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 instructions/apache/camel/agents-mdgit clone --depth 1 https://github.com/apache/camelWhat 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 | $0.07481 | $0.07481 |
| Opus 5 | $0.03741 | $0.03741 |
| Sonnet 5 | $0.01496 | $0.01496 |
| Haiku 4.5 | $0.00748 | $0.00748 |
Grade A, and why
camel 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 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 — 628 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apache Camel - AI Agent Guidelines
Guidelines for AI agents working on this codebase.
Project Info
Apache Camel is an integration framework supporting routing rules in Java, XML and YAML DSLs.
- Version: 4.23.0-SNAPSHOT
- Java: 17+
- Build: Maven 3.9.12+
AI Agent Rules of Engagement
These rules apply to ALL AI agents working on this codebase.
Attribution
- All AI-generated content (GitHub PR descriptions, review comments, JIRA comments) MUST clearly identify itself as AI-generated and mention the human operator. Example: "Claude Code on behalf of [Human Name]"
- Never guess or hallucinate the operator's name. Always determine it programmatically:
- Use
gh api /user --jq '.login'to get the authenticated GitHub username. - If for any reason the lookup fails, omit the name rather than guessing.
- Use
- AI coding agents MUST be configured to add co-authorship trailers to commits
(e.g.,
Co-authored-by). For Claude Code, enable this via the attribution settings.
PR Volume
- An agent MUST NOT open more than 10 PRs per day per operator to ensure human reviewers can keep up.
- Prioritize quality over quantity — fewer well-tested PRs are better than many shallow ones.
Git branch
- An agent MUST NEVER push commits to a branch it did not create.
- If a contributor's PR needs changes, the agent may suggest changes via review comments, but must not push to their branch without explicit permission.
- An agent MUST use its own fork to push branches instead of the main apache/camel repository.
Pushing directly to the upstream repo fills it with stale branches that waste CI time and disk
space for all contributors. A
pr-cleanup-branches.ymlworkflow deletes non-protected branches when their PR is closed, but using a fork avoids the problem entirely. - An agent must provide a useful name for the git branch. It should contain the global topic and issue number if possible.
- After a Pull Request is merged or rejected, the branch MUST be deleted. The
pr-cleanup-branches.ymlworkflow handles this automatically for branches pushed directly toapache/camel, but agents using forks must delete their remote branch manually.
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 · 628 lines · 7,481 tokens per session scan A c275c79a8803
camel AGENTS.md is an instructions file published in the GitHub repository apache/camel (6,306 stars, last pushed 2d ago), licensed Apache-2.0. It adds 7,481 tokens to every session, about $0.0374 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.
Other instructions, from other repositories
camel-quarkus AGENTS.md
Instructions for apache/camel-quarkus, covering apache camel quarkus - ai agent guidelines, project overview, what not to do, technology stack and repository structure.
camel-spring-boot AGENTS.md
Instructions for apache/camel-spring-boot, covering apache camel spring boot - ai agent guidelines, project info, ai agent rules of engagement, attribution and pr volume.
camel-quarkus-examples AGENTS.md
Instructions for apache/camel-quarkus-examples, covering apache camel quarkus examples - ai agent guidelines, project info, rules of engagement (essentials), repository structure and anatomy of an example.
camel-spring-boot-examples AGENTS.md
AGENTS.md instructions for apache/camel-spring-boot-examples, covering apache camel spring boot examples - ai agent guidelines, project info, rules of engagement (essentials), repository structure and anatomy of an example.
camel-spring-boot CLAUDE.md
Instructions for apache/camel-spring-boot, covering apache camel spring boot - ai agent guidelines, project info, ai agent rules of engagement, attribution and pr volume.
camel-quarkus CLAUDE.md
Instructions for apache/camel-quarkus, a project described as: Apache Camel Quarkus.