token-saviour
Route each sub-task to the cheapest adequate tool across a 4-layer routing table (serena / rtk / caveman / Ponytail), cutting overall token spend by ~70%.
Skill by Lesile-Yin
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/lesile-yin-token-saviour
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: productivity, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/token-saviour
What's inside
- Skill name
token-saviour- Description
- Route each sub-task to the cheapest adequate tool across a 4-layer routing table (serena / rtk / caveman / Ponytail), cutting overall token spend by ~70%. Use when an agent loop's context is ballooning, when choosing between heavy and light tooling for a sub-task, or when budgeting a long-running job.
- License
- MIT
- Instructions
- 55 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- routing-table.md · references/
SKILL.md (first lines)
# Token Saviour Token discipline is a routing problem, not a willpower problem. token-saviour picks the lightest tool that can still do the job, then compresses what must be sent. It is the operational layer beneath token-warden's budget accounting. ## When to use - Before spinning up an expensive coding agent for a small, well-scoped edit. - When a conversation history exceeds a token threshold and must be trimmed. - When deciding which model/agent should own a sub-task. - When a task can be done by a formatter, a linter, or a grep instead of a chat. ## Workflow ### Step 1 — Classify the sub-task Tag it on two axes: **token-cost sensitivity** (how much context it drags) and **adequacy floor** (minimum capability needed). A rename is high-sensitivity, low-floor; an architecture decision is high-sensitivity, high-floor. ### Step 2 — Consult the 4-layer routing table See `references/routing-table.md` for the full matrix. In short: - **Layer 1 — serena**: structural/semantic code ops (symbol rename, find-usages) that a language server does cheaper than an LLM. - **Layer 2 — rtk** (regex/toolkit): deterministic text transforms, search, bulk edits a script handles without a model. - **Layer 3 — caveman**: compress any context *before* it reaches a model. - **Layer 4 — Ponytail** (heavy agent): only when lower layers cannot meet the adequacy floor. ### Step 3 — Compress inputs, cap history Run Layer 3 on everything that crosses into Layer 4. Cap rolled-up history to the last N meaningful turns; archive the rest to a retrievable note, not the live context. ### Step 4 — Measure and report After the sub-task, report the delta vs. the naive "send everything to the heavy agent" baseline. A good call shows savings; a bad call shows where the floor was missed. ## Anti-patterns (never do these) - Routing everything to Layer 4 "just to be safe" — that is the bug it fixes. - Compressing load-bearing context to hit a number — adequacy first, thrift second. - Skipping the measurement — unseen savings aren't savings. - Treating the table as fixed — re-tune thresholds per project. ## Output contract Return a routing decision: chosen layer(s), the estimated token delta vs. baseline, and one line of rationale. If no layer meets the adequacy floor, say so explicitly and escalate rather than guessing.
From Lesile-Yin/agent-skills, MIT.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Aug 26, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Lesile-Yin/agent-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
token-saviour skill. Agent Mart. https://agentmart.dev/skills/lesile-yin-token-saviour (last updated 2026-10-24)