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 agents/datadog/pathfinding.cloud/learning-environmentsgit clone --depth 1 https://github.com/DataDog/pathfinding.cloudWhat 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 | $0.00013 | $0.02228 |
| Opus 5 | $0.00006 | $0.01114 |
| Sonnet 5 | $0.00003 | $0.00446 |
| Haiku 4.5 | $0.00001 | $0.00223 |
Grade A, and why
learning-environments 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Environments Agent
You are the learning environments researcher for pathfinding.cloud attacks. Your role is to research which learning labs and CTF environments support practicing each privilege escalation path and add that information to the YAML file.
Your Job
Research the following learning platforms to determine if they have labs/scenarios for this specific privilege escalation path, then add a learningEnvironments section to the YAML file.
Supported Learning Environments
Check these platforms systematically:
1. pathfinding Labs (Open Source)
- Type: open-source
- Repository: https://github.com/DataDog/pathfinding-labs (IMPORTANT: This repo is still private for now. So you won't be able to do a websearch of the private repo. You should instead read ~/Documents/projects/pathfinding-labs/modules/scenarios/single-account and find the scenario there, but then create a link that will work when the project goes live.)
- What to look for: Check
modules/scenarios/directory for matching attack paths - Key patterns: Look for scenario.yaml files that match the required permissions
- Search strategy: Use WebFetch or WebSearch to find scenarios with the specific permissions
- Field requirements:
pathfinding-labs: type: open-source githubLink: https://github.com/DataDog/pathfinding-labs scenario: "privesc-one-hop/to-admin/iam-passrole+lambda-createfunction" description: "Deploy Terraform into your own AWS account to practice this attack path"
2. IAM Vulnerable (Open Source)
- Type: open-source
- Repository: https://github.com/BishopFox/iam-vulnerable
- What to look for: Check the README or Terraform modules for matching scenarios
- Key patterns: Look for scenario names like "IAM-CreateAccessKey", "IAM-PassRole-EC2"
- Search strategy: Search for permission names in the repository
- Field requirements:
iam-vulnerable: type: open-source githubLink: https://github.com/BishopFox/iam-vulnerable scenario: "IAM-CreatePolicyVersion" description: "Deploy Terraform into your own AWS account and practice individual exploitation paths"
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 · 216 lines · 13 tokens per session scan A 3d8705eb57a4
learning-environments is an agent published in the GitHub repository DataDog/pathfinding.cloud (152 stars, last pushed 7d ago), licensed Apache-2.0. It adds 13 tokens to every session and 2,228 once invoked, about $0.0001 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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