Nvflare Autofl Report
NVIDIAOfficialCreated by NVIDIA FLARE Team
- Skill
- Content generator
- Software development
- Apache-2.0
- Medium risk
- Has scripts
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://autogineer.com/agent-skills/nvidia/nvflare-autofl-report/downloadUnzip 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 throughnvflare-autoflfirst.Generate the deterministic report artifacts:
python "$REPORTER" <job-dir>Resolve
REPORTERonce to the absolute path ofscripts/generate_report.pyrelative to thisSKILL.md. Do not assume a provider-specific skill installation directory or$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-interruptedThis 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.pngwhen 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.tsvas 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
references/report-contract.md
Auto-FL Final Report Contract
Inputs
The report generator consumes product Auto-FL artifacts from the job directory:
results.tsv: append-only result evidence;autofl.yaml: requested and imported campaign contract;.nvflare/autofl/campaign_state.json: stop/finalization state;.nvflare/autofl/candidates/*/candidate_manifest.json: optional detailed candidate provenance;progress.png: existing plot to refresh safely.
The current ledger contract includes status, name, score, runtime, changed files, hypothesis/diff summary, exact run command, artifacts, failure reason, candidate manifest, base candidate, patch hash, metric name/source/artifact, candidate kind, algorithm family, and literature event ID. Older ledgers may omit newer fields; the report discloses missing provenance rather than failing when the essential status and score columns remain readable.
Termination
final_response_allowed=true is the preferred deterministic proof that a
campaign may be finalized. For abrupt process termination, the user and agent
may explicitly confirm interruption. This assertion is report provenance only:
it must not mutate campaign state. --confirm-interrupted bypasses only stale
stop state. Finalization is refused when campaign state reports pending work,
the ledger has a candidate row, or an available manifest is not in a
recognized terminal status: keep, discard, crash, or abandoned.
Missing, unknown, or unreadable manifest status also blocks finalization
because the helper cannot prove that its candidate is complete. Finalize or
abandon such candidates before generating an authoritative report.
The reporter acquires the same nonblocking
.nvflare/autofl/campaign.lock used by runner lifecycle actions before reading
campaign evidence and holds it through plotting and report writes. If another
action owns the lock, reporting fails cleanly rather than publishing a mixed
snapshot. On POSIX, the persisted lock file is a stable lock target rather than
proof of a live owner. The reporter can therefore read a read-only campaign
archive when the lock file already exists and the three outputs point to a
writable location. NVFlare supports POSIX platforms; reporting fails clearly
when fcntl locking is unavailable.
Candidate Lineage
Candidates are based on the current best source snapshot. A retained candidate
can therefore record changed_files=none while inheriting algorithm code from
its base_candidate. Follow base_candidate links back to baseline and union
changed files across the chain. Mark lineage partial when an ancestor row or
manifest is unavailable.
Ledger paths may refer to the container or host where a candidate ran. When a
recorded manifest path is unavailable, the reporter checks the campaign-local
.nvflare/autofl/candidates/<candidate_id>/candidate_manifest.json path and
accepts it only when the manifest candidate_id exactly matches the ledger
candidate name. The JSON summary preserves recorded_path, resolved_path,
and whether resolution used the recorded or campaign-local path.
Literature Evidence
A literature row opens a checkpoint and records a literature_event_id.
Associate candidate rows carrying that ID with the checkpoint, even when a
later review was recorded before the batch completed. Candidates without an
event ID are not attributed to a literature checkpoint; the report does not
guess from row order. Compare the best finalized keep or discard score in
that evidence with the retained incumbent immediately before the review.
Pending candidates and crashes are attempts but never scored improvements,
even if a malformed crash row contains a numeric score:
helped: a following candidate improved the incumbent;matched: the best following candidate tied the incumbent;not_confirmed: candidates ran but none matched or improved the incumbent;failed: attempts produced no score;not_evaluated: no candidate attempt followed the checkpoint;incomplete: authoritative campaign state shows that fewer than the required exploration candidates completed before the campaign stopped. The report preserves measured evidence but does not present the partial batch as a confirmed negative result.
Preserve [src: ...] markers from the checkpoint. These are campaign-recorded
source identifiers, not independently verified citations.
Result Selection
best is strictly the best scored retained result: a baseline or keep row.
best_observed may identify a better scored discard as unretained evidence.
candidate and crash rows are excluded from retained best selection,
running-best milestones, and literature improvements. Literature tables render
status first, so scored crashes remain crash and unscored discards remain
n/a.
Baseline identity is strict: only status=baseline is a baseline. Names and
run-command text do not change result status. The report carries the runner's
per-row metric extraction provenance, candidate kind, algorithm family, and
literature linkage into the JSON summary and best-candidate Markdown section.
Outcome Synthesis
The human-readable report leads with three deterministic views built from the same ledger evidence:
Selected Candidate And Why identifies the best scored retained result, its baseline delta, declared family and kind, hypothesis, and retained lineage. It does not claim robustness from a single run.
What Helped contains only
keeprows that strictly improved the retained incumbent at that point in the campaign. Major entries are selected by the first, final, and largest measured improvements.What Did Not Help selects a representative scored
discardfor each explicit algorithm-family/literature-event group and groups crashes or unscored discards by their recorded failure reason.
outcome_summary.families reports helped, mixed, not_confirmed, or
failed from explicit algorithm_family values; missing values remain
unclassified. The helper never infers a family or mechanism from candidate
names. outcome_summary.literature provides a compact count and strongest
helped checkpoint while literature_reviews retains every detailed event.
Trajectory compression preserves the first and final running-best rows and fills the remaining limit with the largest objective improvements, restoring chronological order for presentation. A scored discarded row may remain visible as observed trajectory evidence, but it never enters What Helped or retained selection.
The objective contract records two distinct provenance fields. metric_source
describes where measurements came from and defaults to NVFlare metric artifacts. metric_contract_source records how the importer selected the
metric, for example user_request, arg:key_metric, core_default when the
job uses NVFlare's resolved default key metric, or default when the importer
uses an unresolved metric fallback.
Product Auto-FL campaigns preserve the imported objective.mode (min or
max) and its provenance. Reported improvement is direction-adjusted and
therefore positive when the objective improves, while ledger scores remain raw.
A report refuses legacy minimization evidence without direction provenance or
a malformed/conflicting direction contract.
Comparability
autofl.yaml describes the imported/declarative contract; exact commands in
results.tsv describe what executed. Report both. Compare baseline and best
command options and flag changes to clients, rounds, local epochs/steps, batch
sizes, data partitioning, seed, model architecture, model-size limits,
aggregation, or final-evaluation clients.
The report derives candidate attempts, baseline score, and improvement from
the ledger and cross-checks those values against authoritative campaign state.
It also verifies that the state's results pointer names the ledger being
reported. It exposes state/ledger disagreements as warnings rather than
silently choosing one account. The state-derived abandoned_candidates count
remains visible because abandoned manifests do not necessarily have ledger
rows.
When a test-like metric guided multiple candidate decisions, state that the selected candidate needs one final evaluation on an untouched holdout. Do not silently present repeated test-set selection as an unbiased final estimate.
Outputs
autofl_final_report.md: human-readable review artifact;
autofl_report_summary.json: machine-readable summary using schemanvflare.autofl.report.v1;progress.png: refreshed using thenvflare-autoflproduct plotter when plotting is available.
The Markdown report embeds an available progress plot using a path relative to the report file. JSON and the report artifact appendix retain absolute paths for provenance.
The JSON summary remains nvflare.autofl.report.v1 and includes
artifacts.progress_plot_available and objective.metric_contract_source,
plus selection, outcome_summary, abandoned_candidates, and
state_accounting for the concise evidence synthesis and consistency check.
Each literature_reviews entry carries completion, either an object with
completed, required, complete, and reason, or null when campaign state
does not record a matching exploration batch.
Missing plotting dependencies or an invalid existing PNG produce warnings and
progress_plot_available=false, but do not suppress the Markdown or JSON
report. The invalid or failed plot artifact is preserved and is not embedded
in Markdown.
All relative helper path arguments, including --plotter, resolve from the
campaign directory rather than the invoking shell's current working directory.
Before plotting or writing, the helper canonicalizes the three writable output
paths and rejects aliases with the ledger, state, config, agent context,
plotter, campaign lock, candidate manifests, job.py, or files listed in
trust_contract.allowed_edit_paths. Outputs within the campaign directory
must not match trust_contract.allowed_create_patterns. The progress,
Markdown, and JSON outputs must also be mutually distinct. Case-folded paths
and existing symlink or hard-link aliases are treated as collisions so a
configuration is portable to case-insensitive filesystems.
Report generation must be independent of Git and must not edit campaign source, the ledger, manifests, or state.
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
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