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Official agent skill

Nvflare Autofl Report

by NVIDIA in NVIDIA/skills

Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

OfficialApache-2.0Auto-check passed

Install Nvflare Autofl Report

skills CLI
$ npx skills add NVIDIA/skills --skill nvflare-autofl-report -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills nvflare-autofl-report --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nvflare-autofl-report .claude/skills/nvflare-autofl-report && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nvflare-autofl-report
GitHub stars
3.5k
Token cost
~2.6k tokens
SKILL.md length
1,272 words
Files
8 (incl. scripts, references)
Skills in repo
386
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

  • Works in 5 steps: Locate the job directory containing… → Confirm execution has stopped. Prefer… → Generate the deterministic report… → …
  • SKILL.md covers Purpose, Use When, Do Not Use When and Available Scripts, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Nvflare Autofl Report is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/report-contract.md`). Compatibility notes: Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign.

The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

Example prompts

  • “/nvflare-autofl-report”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign.

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Locate the job directory containing results.tsv, autofl.yaml,
  2. Confirm execution has stopped. Prefer campaign state with
  3. Generate the deterministic report artifacts
  4. Read both autofl_final_report.md and autofl_report_summary.json. Check
  5. Give the user the baseline, best score, delta, strongest candidate lineage,

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign.

    From compatibility in the SKILL.md frontmatter.

Context cost

Nvflare Autofl Report loads about 2.6k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 47 tokens; SKILL.md has 1,272 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 1,272 words, ~2,631 tokens.

Download SKILL.mdSave it as .claude/skills/nvflare-autofl-report/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
nvflare-autofl-report
description
Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.
compatibility
Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign.
license
Apache-2.0
metadata.version
0.1.0
metadata.author
NVIDIA FLARE Team <federatedlearning@nvidia.com>
metadata.min-flare-version
2.9.0
metadata.blast-radius
edits_files
metadata.category
Reporting
metadata.tags
nvflare, federated-learning, optimization, reporting
metadata.languages
python

NVFLARE Auto-FL Report

Purpose

Turn the recorded evidence from a stopped NVFLARE Auto-FL campaign into a reproducible Markdown report, machine-readable JSON summary, and refreshed progress plot without changing the campaign or its results.

Use When

Use this skill after an NVFLARE Auto-FL campaign has stopped, reached an explicit cap, hit a hard blocker, or was manually interrupted. Use it when the user asks for the final report, achieved improvement, literature findings, failed ideas, reproduction details, or a refreshed progress plot.

Do Not Use When

Do not use this skill to start or continue optimization, invent missing results, or finalize a campaign that is still running. Use nvflare-autofl for the active candidate loop. Do not stop an active campaign merely because the user asks for a status snapshot.

Available Scripts

ScriptPurposeArguments
scripts/generate_report.pyValidate stopped campaign evidence and generate the report, JSON summary, and optional progress plot.Campaign job directory; optional evidence/output paths, interruption confirmation, plot settings, and agent context. Run python scripts/generate_report.py --help for the complete CLI.

Run this bundled CLI directly with Python. This skill has no NVFLARE or agent run_script() helper; do not invent or call one. Resolve the script from the directory containing this SKILL.md, as shown in the workflow.

Workflow

  1. Locate the job directory containing results.tsv, autofl.yaml, .nvflare/autofl/campaign_state.json, and candidate manifests.

  2. Confirm execution has stopped. Prefer campaign state with final_response_allowed=true. When a process was abruptly interrupted and state is stale, independently confirm no campaign or job process remains, then use --confirm-interrupted. Before finalizing, confirm that campaign state, results.tsv, and available candidate manifests contain no pending candidate. Finalize or abandon pending work through nvflare-autofl first.

  3. Generate the deterministic report artifacts:

    bash
    python "$REPORTER" <job-dir>

    Resolve REPORTER once to the absolute path of scripts/generate_report.py relative to this SKILL.md. Do not assume a provider-specific skill installation directory or $CODEX_HOME.

  4. Read both autofl_final_report.md and autofl_report_summary.json. Check warnings about metric use, executed budget changes, campaign-state/ledger disagreement, missing provenance, and incomplete interruption state.

  5. Give the user the baseline, best score, delta, strongest candidate lineage, concise "what helped" and "what did not help" findings, literature ideas that helped or failed, selection rationale, reliability caveats, and absolute artifact paths.

The helper attempts to refresh progress.png by reusing the product Auto-FL plotter. Plotting is optional evidence: if plotting dependencies are missing or the artifact is not a valid PNG, the helper preserves the artifact, records a warning and artifacts.progress_plot_available=false, and still writes the Markdown and JSON reports without embedding the broken image. It does not modify source, candidate manifests, results.tsv, or campaign state. All relative helper path options, including --plotter, resolve from the campaign job directory rather than the shell's current working directory. The helper holds the campaign lifecycle lock from evidence loading through artifact writes. It refuses a busy campaign and rejects writable output paths that alias campaign evidence, job.py, trust-contract source paths, or another output, including filesystem aliases. Outputs must not match the trust contract's allowed source-creation patterns. A read-only campaign archive is reportable only when its persisted campaign.lock already exists and writable output paths are supplied; the persisted lock target alone does not imply live contention.

Interrupted Campaigns

The report helper must refuse state with final_response_allowed=false unless the human has said the campaign was stopped/interrupted and the agent confirms that execution is no longer active. Only then run:

bash
python "$REPORTER" <job-dir> --confirm-interrupted

This records a reporting-time interruption assertion; it does not rewrite the campaign state or pretend the runner finalized cleanly. It bypasses only stale stop state. A candidate ledger row or pending-candidate state always blocks finalization. Any available candidate manifest whose status is not a recognized terminal value (keep, discard, crash, or abandoned) also blocks. Missing, unknown, or unreadable status is unfinished evidence because completion cannot be established. The agent must finalize or abandon that candidate first.

Troubleshooting

Error or symptomCauseSolution
Reporting is refused because final_response_allowed=false.The campaign may still be active, or persisted state may be stale after an interruption.Confirm no campaign or job process remains. If a human confirmed the interruption, rerun with --confirm-interrupted; otherwise continue or stop the campaign through nvflare-autofl.
Reporting is refused for a pending candidate or unknown manifest status.The recorded evidence cannot establish that candidate execution finished.Finalize or abandon the candidate through nvflare-autofl, then regenerate the report.
The campaign lock is busy.Another campaign or reporting process holds the lifecycle lock.Identify and wait for the active process. Never bypass or delete a live lock.
The JSON says progress_plot_available=false.Plotting dependencies are unavailable or the generated artifact is not a valid PNG.Use the completed Markdown and JSON reports, review their warning, and install the campaign's plotting dependencies before retrying if a plot is required.
An output path is rejected.The path aliases protected campaign evidence, source, or another output.Choose distinct writable output paths outside protected campaign inputs and rerun.
Show full SKILL.md (473 more words)Show less

Report Contract

The final report must include:

  • campaign termination reason, objective, metric source, direction, environment, cap, abandoned-candidate count, and declared fixed budget;
  • baseline, best retained result, score delta, runtime, failures, and status counts;
  • selected-candidate rationale, strict retained improvements, representative non-improvements, grouped failures, and outcomes by recorded algorithm family;
  • running-best trajectory selected by first, final, and largest objective improvements, plus a refreshed progress.png when plotting is available, with explicit plot availability in the JSON summary otherwise;
  • best-candidate manifest, patch hash, base-candidate lineage, inherited code changes, artifacts, and exact baseline/best commands;
  • every recorded literature checkpoint, its event ID and source markers, explicitly linked candidates, and whether measured evidence helped, matched, failed, or did not confirm the idea;
  • discarded/crashed ideas and deterministic comparability warnings;
  • optional agent model, reasoning effort, cost, or tooling notes when supplied;
  • absolute paths to autofl.yaml, results.tsv, campaign state, progress.png, autofl_report_summary.json, and autofl_final_report.md.

The report must distinguish imported/declared budget from executed command arguments. It must warn when the best candidate changed training compute or comparison population, when authoritative state disagrees with ledger-derived accounting, or when repeated selection used a test-like metric. Product Auto-FL campaigns retain the imported min or max direction; the report rejects legacy minimization evidence without direction provenance. It must not add PR-specific sections such as "Product Findings" unless the user explicitly requests them. best means a scored retained baseline or keep row; an unretained scored discard may appear only as best_observed. Candidate and crash rows never become retained best results, milestones, or literature improvements. Baseline identity is determined strictly by status=baseline, matching the campaign guard. The report preserves per-run metric name, extraction source, artifact, candidate kind, algorithm family, and literature event linkage. It does not infer algorithm families or mechanisms from candidate names.

Read report-contract.md when interpreting lineage, literature outcomes, budget warnings, or interrupted state.

Limitations

  • The report is only as complete as the persisted ledger, campaign state, and candidate manifests; it does not validate or reconstruct unrecorded claims.
  • Results from a single campaign do not establish robustness or generalization.
  • The helper does not start, stop, resume, or resubmit campaign jobs.
  • Progress plotting remains optional, so Markdown and JSON may be produced without an embeddable PNG.
  • Copied campaign archives may retain only partial provenance when recorded absolute manifest paths are no longer available.

Requirements

  • Treat results.tsv as recorded evidence; never repair scores by guessing.
  • Work without Git. Do not commit or push unless the user separately asks.
  • Preserve the campaign and job sources exactly as found.
  • Never bypass campaign-lock contention or output-path collision checks.
  • Use candidate manifests when available, but still report partial provenance when copied artifacts make old absolute manifest paths unavailable.
  • Keep conclusions proportional to the evidence. A single run is a candidate, not a robustness claim.
  • For POC/production, report standard NVFLARE job IDs and downloaded artifacts already present in the ledger; do not resubmit jobs during reporting.

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/nvflare-autofl-report of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • references/report-contract.md
  • scripts/generate_report.py
  • skill-card.md
  • skill.oms.sig
  • tests/helper_scripts.md

Open the folder on GitHubat commit dfdd080

Compare with similar skills

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Literature Search Methodologyaiming-lab/AutoResearchClaw15k—~709Automated safety check: PassMIT
Systematic Literature Review Builderbytedance/deer-flow84k2 repos~4.3kAutomated safety check: PassMIT
Arpsych Literature Synthesisbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.2kAutomated safety check: PassMIT

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Questions about Nvflare Autofl Report

What does Nvflare Autofl Report do?

Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign. Nvflare Autofl Report is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

How do I install Nvflare Autofl Report in Claude Code?

Run `npx skills add NVIDIA/skills --skill nvflare-autofl-report -a claude-code`. Or copy the skill folder (skills/nvflare-autofl-report in NVIDIA/skills) into .claude/skills/nvflare-autofl-report in your project. Claude Code loads it when a task matches its description.

How do I install Nvflare Autofl Report in Codex?

Run `npx skills add NVIDIA/skills --skill nvflare-autofl-report -a codex`. Or copy the skill folder (skills/nvflare-autofl-report in NVIDIA/skills) into .agents/skills/nvflare-autofl-report in your project. Codex loads it when a task matches its description.

Can I use Nvflare Autofl Report in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nvflare-autofl-report -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nvflare-autofl-report, .gemini/skills/nvflare-autofl-report, .github/skills/nvflare-autofl-report and .opencode/skills/nvflare-autofl-report in your project.

What does Nvflare Autofl Report need to run?

Going by SKILL.md and its folder, Nvflare Autofl Report needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign..

Does Nvflare Autofl Report access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nvflare Autofl Report safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Nvflare Autofl Report use?

Nvflare Autofl Report is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nvflare Autofl Report use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.

What are the alternatives to Nvflare Autofl Report?

Skills that share tags, products or a category with Nvflare Autofl Report: Literature Review (K-Dense-AI/scientific-agent-skills, 48k stars), Scientific Thinking Literature Review (affaan-m/ECC, 276k stars), Literature Search Methodology (aiming-lab/AutoResearchClaw, 15k stars) and Systematic Literature Review Builder (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nvflare Autofl Report?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.