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/RasputinKaiser/Self-Improvement-PluginWrote 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/rasputinkaiser/self-improvement-plugin/checkpoint)<a href="https://agentmods.dev/commands/rasputinkaiser/self-improvement-plugin/checkpoint"><img src="https://agentmods.dev/badge/commands/rasputinkaiser/self-improvement-plugin/checkpoint/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/rasputinkaiser/self-improvement-plugin/checkpoint"><img src="https://agentmods.dev/badge/commands/rasputinkaiser/self-improvement-plugin/checkpoint.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 | $0.00193 |
| Opus 5.5 | $0.00004 | $0.00077 |
| Sonnet 5.5 | $0.00002 | $0.00039 |
| Haiku 4.5 | $0.00001 | $0.00019 |
Grade A, and why
checkpoint 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 28d 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
Follow references/command-protocol.md.
Run python3 <plugin-root>/scripts/snapshot_harness.py --reason "before: <scope>".
Retain the exact snapshot ID, paths covered, workspace/ref, dirty state, and a
continuity packet through the documented compact_continuity.py JSON-stdin hook.
A snapshot only covers its declared files, not the entire machine.
Treat recovery as a transaction: record the before-state identity and intended
writes; preserve user changes made after the snapshot. Inspect
python3 <plugin-root>/scripts/restore_harness.py <snapshot-id> --dry-run first.
Use an explicit snapshot ID rather than relying on a moving latest pointer.
Restoration requires the user's authorized exact scope; the dry-run is not a
restoration receipt. Prefer the adaptation controller's journaled rollback for
an explicitly activated candidate.
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.
- 28d ago Changed · +2 lines · -4 tokens per session 8b52e9786c32
- 1mo ago First seen · 16 lines · 15 tokens per session scan A ca63f765f9fb
checkpoint is a command published in the GitHub repository RasputinKaiser/Self-Improvement-Plugin (8 stars, last pushed 13d ago), licensed MIT. It adds 11 tokens to every session and 193 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 2026-08-31.
Other commands, from other repositories
sessions
Manage OpenAI Codex session history, aliases, and session metadata.
projects
List registered projects and instinct counts.
promote
Promote project instincts to global scope.
fechar-sessao
Closes the current work session — rewrites the Estado Atual section of AGENTS.md to reflect where things stand now.
retro
Write this session's lessons into the project's lessons/ directory using the memory-discipline format.
save-session
Save current session state to a dated file in /.codex/session-data/ so work can be resumed in a future session with full context.