
Token Efficiency
- 92 installs
- 2.8k repo stars
- Updated August 3, 2026
- rohitg00/pro-workflow
Helps with ai & agent building tasks during AI-assisted development.
About
token-efficiency is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- token-efficiency
- AI & Agent Building
- AI-coding skill
Token Efficiency by the numbers
- 92 all-time installs (skills.sh)
- +19 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #4,749 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 92 |
|---|---|
| repo stars | ★ 2.8k |
| Last updated | August 3, 2026 |
| Repository | rohitg00/pro-workflow ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
Token Efficiency
Reduce output token waste and prevent iteration cycles that consume context.
Trigger
Use when:
- Sessions feel expensive or slow
- Output is verbose with filler text
- Claude is re-reading files or iterating unnecessarily
- Setting up a new project for token-efficient work
Anti-Sycophancy Rules
These patterns waste 30-60% of output tokens:
| Pattern | Example | Fix |
|---|---|---|
| Sycophantic opener | "Sure! Great question!" | Delete. Lead with answer. |
| Prompt restatement | "You're asking about X..." | Delete. Answer directly. |
| Closing fluff | "Let me know if you need anything!" | Delete. Stop after the answer. |
| Unsolicited suggestions | "You might also want to..." | Delete unless asked. |
| AI disclaimers | "As an AI model..." | Delete entirely. |
| Verbose preambles | "I'll help you with that..." | Delete. Start with the action. |
Tool-Call Budgets
Set explicit budgets by task complexity:
| Task Type | Tool-Call Budget | Wrap-Up At |
|---|---|---|
| Quick fix / lookup | 20 calls | 15 |
| Bug fix | 30 calls | 25 |
| Feature (small) | 50 calls | 40 |
| Feature (large) | 80 calls | 65 |
| Refactor | 50 calls | 40 |
| Exploration / research | 30 calls | 25 |
At the wrap-up threshold: commit progress, assess remaining work, decide whether to continue or start fresh.
One-Pass Coding Discipline
For simple-to-medium tasks:
1. Read all relevant files including tests first 2. Understand what tests assert before coding 3. Write complete solution in one pass — not incrementally 4. Run tests once — if pass, STOP immediately 5. If fail: read the error, fix once, retest 6. Never iterate more than twice on the same failure — rethink approach 7. Never refactor, improve, or polish passing code
Task Profiles
Switch profiles based on what you're doing:
Coding Profile
- Return code first, explanation after (only if non-obvious)
- Simplest working solution, no over-engineering
- Read file before modifying — always
- No docstrings on unchanged code
- No error handling for impossible scenarios
- State bug, show fix, stop
Agent/Pipeline Profile
- Structured output only: JSON, bullets, tables
- No prose unless targeting a human reader
- Every output must be parseable without post-processing
- Execute task, do not narrate actions
- Never invent file paths, API endpoints, or function names
- If unknown: return null or "UNKNOWN", never guess
Analysis Profile
- Lead with finding, context and methodology after
- Tables and bullets over prose
- Numbers must include units
- Never fabricate data points
- Summary first (3 bullets max), caveats last
Read-Before-Write Enforcement
Hard rules: 1. Never write a file you haven't read in this session 2. Never re-read a file already read unless it was modified 3. Read tests before coding — understand what passes before writing 4. Read error output carefully before attempting a fix
ASCII-Only Output
Use ASCII characters only in all output:
--not—(em dash)"not""(smart quotes)'not''(curly apostrophes)- No emoji unless explicitly requested
- No Unicode decorators or special characters
This ensures clean copy-paste for code and compatibility with downstream systems.
Measuring Impact
Track these metrics to measure token savings:
- Output length: average words per response (target: 30-50% reduction)
- Tool calls per task: should stay within budget tier
- Re-read count: should be near zero
- Write-without-read count: should be zero
- Iteration cycles: tests should pass in 1-2 attempts, not 5+
Attribution
Token efficiency patterns adapted from drona23/claude-token-efficient (MIT).