Taxonomy Distribution Report

Overview

• Total Concepts: 475
• Number of Taxonomies: 15
• Average Concepts per Taxonomy: 31.7

Distribution Summary

Category	TaxonomyID	Count	Percentage	Status
OBS	OBS	65	13.7%	✅
Foundation Concepts - Prerequisites	FOUND	50	10.5%	✅
OPT	OPT	45	9.5%	✅
RAG	RAG	40	8.4%	✅
BUDG	BUDG	35	7.4%	✅
ROUT	ROUT	30	6.3%	✅
HARN	HARN	28	5.9%	✅
ECON	ECON	27	5.7%	✅
ANTH	ANTH	25	5.3%	✅
OAI	OAI	25	5.3%	✅
SKIL	SKIL	25	5.3%	✅
AB	AB	25	5.3%	✅
GOOG	GOOG	20	4.2%	✅
CAP	CAP	20	4.2%	✅
PRIV	PRIV	15	3.2%	✅

Visual Distribution

OBS                       ██████  65 ( 13.7%)
Foundation Concepts - Pre █████  50 ( 10.5%)
OPT                       ████  45 (  9.5%)
RAG                       ████  40 (  8.4%)
BUDG                      ███  35 (  7.4%)
ROUT                      ███  30 (  6.3%)
HARN                      ██  28 (  5.9%)
ECON                      ██  27 (  5.7%)
ANTH                      ██  25 (  5.3%)
OAI                       ██  25 (  5.3%)
SKIL                      ██  25 (  5.3%)
AB                        ██  25 (  5.3%)
GOOG                      ██  20 (  4.2%)
CAP                       ██  20 (  4.2%)
PRIV                      █  15 (  3.2%)

Balance Analysis

✅ No Over-Represented Categories

All categories are under the 30% threshold. Good balance!

Category Details

OBS (OBS)

Count: 65 concepts (13.7%)

Concepts:

• 1. Structured Logging
• 1. Log Schema Design
• 1. Log Line
• 1. JSON Log Format
• 1. Log Field
• 1. Required Log Field
• 1. Optional Log Field
• 1. Model Field
• 1. Prompt Hash
• 1. Input Token Field
• 1. Output Token Field
• 1. Cached Token Field
• 1. Latency Field
• 1. Cost Field
• 1. Feature Tag
• ...and 50 more

Foundation Concepts - Prerequisites (FOUND)

Count: 50 concepts (10.5%)

Concepts:

• 1. Generative AI
• 1. Large Language Model
• 1. Foundation Model
• 1. Transformer Architecture
• 1. Autoregressive Generation
• 1. Token
• 1. Input Token
• 1. Output Token
• 1. Cached Token
• 1. Reasoning Token
• 1. Token Count
• 1. Tokenizer
• 1. Byte Pair Encoding
• 1. SentencePiece
• 1. Vocabulary Size
• ...and 35 more

OPT (OPT)

Count: 45 concepts (9.5%)

Concepts:

• 1. Prompt Engineering
• 1. System Prompt Hygiene
• 1. Instruction Compression
• 1. Few-Shot Example
• 1. Few-Shot Pruning
• 1. Zero-Shot Prompting
• 1. Chain Of Thought
• 1. Dead Context
• 1. Redundant Instruction
• 1. Verbose Boilerplate
• 1. Prompt Template
• 1. Template Versioning
• 1. Prompt Variable
• 1. Variable Interpolation
• 1. Prompt Compression Tool
• ...and 30 more

RAG (RAG)

Count: 40 concepts (8.4%)

Concepts:

• 1. Retrieval Augmented Generation
• 1. Embedding
• 1. Vector Database
• 1. Chunking
• 1. Chunk Size
• 1. Chunk Overlap
• 1. Top-K Retrieval
• 1. Reranker
• 1. Cross-Encoder Reranker
• 1. Retrieval Score
• 1. Context Injection
• 1. Retrieved Context Bloat
• 1. Context Pruning
• 1. Hybrid Retrieval
• 1. BM25 Retrieval
• ...and 25 more

BUDG (BUDG)

Count: 35 concepts (7.4%)

Concepts:

• 1. Agent Budget Policy
• 1. Per-Session Token Budget
• 1. Per-Session Tool Call Budget
• 1. Loop Iteration Limit
• 1. Wall Clock Limit
• 1. Cost Cap
• 1. Graceful Degradation
• 1. Budget Exhaustion Handling
• 1. Runaway Detection
• 1. Circuit Breaker Pattern
• 1. Tool Call Throttling
• 1. Subtask Budget Allocation
• 1. Budget Audit Log
• 1. Budget Reporting
• 1. Per-Engineer Budget
• ...and 20 more

ROUT (ROUT)

Count: 30 concepts (6.3%)

Concepts:

• 1. Model Routing
• 1. Cheap-First Cascade
• 1. Escalation Trigger
• 1. Confidence Threshold
• 1. Quality Gate
• 1. Fallback Model
• 1. Cross-Vendor Routing
• 1. Task Classifier
• 1. Difficulty Estimation
• 1. Routing Policy
• 1. Routing Cost Savings
• 1. Routing Quality Risk
• 1. Per-Task Model Selection
• 1. Vendor Lock-In Risk
• 1. Vendor-Neutral Abstraction
• ...and 15 more

HARN (HARN)

Count: 28 concepts (5.9%)

Concepts:

• 1. AI Coding Harness
• 1. Agentic Loop
• 1. Tool Use Loop
• 1. Claude Code
• 1. Claude Code Session
• 1. Claude Code Hooks
• 1. OpenAI Codex CLI
• 1. Codex Session
• 1. Google Antigravity
• 1. Antigravity Workspace
• 1. Harness System Prompt
• 1. Harness Token Overhead
• 1. Session Token Accumulation
• 1. Per-Session Token Cost
• 1. Conversation Compaction
• ...and 13 more

ECON (ECON)

Count: 27 concepts (5.7%)

Concepts:

• 1. Per-Million-Token Price
• 1. Input Token Price
• 1. Output Token Price
• 1. Cached Input Price
• 1. Output Premium
• 1. Unit Economics
• 1. Cost Per Request
• 1. Cost Per Feature
• 1. Cost Per User
• 1. Cost Per Outcome
• 1. Cost Attribution
• 1. Token Budget
• 1. Monthly Token Spend
• 1. Forecasting Token Cost
• 1. Cost-Quality Tradeoff
• ...and 12 more

ANTH (ANTH)

Count: 25 concepts (5.3%)

Concepts:

• 1. Anthropic API
• 1. Claude Messages API
• 1. Claude Model Family
• 1. Claude Opus
• 1. Claude Sonnet
• 1. Claude Haiku
• 1. Anthropic SDK
• 1. API Key Management
• 1. Anthropic Prompt Caching
• 1. Cache Control Parameter
• 1. Cache Breakpoint
• 1. Cache TTL
• 1. Cache Read Tokens
• 1. Cache Write Tokens
• 1. Extended Thinking
• ...and 10 more

OAI (OAI)

Count: 25 concepts (5.3%)

Concepts:

• 1. OpenAI API
• 1. Chat Completions API
• 1. OpenAI Responses API
• 1. OpenAI Model Family
• 1. GPT Model Series
• 1. OpenAI O Series
• 1. Reasoning Model
• 1. OpenAI SDK
• 1. Function Calling
• 1. Tool Choice Parameter
• 1. JSON Mode
• 1. Structured Outputs
• 1. Response Format
• 1. OpenAI Streaming
• 1. OpenAI Batch API
• ...and 10 more

SKIL (SKIL)

Count: 25 concepts (5.3%)

Concepts:

• 1. Skill
• 1. Skill Description
• 1. Skill Body
• 1. Skill Trigger
• 1. Skill Invocation
• 1. Skill Frontmatter
• 1. Skill Bundle
• 1. Bundled Script
• 1. Skill Asset File
• 1. Lazy Skill Loading
• 1. Eager Skill Listing
• 1. Task Decomposition
• 1. Task-Skill Binding
• 1. Skill Selection
• 1. Skill Misfire
• ...and 10 more

AB (AB)

Count: 25 concepts (5.3%)

Concepts:

• 1. A/B Testing
• 1. Hypothesis
• 1. Null Hypothesis
• 1. Control Group
• 1. Treatment Group
• 1. Traffic Split
• 1. Random Assignment
• 1. Stratified Assignment
• 1. Primary Metric
• 1. Guardrail Metric
• 1. Quality Metric
• 1. Cost Metric
• 1. Latency Metric
• 1. Satisfaction Metric
• 1. Sample Size Calculation
• ...and 10 more

GOOG (GOOG)

Count: 20 concepts (4.2%)

Concepts:

• 1. Google Gemini API
• 1. Gemini Model Family
• 1. Gemini Pro
• 1. Gemini Flash
• 1. Gemini Ultra
• 1. Gemini SDK
• 1. Long Context Window
• 1. One Million Context
• 1. Gemini Function Calling
• 1. Gemini Tool Config
• 1. Gemini Streaming
• 1. Gemini Batch Mode
• 1. Gemini Caching
• 1. Vertex AI
• 1. Google AI Studio
• ...and 5 more

CAP (CAP)

Count: 20 concepts (4.2%)

Concepts:

• 1. Baseline Cost Measurement
• 1. Optimization Hypothesis
• 1. Quality Regression Detection
• 1. Before-After Report
• 1. Optimization Backlog
• 1. Cost Reduction Target
• 1. Pilot Rollout
• 1. Canary Deployment
• 1. Token Dashboard Project
• 1. Vendor-Neutral Logging Project
• 1. Skill Refactor Project
• 1. Budget Policy Document
• 1. Engineering Manager Review
• 1. Cost Reduction Postmortem
• 1. Reproducible Benchmark
• ...and 5 more

PRIV (PRIV)

Count: 15 concepts (3.2%)

Concepts:

• 1. Data Privacy
• 1. PII Detection
• 1. Sensitive Field Redaction
• 1. Compliance Risk
• 1. GDPR
• 1. HIPAA
• 1. SOC2 Audit
• 1. Data Residency
• 1. Vendor Data Retention
• 1. Opt-Out Of Training
• 1. Logging Privacy Risk
• 1. Hashing Sensitive Strings
• 1. Tokenized Identifier
• 1. Audit Trail
• 1. Anonymization Strategy

Recommendations

• ✅ Excellent balance: Categories are evenly distributed (spread: 10.5%)
• ✅ MISC category minimal: Good categorization specificity

Educational Use Recommendations

• Use taxonomy categories for color-coding in graph visualizations
• Design curriculum modules based on taxonomy groupings
• Create filtered views for focused learning paths
• Use categories for assessment organization
• Enable navigation by topic area in interactive tools

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Report generated by learning-graph-reports/taxonomy_distribution.py