Tools Impact Engine Evaluate
B+ 88 completed
Ai Ml
unknown / python · tiny
45
Files
2,170
LOC
1
Frameworks
4
Languages
Pipeline State
completedRun ID
#299181Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
45.99Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #48287
Member of a group with 1 similar repo(s) — canonical #6293 view group →
Top concepts (4)
testingConfigurationDatabaseTesting
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🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
score_initiativeCalculates a structured evaluation result for a given initiative dictionary by deterministically generating a confidence score. It accepts a dictionary containing various metrics like model type, cost, and effect estimates. The function returns a dictionary representing the serialized evaluation res
ArtifactReview.executeReviews an artifact using an internal engine, accepting a dictionary representing a pipeline event that must contain an initiative ID and artifact text. It processes this event into a payload, passes it to the engine for scoring, and logs the initiative ID and overall score. The function returns a d
_render_templateRenders a template string by substituting placeholders with provided values. It accepts a template string and a dictionary of variables as inputs, returning the resulting formatted string as output. If the jinja2 library is unavailable, it attempts a fallback mechanism by sanitizing the template to
AI Prompt
Create a Python tool for evaluating the reliability of causal impact estimates, similar to the Impact Engine Evaluate project. The core functionality should involve calculating a confidence score for various estimates based on their measurement design. This score should then be used to penalize downstream return estimates, making the system conservative when evidence is weak and aggressive when evidence is strong. Please structure the project to include testing using pytest and manage configuration using YAML or TOML files.
python testing causal-inference impact-engine evaluation pytest data-science
Generated by gemma4:latest
Catalog Information
The impact-engine-evaluate project provides confidence scoring and agentic artifact review for the impact engine pipeline.
Description
This project evaluates the impact of various artifacts in a pipeline, providing confidence scores to help improve decision-making. It uses machine learning techniques to analyze data and provide actionable insights. The project is designed to be used in conjunction with other tools in the impact engine pipeline. Its primary function is to review and score artifacts based on their potential impact.
الوصف
هذا المشروع يقوم بتقدير الثقة وتقييم الأعمال المتمثلة في خطوط تدفق التأثير. يستخدم تقنيات التعلم الآلي لتحليل البيانات وتقديم استنتاجات قابلة للتنفيذ. يهدف المشروع إلى استخدامها مع أدوات أخرى في خطوط تدفق التأثير. وظيفته الرئيسية هي مراجعة وتقييم الأعمال على أساس تأثيرها المحتمل.
Novelty
7/10Tags
confidence-scoring impact-evaluation pipeline-review machine-learning decision-support artifact-analysis
Technologies
anthropic numpy openai
Claude Models
claude-opus-4.6 claude-sonnet-4.6
Quality Score
B+
88.0/100
Structure
80
Code Quality
100
Documentation
80
Testing
85
Practices
82
Security
100
Dependencies
90
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (63% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
Recommendations
- Add a LICENSE file (MIT recommended for open source)
Security & Health
4.1h
Tech Debt (D)
Medium
DORA Rating
A
OWASP (100%)
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PASS
Quality Gate
A
Risk (4)
Unknown
License
6.8%
Duplication
Languages
Frameworks
pytest
Symbols
variable67
method28
class22
function12
constant11
protocol2
Concepts (4)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Repobility · MCP-ready · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Configuration | Detected from 4 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 2 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 12 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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