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 skills/tacoda/keystone/keystone-learnnpx skills add tacoda/keystone --skill keystone-learngit clone --depth 1 https://github.com/tacoda/keystoneWrote 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/skills/tacoda/keystone/keystone-learn)<a href="https://agentmods.dev/skills/tacoda/keystone/keystone-learn"><img src="https://agentmods.dev/badge/skills/tacoda/keystone/keystone-learn.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 | $0.00026 | $0.00319 |
| Opus 5 | $0.00013 | $0.00160 |
| Sonnet 5 | $0.00005 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
keystone-learn 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.
What it actually says
keystone:learn — capture a learning candidate
The Learning flywheel's additive step. Write the smallest record of what
surprised you to .charter/learning/inbox/<timestamp>-<slug>.md
so a later synthesize can promote it to a guide, corpus entry, or
sensor.
Canonical playbook: .charter/actions/learn.md. Open it and
follow the activities — file shape, frontmatter, and proposed-layer
selection all live there.
Run
Open .charter/actions/learn.md and execute every activity.
When to trigger
- During implementation when something behaves unexpectedly.
- After an incident or review comment that revealed a missing rule.
- Mid-task when an agent ran into a gap the charter should have closed.
Followups
- Review
.charter/learning/inbox/at the dashboard's/inboxview. Mark candidates accepted / rejected. - Run
/keystone:synthesizeto promote accepted candidates.
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 · 47 lines · 26 tokens per session scan A b71ee6aae4da
keystone-learn is a skill published in the GitHub repository tacoda/keystone (44 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 319 once invoked, about $0.0001 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.
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