Skill Benchmark: amc-run-video-calibration
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
- Skill:
amc-run-video-calibration - Evaluation date: 2026-08-08
- Evaluator version:
1.1.2 - Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5) - Tasks: 6 evaluation tasks (5 positive, 1 negative)
- Dataset digest:
sha256:27e110482838494c58572fb5c4852aa0ba9c36f0b5f423ee26f53e1a25ed3ed0(skill-evaluator-dataset-snapshot/1) - Attempts per task: 1
- Environment:
k8s-sandbox - 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
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 49% → 82% (+33 points) | 35% → 83% (+48 points) |
| Security | 92% → 92% (±0 points) | 42% → 100% (+58 points) |
| Correctness | 17% → 83% (+67 points) | 13% → 77% (+63 points) |
| Discoverability | 53% → 93% (+40 points) | 47% → 89% (+42 points) |
| Effectiveness | 30% → 50% (+20 points) | 27% → 49% (+21 points) |
| Efficiency | 54% → 91% (+37 points) | 47% → 100% (+53 points) |
How to read this table: baseline is the same task attempted without the target skill. Uplift is skill score - baseline score, shown in percentage points.
Example: 47% → 92% (+45 points) means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED | 1 validator(s); 0 finding(s) |
| Tier 2 | Semantic deduplication | NOT RUN | No result was recorded |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 6 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>- Schema & Repository Governance: Found skill manifest: SKILL.md
- AGENT_EVAL: Tier 3 evaluation complete: verdict PASS; best agent codex
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did it avoid wasted tool or skill usage? | skill_efficiency (100%) |
- 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). - Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was found and executed.skill_efficiency(Efficiency): routing quality, workspace-aware skill reads, and productive tool use.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.
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.