How do I install the Token Hobo skill?

Run npx skillmds@latest add kimiguel/token-hobo in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Token Hobo safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Token Hobo?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Token Hobo free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Token Hobo?

KiMiGuel (@kimiguel) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Token Hobo by kimiguel — Agent Skill

Name: Token Hobo
Author: KiMiGuel

Token Hobo

Prompt optimization for reducing token consumption in AI coding agents. Use when writing, reviewing, or refactoring prompts for Kimi Code, Codex, or Claude Code to cut token cost — e.g. "optimize this prompt", "make this prompt cheaper", "reduce token usage", "tighten this system prompt". Applies three rule sets — Structural Optimization (system roles, stop sequences), Output Control (character limits, no preamble, few-shot efficiency), and Input Management (trim history, semantic compression) — and outputs platform-formatted prompts.

KiMiGuel 8102692 2 files · 4.9 KB Updated Sep 17, 2026

File contents

Token_Hobo (token-hobo)

Optimizing prompts reduces token consumption. Apply the 3 rule sets below to every prompt you write, review, or refactor. Then format the result for the target platform (Kimi Code, Codex, Claude Code).

Rule 1 — Structural Optimization

System Roles: Define constraints once in the system prompt. Never repeat the same constraint across user messages or per-turn instructions.
- Bad: "Reply briefly" in every message. Good: one line in the system prompt.
Stop Sequences: Set explicit end markers so the model stops instead of generating unnecessary text.
- Use a clear terminator (e.g. ### END, </answer>) and reference it: "End your reply with ### END."

Rule 2 — Output Control

Character Limits: Explicitly instruct the AI to reply under a specific word count (e.g. "Answer in under 100 words.").
No Preamble: Tell the model to skip introductory phrases and give the answer directly. Ban "Sure,", "Certainly,", "Here's the answer:", restating the question.
Few-Shot Efficiency: Keep training examples short, concise, and highly relevant. Cut examples that don't change the output. One tight example beats three long ones.

Rule 3 — Input Management

Trim History: Remove old or irrelevant messages from the chat context window. Keep only what the current task needs.
Semantic Compression: Filter filler words from input data before sending it (greetings, hedging, repeated context, verbose logs). Keep facts, commands, errors — drop the prose around them.

Optimization Workflow

Identify the target platform (Kimi Code, Codex, Claude Code). Ask if unspecified.
Scan the prompt against all 7 rules above. Mark every violation.
Rewrite the prompt applying fixes. Keep intent identical — cut cost, not meaning.
Report savings briefly: what was cut and why (one line per change, no fluff).

Platform Formatting

Kimi Code: System-role constraints go in a .md skill/prompt file loaded once. Deliver copy-paste ready prompt text. Include work-style clause if the user's standing instructions require it.
Codex: Constraints go in AGENTS.md at repo root. Keep it short — it's loaded on every run.
Claude Code: Constraints go in CLAUDE.md or slash-command files. Use explicit stop markers and word caps in the command body.

See references/platform_notes.md for per-platform formatting details and before/after examples.

---name: token-hobodescription: Prompt optimization for reducing token consumption in AI coding agents. Use when writing, reviewing, or refactoring prompts for Kimi Code, Codex, or Claude Code to cut token cost — e.g. "optimize this prompt", "make this prompt cheaper", "reduce token usage", "tighten this system prompt". Applies three rule sets — Structural Optimization (system roles, stop sequences), Output Control (character limits, no preamble, few-shot efficiency), and Input Management (trim history, semantic compression) — and outputs platform-formatted prompts.---# Token_Hobo (token-hobo)Optimizing prompts reduces token consumption. Apply the 3 rule sets below to every prompt you write, review, or refactor. Then format the result for the target platform (Kimi Code, Codex, Claude Code).## Rule 1 — Structural Optimization- **System Roles**: Define constraints once in the system prompt. Never repeat the same constraint across user messages or per-turn instructions. - Bad: "Reply briefly" in every message. Good: one line in the system prompt.- **Stop Sequences**: Set explicit end markers so the model stops instead of generating unnecessary text. - Use a clear terminator (e.g. `### END`, `</answer>`) and reference it: "End your reply with `### END`."## Rule 2 — Output Control- **Character Limits**: Explicitly instruct the AI to reply under a specific word count (e.g. "Answer in under 100 words.").- **No Preamble**: Tell the model to skip introductory phrases and give the answer directly. Ban "Sure,", "Certainly,", "Here's the answer:", restating the question.- **Few-Shot Efficiency**: Keep training examples short, concise, and highly relevant. Cut examples that don't change the output. One tight example beats three long ones.## Rule 3 — Input Management- **Trim History**: Remove old or irrelevant messages from the chat context window. Keep only what the current task needs.- **Semantic Compression**: Filter filler words from input data before sending it (greetings, hedging, repeated context, verbose logs). Keep facts, commands, errors — drop the prose around them.## Optimization Workflow1. Identify the target platform (Kimi Code, Codex, Claude Code). Ask if unspecified.2. Scan the prompt against all 7 rules above. Mark every violation.3. Rewrite the prompt applying fixes. Keep intent identical — cut cost, not meaning.4. Report savings briefly: what was cut and why (one line per change, no fluff).## Platform Formatting- **Kimi Code**: System-role constraints go in a `.md` skill/prompt file loaded once. Deliver copy-paste ready prompt text. Include work-style clause if the user's standing instructions require it.- **Codex**: Constraints go in `AGENTS.md` at repo root. Keep it short — it's loaded on every run.- **Claude Code**: Constraints go in `CLAUDE.md` or slash-command files. Use explicit stop markers and word caps in the command body.See [references/platform_notes.md](references/platform_notes.md) for per-platform formatting details and before/after examples.

Frequently asked questions