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 agent-session-memory-protocolgit 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/agent-session-memory-protocol)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/agent-session-memory-protocol"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/agent-session-memory-protocol/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/agent-session-memory-protocol"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/agent-session-memory-protocol.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 | $0.00130 | $0.01822 |
| Opus 5.5 | $0.00052 | $0.00729 |
| Sonnet 5.5 | $0.00026 | $0.00364 |
| Haiku 4.5 | $0.00013 | $0.00182 |
Grade A, and why
agent-session-memory-protocol 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 1mo 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Session Memory Protocol
Problem
Long-running Kaggle experiments span hours to days. Between sessions:
- Agent forgets what was tried and why
- Submissions and their OOF/LB pairs get lost
- Dead ends get repeated
- Handoff to a new session loses critical context
The 4-File Session Memory Structure
Create a session directory per experiment session:
~/.claude/projects/<project>/sessions/
└── <YYYYMMDD_HHMMSS>/
├── session_state.md # Goal, subtask, status, plan, blockers, next actions
├── timeline.md # Append-only log of actions, commands, results
├── experiments.md # Structured table of all submissions/attempts
└── handoff.md # Concise resume instructions for next session
File 1: session_state.md
# Session State
- Session: 20260708_140000
- Project: pokemon-tcg-ai-battle
- Started: 2026-07-08 14:00
- Updated: 2026-07-08 18:30
## Goal
Optimize PTCG agent to reach top 200 on the leaderboard.
## Current Subtask
Waiting for Nithin A/B convergence after 07-06 re-submit (μ₀=600 reset).
## Status
- Nithin A: LB 869 (converging from 600, +17/day)
- Nithin B: LB 772 (converging from 600, +2.5/day)
- Latest-2 = Nithin A + Nithin B (both in final-2 slots)
## Plan
1. WAIT — do not re-submit (resets convergence)
2. Check LB daily until ~Jul 20 (expected ~950/910)
3. Aug 10-14: submit 2 copies of best agent (high-roll)
## Blockers
- GPU quota exhausted (30h/week) — blocks Biohub, Industrial T1, ARC-AGI-3
## Next Actions
- [ ] Jul 13: Check PTCG LB (expected Nithin A ~920)
- [ ] Jul 20: Check PTCG LB (expected Nithin A ~950)
- [ ] Aug 10: Begin high-roll duplicate submissions
File 2: timeline.md
# Timeline (append-only)
## 2026-07-08 14:00 — Session Start
- Recalled AutoMem: 109 memories, PTCG status, ROGII Pipeline A
## 14:15 — PTCG Check
- Nithin A: 869.2 (+17 from yesterday)
- Nithin B: 772.3 (+2.5)
- Decision: WAIT, no re-submit
## 14:30 — ROGII Pipeline A
- Pushed rogii-pipea-only kernel (Pipeline B trimmed)
- Kernel COMPLETE, submission.csv written (14152 rows)
- Manual Submit to Competition required
## 15:00 — ROGII Score
- LB: 19545.46 (TERRIBLE — predicted values include -27561 outliers)
- Root cause: Pipeline A predictions have negative TVT values for some rows
- Old v7 (13.82) still better than Pipeline A on LB
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.
- 1mo ago First seen · 182 lines · 130 tokens per session scan A 35ca27ccd3ee
agent-session-memory-protocol is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 11d ago), licensed MIT. It adds 130 tokens to every session and 1,822 once invoked, about $0.0005 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.
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