Nvflare Shared

Skill Benchmark: nvflare-shared

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

• Skill: nvflare-shared

• Evaluation date: 2026-09-12

• Evaluator version: 1.5.6

• Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)

• Tasks: 3 evaluation tasks (3 positive)

• Dataset digest: sha256:0267fb75cdd17b87a2fc8e8a481b55058ed90097b92f97f5a9c8dba18724d58e (skill-evaluator-dataset-snapshot/1)

• Attempts per task: 3

• Environment: k8s-sandbox

• Tier 2 evidence: required for publication

• Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

• is safe to use;

• produces correct answers;

• is discovered and activated when needed;

• helps the agent complete the user's goal and expected workflow; and

• avoids wasted skill and tool usage.

Results at a Glance

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

• MEDIUM QUALITY/quality_efficiency: Instructions lack clear action verbs (skills/nvflare-shared/SKILL.md)

• MEDIUM SECURITY/Unknown (SQP-2): The guidance in lines 19–28 instructs the agent to write or update files (e.g., client.py, job.py, model.py, requ (references/runtime-output-guidance.md:19`)

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Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

• Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

• Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

• Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

• The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

• Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

• Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

• security (Security): unsafe operations, secret leakage, and unauthorized access.

• skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

• skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

• accuracy (Accuracy): final-answer correctness against the reference answer.

• goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

• behavior_check (Behavior Check): whether the expected workflow behavior was followed.

• token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

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Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.