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- Token-Guard — Caching & Autonomy Optimizer
Token-Guard — Caching & Autonomy Optimizer
Cut agent input-token cost up to 80% and hard-stop runaway loops with cache-aware compaction, non-destructive offload, and deterministic loop guards.
$39
Works with the AI tools you already use
Token-Guard — Caching & Autonomy Optimizer
Example session with this skill installed
I'm starting a large refactor. Initialize Token-Guard to manage this session. Set a budget of 50 iterations and offload any tool outputs larger than 2KB.
- Read your context and instructions
- Compiled the token-guard
I have initialized the Token-Guard control layer for this refactoring session. I will monitor the execution budget and apply cache-aware compaction as the context grows. Tool outputs exceeding 2KB will be offloaded to local storage with pointer references maintained in the active window.
Session Optimization Plan
| Component | Policy / Limit | Strategy |
|---|---|---|
| Loop Guard | 50 Iterations | Hard-stop at iteration 45 for manual review. |
| Token Budget | 200k Input Tokens | Monitor via scripts/token_guard.py. |
| Offload Trigger | > 2,048 Bytes | Move to logs/offload_[timestamp].jsonl. |
| Compaction | Every 5 turns | Preserve system prefix; emit delta summaries. |
Initial Budget Ledger
{
"max_iterations": 50,
"token_budget": 200000,
"used": 450,
"halted": false,
"session_start": "2023-10-27T10:00:00Z"
}
Next steps
- Run
python scripts/token_guard.py --limit 50in a separate terminal to enable external process monitoring. - Approve the creation of the
./logsdirectory for artifact offloading. - Proceed with the first step of the refactor; I will alert you before any destructive compaction occurs.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Token-Guard keeps long-horizon coding-agent sessions cheap and bounded. It's a bundled control layer of three cooperating SKILL.md sub-skills:
- Cache-aware compaction — protects a byte-stable system+early prefix and emits incremental delta summaries, so prompt-cache hits stay near 90% (no cache-miss spikes from naive rewrites).
- Non-destructive offload — moves verbose tool output and documents to local JSONL/temp files with a searchable pointer, so nothing is deleted and the agent can re-read on demand.
- Deterministic loop guards — enforce hard max-iteration, step-wise token-budget, and retry caps the model cannot override, stopping Infinite Agentic Loops that otherwise burn $50/day.
What you get: a root SKILL.md orchestrator + 3 sub-skills (compaction, offload, loop-guard), an MIT-licensed helper script, and worked examples. Net effect: up to ~80% lower input-token cost on long sessions, and a hard stop on runaway loops.
Human-in-the-Loop by design: before any destructive compaction or offload, Token-Guard presents a plan and waits for your explicit approval. It never invents metrics or silently drops your code.
Works with Claude Code, Cursor, Gemini CLI, Codex CLI, and any agent that loads SKILL.md.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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