Nvflare Autofl Report

NVIDIAOfficialCreated by NVIDIA FLARE Team

Skill
Content generator
Software development
Apache-2.0
Medium risk
Has scripts

From the author

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

— the description by NVIDIA FLARE Team

AutoGineer summary

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

The nvflare-autofl-report skill provides a standardized way to synthesize evidence from a stopped or interrupted NVFLARE Auto-FL campaign. It processes campaign artifacts to produce a comprehensive Markdown report, a machine-readable JSON summary, and a refreshed progress plot. The tool ensures data integrity by validating campaign state and ledger entries, providing clear insights into baseline performance, best-retained results, and literature-based outcomes without modifying the original campaign data.

What it does

Validates campaign evidence from job directories.
Generates a reproducible Markdown final report.
Creates a machine-readable JSON summary of campaign results.
Refreshes the campaign progress plot as a PNG artifact.
Synthesizes literature outcomes and candidate lineage.
Provides warnings for metric usage, budget changes, or incomplete state.

When to use it

After an NVFLARE Auto-FL campaign has reached a cap or stopped.
When a campaign was manually interrupted and requires a final report.
When you need to extract findings on what helped or failed during optimization.
When you need to document the best candidate and its lineage for reproducibility.

Advantages

Ensures deterministic and reproducible reporting.
Maintains strict separation between campaign execution and reporting.
Provides clear provenance for candidate lineage and literature checkpoints.
Safely handles interrupted campaigns with explicit confirmation.
Prevents modification of original campaign results or state.

Usage examples

“Generate the final report for my completed Auto-FL campaign in /jobs/experiment_01.”: The tool validates the job directory and produces autofl_final_report.md and autofl_report_summary.json with the campaign's performance metrics.
“My campaign was interrupted, how do I get a report?”: After confirming no active processes remain, run the script with --confirm-interrupted to generate the report based on the available state.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/nvflare-autofl-report/download

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

mkdir -p .agents/skills && unzip nvflare-autofl-report.zip -d .agents/skills

Documentation by NVIDIA FLARE Team

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

Script	Purpose	Arguments
`scripts/generate_report.py`	Validate 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

Locate the job directory containing results.tsv, autofl.yaml, .nvflare/autofl/campaign_state.json, and candidate manifests.
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.
Generate the deterministic report artifacts:
```
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.
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.
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:

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 symptom	Cause	Solution
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.

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.

Files

ATTRIBUTION.mdAdded by AutoGineer932 B
BENCHMARK.md5.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md10.3 KiB
evals/evals.json5.3 KiB
references/report-contract.md10.2 KiB
scripts/generate_report.pyScript84.1 KiB
skill-card.md3.9 KiB
skill.oms.sig5.2 KiB
tests/helper_scripts.md887 B

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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.

Needs: Needs Python

The author says: Requires NVFLARE 2.9.0+, Python, and artifacts from an NVFLARE Auto-FL campaign.

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

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

Created by

NVIDIA FLARE Team

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/nvflare-autofl-report · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by NVIDIA FLARE Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nvflare-autofl-report.zip