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/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/meta-optimize)<a href="https://agentmods.dev/commands/topprismdata/cultivating-ml-agent/meta-optimize"><img src="https://agentmods.dev/badge/commands/topprismdata/cultivating-ml-agent/meta-optimize/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/topprismdata/cultivating-ml-agent/meta-optimize"><img src="https://agentmods.dev/badge/commands/topprismdata/cultivating-ml-agent/meta-optimize.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 | $0.00016 | $0.00389 |
| Opus 5.5 | $0.00006 | $0.00156 |
| Sonnet 5.5 | $0.00003 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
meta-optimize 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 2mo 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
/meta-optimize — Memory Health Check
Read-only analysis of memory/. Reports issues. Does not modify anything.
Usage
/meta-optimize
/meta-optimize --json
What This Does
Runs bash .claude/hooks/meta_optimize.sh which scans memory/ and reports:
- Staleness — files not modified in 90+ days
- Oversized files — > 500 lines (violates 500-line rule)
- Missing index entries — files not in MEMORY.md
- Possible contradictions — principles with conflicting language
- Coverage gaps — breakthrough experiments without extracted skills
- Stale dead-ends — feedback entries > 365 days old
- Broken internal links — markdown links to non-existent files
Output
Human-readable report. For machine-readable output, use --json.
Workflow After Running
1. /meta-optimize # see issues
2. For each issue, decide: refresh / archive / remove
3. /meta-apply <changes> # cross-model jury required for landing
Why Read-Only
This skill never modifies memory directly. From ARIS design:
"self-evolution is read-only with landing gated by cross-model jury"
Agents modifying their own memory can reinforce errors. External review catches this.
When to Use
- Weekly during long projects
- Before major phase changes
- After a competition completes
- When you suspect memory is becoming stale
When NOT to Use
- Daily (overhead)
- On tiny memory (< 10 files)
- When actively debugging a problem (not the time)
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.
- 2mo ago First seen · 59 lines · 16 tokens per session scan A b6759e9df26b
meta-optimize is a command published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 25d ago), licensed MIT. It adds 16 tokens to every session and 389 once invoked, about $0.0001 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.
meta-optimize compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| meta-optimizetopprismdata/cultivating-ml-agent | Command | 5 | 25d ago | MIT | A | 16 | Claude Code |
| compass-verifychunxiaoxx/nautilus-compass | Command | 1.3k | yesterday | none | A | 0 | Claude Code |
| slm-bot-memoryqualixar/superlocalmemory | Command | 273 | 9d ago | AGPL-3.0 | A | 82 | GitHub Copilot |
| remembersusomejias/rembric | Command | 14 | yesterday | MIT | A | 11 | Claude Code |
| tree-ring-recallTerminallyLazy/Tree-Ring-Memory | Command | 22 | 1mo ago | MIT | A | 12 | Claude Code |
| tree-ring-updateTerminallyLazy/Tree-Ring-Memory | Command | 22 | 1mo ago | MIT | A | 15 | Claude Code |
| tree-ring-certifyTerminallyLazy/Tree-Ring-Memory | Command | 22 | 1mo ago | MIT | A | 17 | Claude Code |