Borrowing it
Nothing to install: this file belongs to depalmar/ai-dfir-toolkit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/depalmar/ai-dfir-toolkit/main/CLAUDE.mdgit clone --depth 1 https://github.com/depalmar/ai-dfir-toolkitWrote 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/depalmar/ai-dfir-toolkit/claude-md)<a href="https://agentmods.dev/instructions/depalmar/ai-dfir-toolkit/claude-md"><img src="https://agentmods.dev/badge/instructions/depalmar/ai-dfir-toolkit/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/depalmar/ai-dfir-toolkit/claude-md"><img src="https://agentmods.dev/badge/instructions/depalmar/ai-dfir-toolkit/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.07802 | $0.07802 |
| Opus 5.5 | $0.03121 | $0.03121 |
| Sonnet 5.5 | $0.01560 | $0.01560 |
| Haiku 4.5 | $0.00780 | $0.00780 |
Grade A, and why
ai-dfir-toolkit CLAUDE.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 25d 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 — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project context
Read this before working in artifacts/. It exists so a session starts with the
rules already loaded instead of rediscovering them.
What this is
artifacts/ is a machine-readable catalog of the endpoint traces left by AI
coding agents, local LLM runtimes, agentic workflow engines, and MCP components.
It answers: what did this tool leave on the host, what does that prove, and in
what order do I collect it.
It is deliberately not a living-off-the-land abuse catalog. LOLBAS and LOLRMM
answer "what can this be misused to do." This one is for the person doing the
acquisition, which is why every artifact carries forensic_value and
evidence_type, and every entry carries a collection block.
Commands
Run everything from artifacts/. On Windows use python, on macOS/Linux
python3; the scripts themselves are cross-platform (pathlib throughout) and
write LF endings on every platform so the generated feeds stay byte-identical.
python scripts/validate.py # schema + sigma + confidence gate
python scripts/validate_techniques.py # ATLAS/ATT&CK ids exist (--refresh re-pins)
python scripts/cve_status.py # KEV claims match the pinned CISA catalog (--refresh)
python scripts/sync_mappings.py # MAPPINGS index tables from the pins (--check gates)
python scripts/intel_graph.py --check # actors + case studies, schemas and cross-refs
python scripts/intel_graph.py --export # intel.json, STIX 2.1 bundle, Maltego CSVs
python scripts/intel_watch.py --dry-run # what the weekly intel watcher would raise
python scripts/forbidden_terms.py # FORBIDDEN_TERMS env/secret; never prints the terms
python scripts/readme_counts.py # README Contents block (--check gates it)
python scripts/normalize.py # collapse vocabulary drift
python scripts/export.py # regenerate docs/api feeds
python scripts/export_forensicartifacts.py # Plaso / GRR / Timesketch format
python scripts/export_kape.py # KAPE targets (--check validates, writes nothing)
python scripts/export_velociraptor.py # Velociraptor artifacts (--check likewise)
python scripts/normalize_notes.py # note style (--check reports, writes nothing)
python scripts/data_sources.py # telemetry coverage (--check audits only)
python scripts/verify_host.py # check catalogued paths on THIS machine
python ../collectors/gen_credential_targets.py # collector targets, also CI-gated
python scripts/build_site.py --check # site data contract, writes nothing
python scripts/build_site.py # regenerate docs/site (CI does this)
python ../artifacts/scripts/validate_mappings.py # run this one from the repo root
python ../skills/agent-artifact-catalog/scripts/new_entry.py "Tool Name"
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.
- 25d ago Changed · +109 lines · +1,789 tokens per session 4d9e95fcde1f
- 1mo ago First seen · 421 lines · 6,013 tokens per session scan A 89807c758821
ai-dfir-toolkit CLAUDE.md is an instructions file published in the GitHub repository depalmar/ai-dfir-toolkit (129 stars, last pushed 6d ago), licensed Apache-2.0. It adds 7,802 tokens to every session, about $0.0312 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.