Borrowing it
Nothing to install: this file belongs to sampleXbro/agentsmesh. 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/sampleXbro/agentsmesh/master/AGENTS.mdgit clone --depth 1 https://github.com/sampleXbro/agentsmeshWrote 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/samplexbro/agentsmesh/agents-md)<a href="https://agentmods.dev/instructions/samplexbro/agentsmesh/agents-md"><img src="https://agentmods.dev/badge/instructions/samplexbro/agentsmesh/agents-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/samplexbro/agentsmesh/agents-md"><img src="https://agentmods.dev/badge/instructions/samplexbro/agentsmesh/agents-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 | $0.01738 | $0.01738 |
| Opus 5.5 | $0.00695 | $0.00695 |
| Sonnet 5.5 | $0.00348 | $0.00348 |
| Haiku 4.5 | $0.00174 | $0.00174 |
Grade A, and why
agentsmesh 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agentsmesh CLAUDE.md — 94% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentsMesh Generation Contract
NEVER edit generated files (CLAUDE.md, AGENTS.md, .claude/, .cursor/, .github/copilot-instructions.md, and other tool outputs) — agentsmesh generate overwrites them. Edit the canonical source in .agentsmesh instead, or agentsmesh.yaml to change targets and features, then run agentsmesh generate.
Lessons (BLOCKING)
Graph .agentsmesh/lessons/lessons.json is canonical; never hand-edit it. Manual: lessons skill.
Recall: before every file edit or state-changing command, MUST run agentsmesh lessons query --file <path> --cmd <command> --session auto and obey matches; at task start, ALSO run agentsmesh lessons query --keyword "<task terms>" --always --session auto for conceptual + universal rules no path/command names. Pure-read commands and recall itself are exempt.
Capture: after any failure, user correction, regression, wrong assumption, useful surprise, repeated friction, or non-obvious fix, MUST self-critique and run agentsmesh lessons add "<imperative rule>" --topic <id> --trigger-file <glob> --evidence <sha|lesson-id>.
Before final: report Lesson: captured <id> or Lesson: none. No recall/capture gate = task incomplete. No shell: use lessons_query / lessons_add.
Operational Guidelines
Workflow
Planning
- Enter plan mode for any non-trivial task (3+ steps or architectural decisions). Write plan to
tasks/todo.md. - If something goes sideways, STOP and re-plan — don't keep pushing.
- Check in before starting implementation. Mark items complete as you go.
Subagents
- Use subagents liberally for research, exploration, and parallel analysis. One tack per subagent.
Verification
- Never mark a task complete without proving it works — run tests, check logs, demonstrate correctness.
- After every feature/story: use
post-feature-qaskill (.agentsmesh/skills/post-feature-qa/). - Ask yourself: "Would a staff engineer approve this?"
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.
- 1mo ago Changed · -118 tokens per session e31197bb6b81
- 1mo ago First seen · 99 lines · 1,856 tokens per session scan A abe071fd784f
agentsmesh AGENTS.md is an instructions file published in the GitHub repository sampleXbro/agentsmesh (26 stars, last pushed 1mo ago), licensed MIT. It adds 1,738 tokens to every session, about $0.0070 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 2026-09-03.
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).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.