Case 00166

Skill knownasnaffy/prompthound/dataset/case_00166

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_00166

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What its author says it does

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Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.

SKILL.md

1.7 KB, 334 tokens by cl100k_base, as published. Nobody here has run it

Context Budgeting Skill

This skill provides a systematic framework for managing the finite context window (RAM) of an OpenClaw agent.

Core Concepts

1. Information Partitioning

Objective/Goal (10%): Core task instructions and active constraints.
Short-term History (40%): Recent 5-10 turns of raw dialogue.
Decision Logs (20%): Summarized outcomes of past steps ("Tried X, failed because Y").
Background/Knowledge (20%): High-relevance snippets from MEMORY.md.

2. Pre-compression Checkpointing (Mandatory)

Before any compaction (manual or automatic), the agent MUST:

Generate Checkpoint: Update memory/hot/HOT_MEMORY.md with:
- Status: Current task progress.
- Key Decision: Significant choices made.
- Next Step: Immediate action required.
Run Automation: Execute scripts/gc_and_checkpoint.sh to trigger the physical cleanup.

Automation Tool: `gc_and_checkpoint.sh`

Located at: skills/context-budgeting/scripts/gc_and_checkpoint.sh

Usage:

Run this script after updating HOT_MEMORY.md to finalize the compaction process without restarting the session.

Integration with Heartbeat

Heartbeat (every 30m) acts as the Garbage Collector (GC):

Check /status. If Context > 80%, trigger the Checkpointing procedure.
Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.