Autofl Nvflare Report
NVIDIA/NVFlare/research/auto-fl-research/skills/autofl-nvflare-report
researchOfficial
Official Provider SkillView repo
Generate and commit a markdown report after an Auto-FL NVFlare autoresearch experiment has been manually stopped. Use when the user asks to summarize a stopped campaign, report achieved improvements, explain implemented literature-derived ideas and sources, refresh progress plots, capture pasted agent model/effort/cost context when available, or commit the final report and progress plot to the current experiment branch.
Files3 files
SKILL.md87 lines
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Skill details
Versionv1.0.0
AuthorNVIDIA
Categoryresearch
Skill IDNVIDIA/NVFlare/research/auto-fl-research/skills/autofl-nvflare-report
Files3 files
Related skills
Autofl NvflareHelp coding agents work on an NVFlare-based Auto-FL harness that follows an autoresearch-style loop. Use when the user wants to create, edit, debug, or extend program.md, task folders such as tasks/cifar10/ and tasks/vlm_med/, task-local job.py, client.py, model.py, shared custom_aggregators.py, mutation policies, results.tsv logging, or coding-agent prompts for a bounded federated-learning research loop. This skill is specifically for NVFlare harness work where the Client API loop, DIFF upload Nvflare Convert PytorchConvert existing PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; do not use for other frameworks or deployment-only tasks.Nvflare Diagnose JobDiagnose failed, stalled, or suspicious NVFLARE jobs in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.Nvflare OrientRoute ambiguous NVFLARE requests by inspecting the local project, checking readiness, and recommending the next specific FLARE workflow skill without editing files.Respond To IssueResearch and draft a response to a GitHub issue or question from an external contributor.Create SpikeInvestigate a plain-language problem description by deeply exploring the codebase, then create a structured GitHub issue with technical findings. Prequel to build-from-issue — maps vague ideas to concrete, buildable issues. Trigger keywords - spike, investigate, explore, research issue, technical investigation, create spike, new spike, feasibility, codebase exploration.