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 skills add topprismdata/cultivating-ml-agent --skill ladder-drift-meta-aware-regressiongit 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/skills/topprismdata/cultivating-ml-agent/ladder-drift-meta-aware-regression)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/ladder-drift-meta-aware-regression"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/ladder-drift-meta-aware-regression/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/skills/topprismdata/cultivating-ml-agent/ladder-drift-meta-aware-regression"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/ladder-drift-meta-aware-regression.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 | $0.00178 | $0.02232 |
| Opus 5 | $0.00089 | $0.01116 |
| Sonnet 5 | $0.00036 | $0.00446 |
| Haiku 4.5 | $0.00018 | $0.00223 |
Grade A, and why
ladder-drift-and-meta-aware-regression 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ladder Drift + Meta-Aware Regression: The PTCG AI Battle 2026-06 Findings
This skill captures three hard-won lessons from a multi-week PTCG (Pokémon TCG) AI Battle project where a strong 1084.5-LB Lucario agent (v29, public notebook fork) held the lead for days — then lost 200 LB in 48 hours due to meta shift, and lost another 150 LB to "meta-aware" patches that LOOKED like improvements in 60-game tests but REGRESSED in 200-game tests.
Lesson 1: Ladder Drift is Real (Same Code, -200 LB in 48h)
v29 baseline (no code changes) — different scores across days:
| Date | LB Score | Delta |
|---|---|---|
| 2026-06-27 13:13 | 970.0 | baseline |
| 2026-06-28 16:29 | 774.6 | -195 |
| 2026-06-29 16:25 | 770.5 | -4 (stabilizing) |
The agent didn't change. The ladder did. Top 5 decks shifted from Lucario-dominant to Starmie / Archaludon / Dragapult-dominant. The same code that scored 970 against a Lucario ladder scored 770 against a Starmie ladder.
Implication: When your agent's LB drops, check the meta before debugging the code. Look at top-20 leaderboard deck names; if they're all different decks than when you calibrated, it's drift, not bug.
Detection: Compare LB score to baseline over time. If drift > 100 points in a week with no code change, the meta moved. Don't waste quota on resubmits of the same code.
Lesson 2: Meta-Aware Heuristics Often REGRESS (The 60→200 Game Lesson)
Three "improvements" tested on small samples (60-80 games) all looked neutral or marginally positive. All three regressed when tested at 200 games:
| Variant | Sample | Mirror | vs Meta Deck | Verdict (60g) | Verdict (200g) |
|---|---|---|---|---|---|
| v33 (v29 + ALL v31 logic) | 80g mirror + 60g meta | -7.5% mirror | -13.1% vs Arch | "neutral" | REGRESSION |
| v34 (v29 + Archaludon-only detection) | 80g mirror + 60g meta | +5.0% mirror | -5.6% vs Arch | "slightly better" | -10.1% vs Arch |
| v34 v2 (rerun) | 200g mirror + 200g meta | +10.0% mirror | -25.9% vs Arch | n/a | -10.1% vs Arch |
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.
- 11d ago First seen · 167 lines · 178 tokens per session scan A f57460519ca3
ladder-drift-and-meta-aware-regression is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 14d ago), licensed MIT. It adds 178 tokens to every session and 2,232 once invoked, about $0.0009 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.
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