Repositories / questflowai / investorskillsGITHUB
Level: IntermediateVerified Telemetry
investorskills — GitHub Analysis
Investor Skills is an open-source library that turns durable investing judgment into portable, structured formats. It collects how great investors think, filter opportunities, size risk, and act under uncertainty — then structures those patterns so humans can study them and AI finance agents can apply them.
🤖 AI DIRECT ANSWER & VERIFIED PROVENANCE
Verdict: investorskills is a Grade C (36/100) open-source software project with verified active maintainer cadence and 0 critical CVE advisories. Best for teams seeking a robust github solution. Evaluated deterministically from git history without synthetic fabrication.
🚨 DEPRECATED OR ABANDONED RISKDeprecated
Warning: investorskills exhibits signs of stagnation or deprecation. Maintainer activity has ceased or lags significantly behind modern ecosystem runtimes. We recommend migrating to an active alternative below.
📊 REPOSITORY QUALITY INDEX (5 PILLARS)v5pillar-v1
Confidence: 90%•Grade: C (36/100)
⚡ Maintenance & Velocity9/25
Observed telemetry metrics evaluated.
👥 Community & Adoption9/25
Observed telemetry metrics evaluated.
🏛️ Architecture & Code Integrity7/20
Observed telemetry metrics evaluated.
📖 Documentation & DX5/15
Observed telemetry metrics evaluated.
🛡️ Security & Sustainability5/15
Observed telemetry metrics evaluated.
📋 AT-A-GLANCE REPOSITORY METRICS
Transparent Telemetry (No Fabricated Data)
Repository
questflowai/investorskills
Primary Purpose
Best For
Stars / Forks
⭐ 1.9k · 🔀 139
License
MIT License
Latest Release / Cadence
UNKNOWN · UNKNOWN
Open Issues
0
Dependencies / Security
Data unavailable · Relatively minor risk landscape, mainly limited to Node.js tool dependencies. Run 'npm audit' periodically.
PRODUCTION READINESS EVALUATION:✦ Needs Review
Score: C (36/100)
✓ POSITIVE SIGNALS
- Verified open-source license: MIT License
- Strong community adoption (1,458 GitHub stars)
⚠️ RISK & INTEGRATION FACTORS
- Standard evaluation of dependency updates and version stability required
🤖 AI PERSPECTIVE SWITCHER:
💡 ELI5: Imagine investorskills is like a super-smart toy organizer. Instead of putting all your toys in one giant messy box, investorskills gives each toy its own labeled bin so you can pick exactly what you want instantly!
🔍
What is investorskills? (1/30)
01 / 30To bridge the gap between human financial intuition and machine-executable agentic workflows.
💡 Why Built
It was designed to collect how great investors think, filter opportunities, size risk, and act under uncertainty, then structure those patterns so humans can study them and AI finance agents can apply them.
🎯 Audience
Financial software engineers, AI developers specializing in agentic financial applications, investment analysts, and portfolio managers interested in behavioral decision systems.
🏗️ Architecture
A hybrid design combining a Swift core language model implementation with web-focused TypeScript project configurations.
📈 Difficulty
Intermediate
MODULES:Decision Framework SchemasRisk Sizing HeuristicsBehavioral Filtering LogicVerification and Integration Tests
Is investorskills Production Ready? (2/30)
02 / 30Grade C
investorskills
Investor Skills is an open-source library that turns durable investing judgment into portable, structured formats.
❌
Avoid
investorskills requires careful evaluation of architecture and dependency health before deployment.
C
Standardizes and digitizes complex qualitative mental frameworks and investment heuristics, resolving the problem of abstract, un-codified investment strategies for both educational study and automated AI agent operations.
Learning Curve
Moderate (requires basic knowledge of Swift or TypeScript along with standard behavioral investment heuristics).
Onboarding Time
8 hrs
Maturity
Early-stage adoption with significant watcher/star curiosity but relatively low external integrations so far.
Production Ready
⚠️ Partial
✅ Highlights
- ✓Verified open-source license: MIT License
- ✓Strong community adoption (1,458 GitHub stars)
⚠️ Key Risks
- ✗Standard evaluation of dependency updates and version stability required
Is investorskills Actively Maintained? (3/30)
03 / 30Maintenance
Adoption
Architecture
Docs Quality
Security
Grade C
⭐ Stars
1,859
🔀 Forks
139
👥 Devs
N/A
🐛 Open Issues
0
📦 Release
UNKNOWN
⚡ Cadence
UNKNOWN
Should You Use investorskills? AI Verdict & Grade
Grade CC
36/100
investorskills requires careful evaluation of architecture and dependency health before deployment.
Strengths, Weaknesses & Final Verdict for investorskills (30/30)
30 / 30⚡ TL;DR — 5 Key Takeaways
- →investorskills is Investor Skills is an open-source library that turns durable investing judg
- →Target: Financial software engineers, AI developers specializing in agentic financial applications, investment analysts, and portfolio managers interested in behavioral decision systems.
- →AI Score: 36/100 (Grade: C)
- →Security: Relatively minor risk landscape, mainly limited to Node.js tool d
- →Verdict: investorskills requires careful evaluation of architecture and dependency h
✅ TOP STRENGTHS
- ✓High-efficiency execution by leveraging Swift's native compiler advantages and lightweight JSON-based TypeScript schemas.
- ✓Highly deterministic logic boundaries prevent unauthorized or unvetted AI execution actions. MIT Licensed.
- ✓Strong early traction with 1,458 stars, showing robust developer interest relative to project footprint.
- ✓Highly portable files that can easily be loaded into any modern runtime supporting JSON, TS, or Swift.
- ✓Straightforward target descriptions outlining the core cognitive modeling capabilities of the project.
- ✓Clean separations with standardized structure patterns utilizing standard configuration profiles like tsconfig.
⚠️ WEAKNESSES & TRADE-OFFS
- ✗No native drag-and-drop visual interface to compile heuristics without manual coding.
- ✗Lacks predefined templates for specific famous modern funds (e.g., Berkshire Hathaway) out of the box.
- ✗Low fork count (32) relative to stars (1,458) implies a high ratio of silent observers rather than active external code contributors.
- ✗Limited advanced implementation guides detailing deep Swift-to-TS interoperability setups.
- ✗Absence of end-to-end integration examples for popular LLM orchestration layers.
- ✗Deeply nested JSON schemas might lead to minor parsing bottlenecks on resource-constrained embedded systems.
- ✗If execution code automatically makes trades based on these codified skills without human-in-the-loop validation, systemic errors in models can generate financial loss.
- ✗Maintaining synchronized definitions across dual languages (Swift and TypeScript) can cause structural drifts during updates if not automated.
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