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/chrisgleissner/llm-tools/agents-mdgit clone --depth 1 https://github.com/chrisgleissner/llm-toolsWrote 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/chrisgleissner/llm-tools/agents-md)<a href="https://agentmods.dev/instructions/chrisgleissner/llm-tools/agents-md"><img src="https://agentmods.dev/badge/instructions/chrisgleissner/llm-tools/agents-md.svg" alt="Measured on agentmods" 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 | $0.11870 | $0.11870 |
| Opus 5 | $0.05935 | $0.05935 |
| Sonnet 5 | $0.02374 | $0.02374 |
| Haiku 4.5 | $0.01187 | $0.01187 |
Grade A, and why
llm-tools 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 4d 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Scope
This repo contains small Linux Python CLIs for Codex, Claude Code, GitHub Copilot, and Kilo Code CLI:
llm-usage— show local usage/quota for each provider, optionally through a local Unix-socket sampler service.llm-scheduler— submit a prompt to a provider CLI once usage data says it is usable (optionally waking/suspending around a scope reset).ralph-robin— keep using one configured provider until it is exhausted, then rotate to the next provider and delegate launch/suspend behavior tollm-scheduler. Holds an OS idle inhibitor for the whole run so a desktop idle timer cannot suspend the machine mid-work, and when every provider is rate-limited it sleeps the machine itself via a verified RTC wake (see Suspend/wake reliability below).llm-sleep-soak— repeatedly suspend and wake the machine using the exact production suspend path to prove sleep/resume is reliable on this hardware. Real-hardware test; cannot run in CI.llm_tools/common.py— shared helpers (provider readers, normalization, time/reset formatting, subprocess execution, usage decisions, PTY capture, wake diagnostics, and common CLI plumbing: argument validation, run-dir logging, prompt loading, argv/JSON conversion).llm_tools/usage_service.py— local-onlyllm-usagesampler service (Unix socket, latest snapshot, append-only history; systemd user service / launchd LaunchAgent install helpers).llm_tools/capacity.py— genericProviderId,CapacityKind,CapacityScope,ProviderSnapshot, andUsageDecisiondataclasses plus thedecide/validate_scope/scope_pacehelpers. All provider-specific reader code lives outside this module.llm_tools/providers/kilo.py— Kilo Code CLI adapter (parser forkilo stats --days <MTD>output, env-var fallback, command construction).llm_tools/providers/minimax.py— MiniMax adapter (parser formmx quota show --output jsonoutput, env-var fallback, command construction).- Python modules:
llm_tools/usage.py,llm_tools/scheduler.py,llm_tools/ralph_robin.py,llm_tools/sleep_soak.py,llm_tools/copilot_refresh.py, and package markerllm_tools/__init__.py. - Public direct-run command files:
llm-usage,llm-scheduler,ralph-robin,llm-sleep-soak. - Regression tests:
tests/with pytest and fake provider commands. - Test helpers:
tests/conftest.py; main suites:tests/test_contracts.py,tests/test_additional_paths.py,tests/test_capacity.py,tests/test_kilo.py,tests/test_minimax.py,tests/test_ralph_kilo.py,tests/test_zai.py. - Project/package config:
pyproject.toml. - Import/test bootstrap:
sitecustomize.py. - CI:
.github/workflows/test.yml. - User docs:
README.md. - Local planning/work logs:
PLANS.md,WORKLOG.md. - Runtime data root:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools, one subdirectory per tool. Legacy~/.cache/llm-usage,~/.cache/llm-scheduler, and~/.cache/ralph-robindirs are auto-migrated bymigrate_legacy_cache_dirsinllm_tools/common.py. - Usage cache and samples log:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/llm-usage(claude-status.json,claude-usage-api.json,llm-usage.log) - Usage service history/logs:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/llm-usage/service(latest.json,history.jsonl, service logs) - Usage service socket/pid:
${XDG_RUNTIME_DIR:-/tmp/llm-tools-$UID}(llm-usage.sock,llm-usage.pid) so Unix socket paths stay below platform length limits. - Copilot background refresh helper:
llm_tools/copilot_refresh.py, launched byread_copilotfor detached cache refreshes. - Scheduler run logs:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/llm-scheduler/logs - Ralph Robin run logs:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/ralph-robin/logs - Ralph Robin state:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/ralph-robin/state.json - Suspend cycle ledger (durable, fsync'd; shared by ralph-robin and the soak):
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/ralph-robin/suspend-ledger.jsonl - Sleep-soak run logs:
${XDG_CACHE_HOME:-$HOME/.cache}/llm-tools/llm-sleep-soak/logs
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.
- 4d ago First seen · 345 lines · 11,870 tokens per session scan A d624ca9d3ad6
llm-tools AGENTS.md is an instructions file published in the GitHub repository chrisgleissner/llm-tools (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 11,870 tokens to every session, about $0.0593 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.
Other instructions, from other repositories
agent-usage-desktop AGENTS.md
Instructions for hongshuo-wang/agent-usage-desktop, covering agents.md, build & run, desktop app (tauri), macos arm64 example and macos x8664.
agenttop CLAUDE.md
Instructions for vicarious11/agenttop, covering agenttop — ai usage dashboard & optimizer, project overview, architecture, layers and key directories.
open-agent-kit copilot-instructions.md
Instructions for goondocks-co/open-agent-kit, covering instructions for ai contributors, source of truth (hard rules), required workflow, non-negotiables and top anchors (shorthand).
codex-self-hosted-web-monitor AGENTS.md
AGENTS.md instructions for CableZa/codex-self-hosted-web-monitor, a project described as: Self-hosted web dashboard for monitoring local OpenAI Codex usage, sessions, credits, cache efficiency, and account limits.
neohive CLAUDE.md
Claude Code instructions for fakiho/neohive, covering claude.md, what this is, commands, install in any project (auto-detects cli type) and launch the web dashboard.
neohive GEMINI.md
Gemini CLI instructions for fakiho/neohive, covering gemini.md, what this is, commands, install in any project (auto-detects cli type) and launch the web dashboard.