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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.\"\nwhen_to_use: Preserving or recovering coding-agent context; creating checkpoints for long-running work, handoffs, disconnects, VS Code restarts, branch switches, or nontrivial edits.\n---\n\n# Session Memory\n\nKeep a durable, human-readable record of the current working session so another agent can resume after a disconnect with minimal context loss.\n\n## When To Use\n\nUse this skill when:\n- The user asks to preserve, recover, checkpoint, or manage agent memory.\n- Work is long-running, experimental, or likely to span disconnects.\n- You are about to make nontrivial edits, run long jobs, switch branches, or pause for user input.\n- You resume in a repo that already has `.\u002Fsession\u002F` directories.\n\n## Session Directory\n\nCreate one directory per working session:\n\n```bash\nmkdir -p session\ndate +%Y%m%d_%H%M%S\nmkdir -p session\u002F\u003Csession_date_time>\n```\n\nUse 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.\n\nExpected files:\n- `session_state.md` - overall goal, current subtask, loaded skills, status, plan, assumptions, blockers, and next actions.\n- `timeline.md` - append-only log of major actions, commands, results, and decisions.\n- `files.md` - files inspected, files changed, and why they matter.\n- `handoff.md` - concise resume instructions for the next agent.\n\nAdd other files only when useful, such as `experiments.tsv`, `review_notes.md`, or copied command logs.\n\n## Start Or Resume\n\nAt the start of a session:\n1. Check for existing session directories:\n\n```bash\nls -dt session\u002F* 2>\u002Fdev\u002Fnull | head\n```\n\n2. If the user is resuming work, read the latest relevant `session_state.md`, `timeline.md`, and `handoff.md`.\n3. If no relevant session exists, create a new timestamped directory.\n4. Write an initial `session_state.md` with the user's overall goal, current subtask, loaded skills, repo path, branch, and known constraints.\n\nDo not treat session notes as the only source of truth. Verify important claims against git state, files, and command output before acting.\n\n## Checkpoint Rhythm\n\nWrite a checkpoint:\n- After gathering enough context to form a plan.\n- Before and after meaningful code edits.\n- Before long-running commands, experiments, branch switches, or anything hard to reconstruct from chat.\n- When the user changes direction.\n- Before final response if the session has meaningful state worth resuming.\n- At least every 30 minutes during active long-running work.\n\nPrefer updating the same files rather than creating many small checkpoint files. Keep the record compact and scannable.\n\n## File Templates\n\n### `session_state.md`\n\n```markdown\n# Session State\n\n- Session: \u003Csession_date_time>\n- Repo: \u003Cabsolute repo path>\n- Branch: \u003Cbranch name>\n- Started: \u003Clocal timestamp>\n- Updated: \u003Clocal timestamp>\n\n## Goal\n\u003CStable overall user goal in one or two sentences. Preserve this across follow-up steering unless the user explicitly changes it.>\n\n## Current Subtask\n\u003CImmediate task or steering request currently being handled.>\n\n## Loaded Skills\n- `\u003Cskill-name>` - \u003Cwhy it was loaded and any important instructions to preserve.>\n\n## Current Status\n\u003CWhat is true now. Include completed work and verification status.>\n\n## Plan\n- [ ] \u003CNext concrete step>\n- [ ] \u003CNext concrete step>\n\n## Assumptions\n- \u003CAssumption and how to verify it if needed.>\n\n## Blockers\n- \u003CBlocker or \"None known\".>\n```\n\n### `timeline.md`\n\n```markdown\n# Timeline\n\n## \u003Clocal timestamp>\n- User asked: \u003Cbrief request>\n- Context gathered: \u003Cfiles\u002Fcommands and key result>\n- Decision: \u003Cimportant choice and rationale>\n- Result: \u003Cedits\u002Ftests\u002Foutcome>\n```\n\n### `files.md`\n\n```markdown\n# Files\n\n## Inspected\n- `\u003Cpath>` - \u003Cwhy it mattered>\n\n## Changed\n- `\u003Cpath>` - \u003Cwhat changed and why>\n\n## Generated\n- `\u003Cpath>` - \u003Cpurpose>\n```\n\n### `handoff.md`\n\n```markdown\n# Handoff\n\n## Resume From Here\n\u003COne paragraph summary of the current state.>\n\n## Next Actions\n- \u003CMost important next action>\n- \u003CVerification or cleanup still needed>\n\n## Watch Outs\n- \u003CRisks, user preferences, or repo constraints the next agent must preserve.>\n```\n\n## Recovery Workflow\n\nWhen resuming after a disconnect:\n1. Find the likely latest session directory.\n2. Read `handoff.md` first, then `session_state.md`, then recent `timeline.md`.\n3. Run lightweight verification such as `git status --short`, `git branch --show-current`, and targeted file reads.\n4. Continue from the latest verified next action.\n5. Append a timeline entry noting the recovery and any mismatches found.\n\n## Quality Rules\n\n- Keep notes factual and terse. 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Future agents need state, not a transcript.",{"type":41,"tag":72,"props":1282,"children":1283},{},[1284],{"type":47,"value":1285},"Record command outcomes that matter, especially failed tests or skipped verification.",{"type":41,"tag":72,"props":1287,"children":1288},{},[1289],{"type":47,"value":1290},"Mention uncommitted changes and whether they were made by the current agent or pre-existing.",{"type":41,"tag":72,"props":1292,"children":1293},{},[1294],{"type":47,"value":1295},"Do not store secrets, tokens, private credentials, or large logs in session files.",{"type":41,"tag":72,"props":1297,"children":1298},{},[1299,1301,1306],{"type":47,"value":1300},"If a session file becomes large, summarize old details and keep the latest next actions near the top of ",{"type":41,"tag":93,"props":1302,"children":1304},{"className":1303},[],[1305],{"type":47,"value":253},{"type":47,"value":373},{"type":41,"tag":1308,"props":1309,"children":1310},"style",{},[1311],{"type":47,"value":1312},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1314,"total":1473},[1315,1333,1351,1362,1374,1388,1401,1415,1428,1439,1453,1462],{"slug":1316,"name":1316,"fn":1317,"description":1318,"org":1319,"tags":1320,"stars":1330,"repoUrl":1331,"updatedAt":1332},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1321,1324,1327],{"name":1322,"slug":1323,"type":15},"Documentation","documentation",{"name":1325,"slug":1326,"type":15},"MCP","mcp",{"name":1328,"slug":1329,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1334,"name":1334,"fn":1335,"description":1336,"org":1337,"tags":1338,"stars":1348,"repoUrl":1349,"updatedAt":1350},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1339,1342,1345],{"name":1340,"slug":1341,"type":15},"Containers","containers",{"name":1343,"slug":1344,"type":15},"Deployment","deployment",{"name":1346,"slug":1347,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1352,"name":1352,"fn":1353,"description":1354,"org":1355,"tags":1356,"stars":1348,"repoUrl":1349,"updatedAt":1361},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1357,1360],{"name":1358,"slug":1359,"type":15},"CI\u002FCD","ci-cd",{"name":1343,"slug":1344,"type":15},"2026-07-14T05:25:59.97109",{"slug":1363,"name":1363,"fn":1364,"description":1365,"org":1366,"tags":1367,"stars":1348,"repoUrl":1349,"updatedAt":1373},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1368,1369,1370],{"name":1358,"slug":1359,"type":15},{"name":1343,"slug":1344,"type":15},{"name":1371,"slug":1372,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1375,"name":1375,"fn":1376,"description":1377,"org":1378,"tags":1379,"stars":1348,"repoUrl":1349,"updatedAt":1387},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1380,1383,1384],{"name":1381,"slug":1382,"type":15},"Debugging","debugging",{"name":1371,"slug":1372,"type":15},{"name":1385,"slug":1386,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1389,"name":1389,"fn":1390,"description":1391,"org":1392,"tags":1393,"stars":1348,"repoUrl":1349,"updatedAt":1400},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1394,1397],{"name":1395,"slug":1396,"type":15},"Best Practices","best-practices",{"name":1398,"slug":1399,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1402,"name":1402,"fn":1403,"description":1404,"org":1405,"tags":1406,"stars":1348,"repoUrl":1349,"updatedAt":1414},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1407,1410,1413],{"name":1408,"slug":1409,"type":15},"Machine Learning","machine-learning",{"name":1411,"slug":1412,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1416,"name":1416,"fn":1417,"description":1418,"org":1419,"tags":1420,"stars":1348,"repoUrl":1349,"updatedAt":1427},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1421,1424],{"name":1422,"slug":1423,"type":15},"QA","qa",{"name":1425,"slug":1426,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":1429,"name":1429,"fn":1430,"description":1431,"org":1432,"tags":1433,"stars":1348,"repoUrl":1349,"updatedAt":1438},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1434,1435],{"name":1343,"slug":1344,"type":15},{"name":1436,"slug":1437,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":1440,"name":1440,"fn":1441,"description":1442,"org":1443,"tags":1444,"stars":1348,"repoUrl":1349,"updatedAt":1452},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1445,1448,1449],{"name":1446,"slug":1447,"type":15},"Code Review","code-review",{"name":1371,"slug":1372,"type":15},{"name":1450,"slug":1451,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1454,"name":1454,"fn":1455,"description":1456,"org":1457,"tags":1458,"stars":1348,"repoUrl":1349,"updatedAt":1461},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1459,1460],{"name":1422,"slug":1423,"type":15},{"name":1425,"slug":1426,"type":15},"2026-07-14T05:25:54.928983",{"slug":1463,"name":1463,"fn":1464,"description":1465,"org":1466,"tags":1467,"stars":1348,"repoUrl":1349,"updatedAt":1472},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1468,1471],{"name":1469,"slug":1470,"type":15},"Automation","automation",{"name":1358,"slug":1359,"type":15},"2026-07-30T05:29:03.275638",496,{"items":1475,"total":1569},[1476,1493,1503,1515,1525,1540,1555],{"slug":1477,"name":1477,"fn":1478,"description":1479,"org":1480,"tags":1481,"stars":23,"repoUrl":24,"updatedAt":1492},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1482,1485,1488,1489],{"name":1483,"slug":1484,"type":15},"Data Analysis","data-analysis",{"name":1486,"slug":1487,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1490,"slug":1491,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1494,"name":1494,"fn":1495,"description":1496,"org":1497,"tags":1498,"stars":23,"repoUrl":24,"updatedAt":1502},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1499,1500,1501],{"name":1343,"slug":1344,"type":15},{"name":1436,"slug":1437,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1504,"name":1504,"fn":1505,"description":1506,"org":1507,"tags":1508,"stars":23,"repoUrl":24,"updatedAt":1514},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1509,1510,1511],{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":1512,"slug":1513,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1516,"name":1516,"fn":1517,"description":1518,"org":1519,"tags":1520,"stars":23,"repoUrl":24,"updatedAt":1524},"amc-run-sample-calibration","run AMC sample dataset calibration","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'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1521,1522,1523],{"name":1483,"slug":1484,"type":15},{"name":9,"slug":8,"type":15},{"name":1425,"slug":1426,"type":15},"2026-07-17T05:29:03.913266",{"slug":1526,"name":1526,"fn":1527,"description":1528,"org":1529,"tags":1530,"stars":23,"repoUrl":24,"updatedAt":1539},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1531,1532,1535,1536],{"name":1469,"slug":1470,"type":15},{"name":1533,"slug":1534,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1537,"slug":1538,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1541,"name":1541,"fn":1542,"description":1543,"org":1544,"tags":1545,"stars":23,"repoUrl":24,"updatedAt":1554},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1546,1547,1550,1551],{"name":1343,"slug":1344,"type":15},{"name":1548,"slug":1549,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1552,"slug":1553,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1556,"name":1556,"fn":1557,"description":1558,"org":1559,"tags":1560,"stars":23,"repoUrl":24,"updatedAt":1568},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1561,1562,1565],{"name":9,"slug":8,"type":15},{"name":1563,"slug":1564,"type":15},"Quantum Computing","quantum-computing",{"name":1566,"slug":1567,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]