Agent Skills: Cheapskate Skill

Cheapskate Skill

UncategorizedID: plurigrid/asi/cheapskate

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pnpm dlx add-skill https://github.com/plurigrid/asi/tree/HEAD/plugins/asi/skills/cheapskate

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plugins/asi/skills/cheapskate/SKILL.md

Skill Metadata

Name
cheapskate
Description
Cheapskate Skill

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

See Also

  • parallel-fanout - Efficient parallel dispatch
  • triad-interleave - Balanced token streams
  • frustration-eradication - Don't waste tokens on frustration

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.