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.
git clone --depth 1 https://github.com/conorbronsdon/agent-context-osWrote 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/commands/conorbronsdon/agent-context-os/setup)<a href="https://agentmods.dev/commands/conorbronsdon/agent-context-os/setup"><img src="https://agentmods.dev/badge/commands/conorbronsdon/agent-context-os/setup/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/commands/conorbronsdon/agent-context-os/setup"><img src="https://agentmods.dev/badge/commands/conorbronsdon/agent-context-os/setup.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 | $0.00011 | $0.00287 |
| Opus 5.5 | $0.00004 | $0.00115 |
| Sonnet 5.5 | $0.00002 | $0.00057 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
setup 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.
What it actually says
/setup — Claude Code adapter
Before delegating, disclose that Claude Code auto-memory is enabled by default
and may write machine-local memory automatically during ordinary sessions. The
user can inspect it with /memory or disable it with
autoMemoryEnabled: false in .claude/settings.local.json. This repository's
review gates govern its own lifecycle and /dream flows; they do not turn
ordinary host auto-memory into a reviewed write path. Do not change the setting
as part of setup.
Read and follow .agents/skills/context-setup/SKILL.md as the canonical workflow.
After the user approves the portable workflow's proposed changes:
- replace the
[Your Name]placeholder inCLAUDE.mdwhen a name was supplied and the placeholder still exists; - if the user wants a Claude Code slash command for a newly approved portable skill, create only a thin adapter in
.claude/commands/and add its row to the table inCLAUDE.md; and - offer, but never assume, a commit after showing the final diff and validation result.
Finish by mapping the portable lifecycle back to Claude Code: /start begins a session and /end closes it.
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 First seen · 27 lines · 11 tokens per session scan A c1f64271e06d
setup is a command published in the GitHub repository conorbronsdon/agent-context-os (29 stars, last pushed 3d ago), licensed MIT. It adds 11 tokens to every session and 287 once invoked, about $0.0000 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 commands, from other repositories
load-context
Load a large file or content into the RLM system for chunk-based analysis.
load
Load the shared Wingman context for this project.
speckit.memory-md.capture-from-diff
Capture durable knowledge and architecture decisions from current or provided diffs.
speckit.memory-md.share-lesson
Promote an approved local technical/architectural lesson from docs/memory into the global shared memory.
speckit.memory-md.init-project
Profile and configure project technical stack for cross-project shared memory sync channels.
speckit.memory-md.prepare-context
Centralized context preparation: check cache health, refresh caches, search memory, and generate synthesis.