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Eval Report · agentscope-ai | DecimalAI

ai mlby agentscope-ai

Eval Report

agentscope-ai/eval-report

Source agentscope-ai/openjudge ↗License Apache-2.0

Use when the user has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis skill.

SkillScore76Maintained

Automatic

AI Engineer pack

Last verified 8/14/2026

What it changes

Passed 17 of 22 test cases with the skill, and 10 of 22 without it.

Without skill44%

With skill76%

Checks it passed that the model alone missed

Classifies the root cause as metric_problem
Categorizes the finding under cross-skill contradictions
Limits the executive summary presentation to the top 3 risks

and 7 more in the full report

10 fixed9 passed either way4 still failing2 broke

Pass-rate lift: +32 pp
Tokens: +126%
Agent turns: 0%
Response time: -23%

Model gemini-3.6-flashMeasured 8/14/2026v119 of 25 cases passed with skill · 22 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/agentscope-ai-eval-report/SKILL.md https://app.decimal.ai/s/agentscope-ai-eval-report/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add agentscope-ai-eval-report, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/agentscope-ai-eval-report/ and pins it in .decimal/skills.lock

decimalai skills add agentscope-ai-eval-report

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/agentscope-ai-eval-report/SKILL.md https://app.decimal.ai/s/agentscope-ai-eval-report/SKILL.md

Raw file: /s/agentscope-ai-eval-report/SKILL.md ↗ · machine-readable summary at /s/agentscope-ai-eval-report.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 10

Prompt

In Step 4 of the diagnostic process, what three analytical perspectives are evaluated to pinpoint weaknesses across test runs?

What was checked, without and with the skill
Checked	Without skill	With skill
Evaluates failure concentration, failure correlation, and per-stratum weakness	Missed	Passed

case-23 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md224 linessingle file

Eval Report

Synthesize everything from your evaluation journey into a comprehensive report. This skill is read-only — it analyzes what exists, doesn't create new graders or datasets.

When to Activate

You've run 2+ evaluation skills and want the big picture
You need to report evaluation status to non-technical stakeholders
You're making a ship/no-ship decision and need evidence
The evaluation system has been running for a while — time for a health check

Checklist

You MUST create a task for each item and complete them in order:

Inventory scan — catalog everything in eval-design.md + runs/ history
Maturity assessment — 5 dimensions × 4 levels
Cross-skill signal synthesis — consistent findings + contradictions
Weakness diagnosis — failure concentration, correlations, stratum gaps
Root cause classification — system / metric / data / unclear
Prioritized recommendations — P0/P1/P2 actions with impact estimates
Executive summary — ship readiness + top 3 risks + next actions

Step 1: Inventory Scan

Read eval-design.md and all runs/ directories. Build a timeline:

Timeline:
  2026-04-15  01-eval-design   → 5 failure modes → 3 dimensions from 200 traces
  2026-04-18  02-metric-design → 4 graders configured (2 LLM + 1 rule + 1 executable)
  2026-04-25  (evaluation run) → 90-sample stratified dataset scored
  2026-05-01  03-align-human   → 2 judges Phase 3, 1 Phase 2, 1 Phase 1 (TPR/TNR + kappa)

Track record

Benchmark

17 / 22

test cases passed, vs 10 / 22 without

Adoption

Users: 1
Times used: 34

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

agentscope-ai / openjudge

skills/eval_pipeline/04-eval-report/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Eval Report

When to Activate

Checklist

Step 1: Inventory Scan

Track record

Benchmark

Adoption

SkillSafety

Source

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