Cheapskate Skill

Trit: -1 (MINUS - validator/constrainer) Purpose: Minimize Amp thread costs through token efficiency

Core Principles

1. Token Conservation

Terse responses: 1-3 sentences unless detail requested
No preamble/postamble: Skip "I'll help you with..." and summaries
Code over prose: Show code, not explanations
Links over content: Reference files, don't paste them

2. Tool Call Efficiency

Parallel reads: Batch independent Read/Grep calls
Targeted searches: Use glob patterns, not broad scans
Single-pass edits: Plan before editing, don't iterate
Skip redundant checks: Trust previous results

3. Subagent Economics

Task tool for isolation: Heavy work in subagents (tokens not returned)
Bounded prompts: Subagent prompts < 500 tokens
No round-trips: Give subagents full context upfront
Kill early: Cancel subagents if direction changes

4. Context Window Management

Skill loading: Only load skills when needed
File excerpts: Read ranges, not full files
Summarize large outputs: Truncate verbose tool results
Avoid re-reading: Cache file contents mentally

Anti-Patterns (Token Wasters)

Pattern	Cost	Fix
Reading entire files	High	Use line ranges `[1, 50]`
Sequential tool calls	Medium	Parallelize independents
Explaining before doing	Medium	Just do it
Asking permission	Low-Medium	Act, don't ask
Repeating user's question	Low	Skip acknowledgment
Long error explanations	Medium	Terse: "Error: X. Fix: Y"
Multiple edit iterations	High	Plan first, single edit
Loading unused skills	Medium	Load on-demand

Efficient Patterns

File Operations

# Bad: Read full 2000-line file
Read("/path/to/big.py")

# Good: Read relevant section
Read("/path/to/big.py", [100, 150])

# Better: Grep first, then targeted read
Grep("def target_function", path="/path/to/big.py")
Read("/path/to/big.py", [142, 165])

Parallel Execution

# Bad: Sequential
Read(file1) → Read(file2) → Read(file3)

# Good: Parallel (single message, 3 tool calls)
Read(file1) | Read(file2) | Read(file3)

Subagent Dispatch

# Bad: Heavy work in main thread (tokens visible)
[read 10 files, analyze, generate report]

# Good: Subagent isolation (only summary returned)
Task("Analyze 10 files, return 3-line summary")

Response Length

# Bad (47 tokens)
"I'll help you implement that feature. Let me start by 
examining the codebase to understand the current architecture,
then I'll make the necessary changes..."

# Good (3 tokens)
[starts making changes]

Cost Estimation Heuristics

Operation	~Tokens
Read 100 lines code	400-800
Grep results (10 matches)	200-400
Edit file	100-300
Skill load	500-2000
Task subagent prompt	200-500
Task subagent result	100-500
Web search result	500-1500
Mermaid diagram	100-300

Cheapskate Checklist

Before responding:

Can I answer in < 3 sentences?
Are all tool calls parallelized?
Am I reading only what's needed?
Should this be a subagent (isolated tokens)?
Did I skip the preamble?
Did I skip the summary?

GF(3) Integration

As MINUS (-1) validator:

Constrains token expenditure
Validates efficiency of other skills
Balances PLUS generators (which produce tokens)

Σ(generator_tokens) + Σ(validator_savings) ≡ 0 (mod 3)

Commands

# Analyze thread token usage
just cheapskate-analyze <thread-id>

# Estimate remaining budget
just cheapskate-budget

# Compress context
just cheapskate-compress

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

networkx [○] via bicomodule
- Universal graph hub

Bibliography References

general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.

cheapskate