Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Keep a durable, human-readable record of the current working session so another agent can resume after a disconnect with minimal context loss.
Use this skill when:
./session/ directories.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.
At the start of a session:
ls -dt session/* 2>/dev/null | head
session_state.md, timeline.md, and handoff.md.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.
Write a checkpoint:
Prefer updating the same files rather than creating many small checkpoint files. Keep the record compact and scannable.
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.>
When resuming after a disconnect:
handoff.md first, then session_state.md, then recent timeline.md.git status --short, git branch --show-current, and targeted file reads.handoff.md.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
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Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
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