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Nemo Rl Session Memory

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Agent tooling
  • Apache-2.0
  • Medium risk
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From the author

Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. Do NOT use for: simple questions, short tasks, one-off commands, linting, or code review.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The nemo-rl-session-memory skill provides a standardized framework for coding agents to persist their working context. By creating a dedicated session directory containing state, timeline, file, and handoff documentation, agents can effectively resume complex tasks after interruptions, VS Code restarts, or branch switches. This skill ensures that critical information—such as current goals, subtasks, and recent decisions—is preserved in a machine-scannable format, reducing context loss and improving handoff efficiency between agent sessions.

What it does

  • Establishes a structured directory for storing session-specific context files.
  • Provides templates for tracking session state, action timelines, file modifications, and handoff instructions.
  • Defines a recovery workflow to verify current repository state against saved session records.
  • Sets a rhythm for periodic checkpointing during long-running development tasks.
  • Ensures that future agents can quickly understand the current progress and pending actions.

When to use it

  • When working on long-running or experimental coding tasks that span multiple sessions.
  • Before performing nontrivial code edits or executing complex, multi-step operations.
  • When a user explicitly requests to save progress or checkpoint the current agent state.
  • When resuming work in a repository that contains existing session documentation.
  • Before switching branches or pausing work for an extended period.

Advantages

  • Minimizes context loss during agent disconnects or environment restarts.
  • Provides a clear, human-readable audit trail of decisions and actions taken.
  • Facilitates seamless handoffs between different agent instances.
  • Standardizes the documentation of project goals and constraints.
  • Encourages proactive verification of repository state during recovery.

Usage examples

“Save our current progress so I can resume this task later.”
The agent creates a new directory in the session folder, populates session_state.md, timeline.md, files.md, and handoff.md with the current project status, and confirms the checkpoint is saved.
“I am back, please recover the context from my last session.”
The agent identifies the most recent session directory, reads the handoff and state files, verifies the current git status, and provides a summary of the last known subtask and next steps.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nemo-rl-session-memory/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nemo-rl-session-memory.zip -d .agents/skills

Documentation by NVIDIA

Session Memory

Keep a durable, human-readable record of the current working session so another agent can resume after a disconnect with minimal context loss.

When To Use

Use this skill when:

  • The user asks to preserve, recover, checkpoint, or manage agent memory.

  • Work is long-running, experimental, or likely to span disconnects.

  • You are about to make nontrivial edits, run long jobs, switch branches, or pause for user input.

  • You resume in a repo that already has ./session/ directories.

Session Directory

Create one directory per working session:

mkdir -p session
date +%Y%m%d_%H%M%S
mkdir -p session/<session_date_time>

Use local time from the machine. Reuse the same session directory for all checkpoints in the same conversation unless the user explicitly starts a new session.

Expected files:

  • session_state.md - overall goal, current subtask, loaded skills, status, plan, assumptions, blockers, and next actions.

  • timeline.md - append-only log of major actions, commands, results, and decisions.

  • files.md - files inspected, files changed, and why they matter.

  • handoff.md - concise resume instructions for the next agent.

Add other files only when useful, such as experiments.tsv, review_notes.md, or copied command logs.

Start Or Resume

At the start of a session:

  1. Check for existing session directories:

ls -dt session/* 2>/dev/null | head

  1. If the user is resuming work, read the latest relevant session_state.md, timeline.md, and handoff.md.

  2. If no relevant session exists, create a new timestamped directory.

  3. Write an initial session_state.md with the user's overall goal, current subtask, loaded skills, repo path, branch, and known constraints.

Do not treat session notes as the only source of truth. Verify important claims against git state, files, and command output before acting.

Checkpoint Rhythm

Write a checkpoint:

  • After gathering enough context to form a plan.

  • Before and after meaningful code edits.

  • Before long-running commands, experiments, branch switches, or anything hard to reconstruct from chat.

  • When the user changes direction.

  • Before final response if the session has meaningful state worth resuming.

  • At least every 30 minutes during active long-running work.

Prefer updating the same files rather than creating many small checkpoint files. Keep the record compact and scannable.

File Templates

session_state.md
# Session State

- Session: <session_date_time>
- Repo: <absolute repo path>
- Branch: <branch name>
- Started: <local timestamp>
- Updated: <local timestamp>

## Goal
<Stable overall user goal in one or two sentences. Preserve this across follow-up steering unless the user explicitly changes it.>

## Current Subtask
<Immediate task or steering request currently being handled.>

## Loaded Skills
- `<skill-name>` - <why it was loaded and any important instructions to preserve.>

## Current Status
<What is true now. Include completed work and verification status.>

## Plan
- [ ] <Next concrete step>
- [ ] <Next concrete step>

## Assumptions
- <Assumption and how to verify it if needed.>

## Blockers
- <Blocker or "None known".>
timeline.md
# Timeline

## <local timestamp>
- User asked: <brief request>
- Context gathered: <files/commands and key result>
- Decision: <important choice and rationale>
- Result: <edits/tests/outcome>
files.md
# Files

## Inspected
- `<path>` - <why it mattered>

## Changed
- `<path>` - <what changed and why>

## Generated
- `<path>` - <purpose>
handoff.md
# Handoff

## Resume From Here
<One paragraph summary of the current state.>

## Next Actions
- <Most important next action>
- <Verification or cleanup still needed>

## Watch Outs
- <Risks, user preferences, or repo constraints the next agent must preserve.>

Recovery Workflow

When resuming after a disconnect:

  1. Find the likely latest session directory.

  2. Read handoff.md first, then session_state.md, then recent timeline.md.

  3. Run lightweight verification such as git status --short, git branch --show-current, and targeted file reads.

  4. Continue from the latest verified next action.

  5. Append a timeline entry noting the recovery and any mismatches found.

Quality Rules

  • Keep notes factual and terse. Future agents need state, not a transcript.

  • Record command outcomes that matter, especially failed tests or skipped verification.

  • Mention uncommitted changes and whether they were made by the current agent or pre-existing.

  • Do not store secrets, tokens, private credentials, or large logs in session files.

  • If a session file becomes large, summarize old details and keep the latest next actions near the top of handoff.md.

Files

skill-card.md

Description: <br>

Manage durable working-session memory for coding agents so that context can be preserved and recovered across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers preserving or recovering coding-agent context across disconnects, VS Code restarts, long-running work sessions, handoffs, or branch switches. <br>

Deployment Geography for Use: <br>

Global <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Files, Shell commands] <br> Output Format: [Markdown] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • claude-code <br>

  • codex <br>

Evaluation Tasks: <br>

Evaluated against 5 evaluation tasks (3 positive skill-activation, 2 negative activation) with 2 attempts per task. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>

  • Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>

  • Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>

  • Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>

  • Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>

Underlying evaluation signals used in this run: <br>

  • security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Verifies that the agent loaded the expected skill and workflow. <br>

  • skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage. <br>

  • accuracy: Grades final-answer correctness against the reference answer. <br>

  • goal_accuracy: Checks whether the overall user task completed successfully. <br>

  • behavior_check: Verifies expected behavior steps, including safety expectations. <br>

  • token_efficiency: Compares token usage with and without the skill. <br>

Evaluation Results: <br>

DimensionNumclaude-codecodex
Security8100% (+0%)100% (+0%)
Correctness896% (+3%)84% (+5%)
Discoverability899% (+1%)91% (+5%)
Effectiveness882% (-5%)81% (+22%)
Efficiency889% (+1%)86% (+10%)

Skill Version(s): <br>

1.5.4 (source: pyproject.toml) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nemo-rl-session-memory · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-rl-session-memory.zip