CLAUDE.md carries the longer explanation.

No hard-coded counts live in this file on purpose — they drift on every ship and would violate our measure-don't-guess rule. Run the commands below for live values.

Project summary

Autonomous DFIR agent. A senior-analyst loop calls typed, read-only MCP tools (two layers: native pure-Python functions + SIFT-tool adapters), logs every call in a SHA-256-chained audit, and emits a findings report. Python 3.10+, MIT, SANS FIND EVIL! 2026.

Repository map

  • dart_audit/ — SHA-256-chained JSONL audit logger (Python package)
  • dart_mcp/ — custom MCP server, typed read-only forensic functions (Python package)
  • dart_agent/ — iteration controller, deterministic + live loops, auth (Python package)
  • dart_corr/ — DuckDB cross-artifact correlation engine (Python package)
  • dart_playbook/ — senior-analyst YAML playbooks; data, no pyproject.toml, loaded by path
  • examples/case-studies/{self-evaluation,external-evaluation}/case-NN/ (README + truth.json + evidence_root), demos
  • scripts/install.sh, benchmark/, scripts/eval/demo.py, generate_realistic_evidence.py
  • tests/ — main pytest suite; dart_corr/tests/ — correlation-engine tests

Preferred commands

export PYTHONPATH=dart_audit/src:dart_mcp/src:dart_agent/src:dart_corr/src

# Full suite — every test must pass
python3 -m pytest tests/ dart_corr/tests/ -q

# Focused
python3 tests/test_mcp_surface.py        # tool-surface drift (asserts the exact set)
python3 tests/test_mcp_bypass.py         # adversarial / read-only guard
python3 -m pytest dart_corr/tests/ -q    # correlation engine

# Live tool count / native / SIFT split
PYTHONPATH=dart_mcp/src python3 -c "import dart_mcp; t=dart_mcp._REGISTRY; \
s=[k for k in t if k.startswith('sift_')]; print(len(t), len(t)-len(s), len(s))"

# Offline demo (deterministic, no API key)
bash examples/demo-run.sh

# Accuracy (deterministic; recall must stay 1.0, hallucination 0)
python3 -m scripts.eval.demo

Read the full file on GitHub · 86 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. 3d ago First seen · 86 lines · 1,035 tokens per session scan A a7ddde1cd1a7

Subscribe to this mod's changes

agentic-dart AGENTS.md is an instructions file published in the GitHub repository Juwon1405/agentic-dart (9 stars, last pushed 2mo ago), licensed MIT. It adds 1,035 tokens to every session, about $0.0052 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.

Related

Other instructions, from other repositories