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/pnmcguire480/cairntirWrote 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/pnmcguire480/cairntir/reason)<a href="https://agentmods.dev/commands/pnmcguire480/cairntir/reason"><img src="https://agentmods.dev/badge/commands/pnmcguire480/cairntir/reason.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 5.1 | $0.00009 | $0.00126 |
| Opus 5 | $0.00005 | $0.00063 |
| Sonnet 5 | $0.00002 | $0.00025 |
| Haiku 4.5 | $0.00001 | $0.00013 |
Grade A, and why
reason 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 7d 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
Use Cairntir's reason skill to think about the user's question with the help of stored memory.
- Call
cairntir_session_startto load the 4-layer context for the current wing - Call
cairntir_recallto retrieve memories specifically relevant to the question - Reason out loud with the retrieved memories in view — cite drawer ids inline
- Propose an answer, and flag any assumptions that should be stress-tested by
cairntir_crucible
$ARGUMENTS
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.
- 7d ago First seen · 13 lines · 9 tokens per session scan A 37309c3af30e
reason is a command published in the GitHub repository pnmcguire480/cairntir (2 stars, last pushed yesterday), licensed MIT. It adds 9 tokens to every session and 126 once invoked, about $0.0000 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 commands, from other repositories
tree-ring-capture
Capture a concise validated lesson decision warning or preference in Tree Ring Memory.
tree-ring-audit
Audit consolidate or forget stale sensitive or superseded Tree Ring Memory entries.
tree-ring-dox-sync
Preview and synchronize DOX-style AGENTS.md guidance as source-linked Tree Ring memory.
tree-ring-recall
Recall durable Tree Ring Memory context before starting or resuming work.
tree-ring-status
Check receipt-backed Tree Ring harness readiness without claiming configuration is activation.
iai-directive
Record a standing order the user typed themselves, as an explicit memory directive.