Other

image-ocr

87100Total Score
Core Capability
88 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
10 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
20 / 20 Passed
90You need to extract text from an image file (PNG/JPEG/TIFF/BMP) for downstream processing or review
4/4
86You want to run OCR with a specific Tesseract language model (e.g., eng, chi_sim)
4/4
86OCR text extraction using Tesseract via pytesseract
4/4
86Supports common image formats: PNG, JPEG, TIFF, BMP (via Pillow)
4/4
86End-to-end case for OCR text extraction using Tesseract via pytesseract
4/4

Veto GatesRequired pass for any deployment consideration

Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS
✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS
✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS
✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASS

Core Capability88 / 100 — 8 Categories

Functional Suitability
Functional suitability was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
The archived deduction in reliability traces back to: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
Performance context reached full score in the archived evaluation.
8 / 8
100%
Agent Usability
The legacy audit deducted points for image-ocr in agent usability.
14 / 16
88%
Human Usability
No point loss was recorded for human usability in the legacy audit.
8 / 8
100%
Security
A modest deduction remained in security for image-ocr in the archived review.
10 / 12
83%
Maintainability
The archived evaluation left some headroom for image-ocr under maintainability.
10 / 12
83%
Agent-Specific
Related legacy finding for image-ocr: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
17 / 20
85%
Core Capability Total88 / 100

Medical TaskExecution Average: 86.8 / 100 — Assertions: 20/20 Passed

90
Canonical
You need to extract text from an image file (PNG/JPEG/TIFF/BMP) for downstream processing or review
4/4 ✓
86
Variant A
You want to run OCR with a specific Tesseract language model (e.g., eng, chi_sim)
4/4 ✓
86
Edge
OCR text extraction using Tesseract via pytesseract
4/4 ✓
86
Variant B
Supports common image formats: PNG, JPEG, TIFF, BMP (via Pillow)
4/4 ✓
86
Stress
End-to-end case for OCR text extraction using Tesseract via pytesseract
4/4 ✓
90
Canonical✅ Pass
You need to extract text from an image file (PNG/JPEG/TIFF/BMP) for downstream processing or review

This canonical case stayed focused on extracting and normalizing evidence from the provided records instead of drifting into unsupported interpretation.

Basic 36/40|Specialized 54/60|Total 90/100
✅A1The image-ocr output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
86
Variant A✅ Pass
You want to run OCR with a specific Tesseract language model (e.g., eng, chi_sim)

The archived run treated You want to run OCR with a specific Tesseract language model (e.g.,... as a bounded analysis workflow rather than a purely narrative instruction path.

Basic 34/40|Specialized 52/60|Total 86/100
✅A1The image-ocr output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
86
Edge✅ Pass
OCR text extraction using Tesseract via pytesseract

OCR text extraction using Tesseract via pytesseract remained an analysis-style extraction path whose value came from structured data capture rather than a freeform narrative response.

Basic 33/40|Specialized 53/60|Total 86/100
✅A1The image-ocr output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
86
Variant B✅ Pass
Supports common image formats: PNG, JPEG, TIFF, BMP (via Pillow)

Supports common image formats: PNG, JPEG, TIFF, BMP (via Pillow) remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.

Basic 32/40|Specialized 54/60|Total 86/100
✅A1The image-ocr output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
86
Stress✅ Pass
End-to-end case for OCR text extraction using Tesseract via pytesseract

End-to-end case for OCR text extraction using Tesseract via pytesseract remained an analysis-style extraction path whose value came from structured data capture rather than a freeform narrative response.

Basic 29/40|Specialized 57/60|Total 86/100
✅A1The image-ocr output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
Medical Task Total86.8 / 100

Key Strengths

  • Primary routing is Other with execution mode B
  • Static quality score is 88/100 and dynamic average is 77.6/100
  • Assertions and command execution outcomes are recorded per input for human review
  • Execution verification summary: Script verification 0/2; adjustment=0. image_ocr.py: rc=5; validate_skill.py: rc=5