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OpenClaw

shed

Context window hygiene for long-running LLM agents. Decision rules for when and how to compress, mask, switch, or delegate context — backed by research (JetBrains/NeurIPS 2025, OpenHands, Letta/MemGPT, LLMLingua). Use when an agent runs for extended sessions, accumulates large tool outputs, approaches context limits, or suffers from compaction/overflow. Also use when designing agent architectures that need to manage context over time.

compass-soul0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install compass-soul/shed

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Shed — Context Hygiene for Agents

Shed what you don't need. Keep what matters.

Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight.

Core Principle

Tool outputs are 84% of your context growth but the lowest-value tokens you carry. (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this.

The Rules

After Every Tool Call
  1. Extract, don't accumulate. When a tool returns large output (file contents, search results, logs, API responses), immediately write the key facts to a file or compress into bullets. The raw output is now disposable.
  2. Ask: "Will I need this verbatim later?" Almost never. The answer you extracted is what matters, not the 500 lines that contained it.
When Context Reaches ~70%
  1. Trigger condensation. Don't wait for the platform to compact you — that's losing control of your own memory. At 70%, actively shed.
  2. Mask old tool outputs first (free, no LLM calls). Keep your reasoning and action history intact — you need your decision chain, not the raw ls -la from 20 turns ago.
  3. Summarize reasoning only as backup. If masking isn't enough, compress old reasoning turns. But this is lossy and costs an LLM call — use sparingly.
  4. Never re-summarize a summary. If you've already condensed once and context is growing again, switch context or spawn a sub-agent. Recursive summarization compounds errors.
When Completing a Task
  1. Write results to file, then switch context immediately. Stale completed-task context is anti-signal for your next task. Don't carry it.
  2. Leave breadcrumbs. Before switching: write what you did, what's next, and where the files are to memory/YYYY-MM-DD.md. Future-you needs a trailhead, not a transcript.
When Delegating Work
  1. Spawn fresh-context sub-agents for complex sub-tasks. Your context is noise for their work....

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/compass-soul/shed
Version
—
Commit
79491dc3de24c352bd55e1c6b5e913edacf2603a
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Shed — Context Hygiene for Agents

*Shed what you don't need. Keep what matters.*

Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight.

## Core Principle

**Tool outputs are 84% of your context growth but the lowest-value tokens you carry.** (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this.

## The Rules

### After Every Tool Call

1. **Extract, don't accumulate.** When a tool returns large output (file contents, search results, logs, API responses), immediately write the key facts to a file or compress into bullets. The raw output is now disposable.
2. **Ask: "Will I need this verbatim later?"** Almost never. The answer you extracted is what matters, not the 500 lines that contained it.

### When Context Reaches ~70%

3. **Trigger condensation.** Don't wait for the platform to compact you — that's losing control of your own memory. At 70%, actively shed.
4. **Mask old tool outputs first** (free, no LLM calls). Keep your reasoning and action history intact — you need your decision chain, not the raw `ls -la` from 20 turns ago.
5. **Summarize reasoning only as backup.** If masking isn't enough, compress old reasoning turns. But this is lossy and costs an LLM call — use sparingly.
6. **Never re-summarize a summary.** If you've already condensed once and context is growing again, switch context or spawn a sub-agent. Recursive summarization compounds errors.

### When Completing a Task

7. **Write results to file, then switch context immediately.** Stale completed-task context is anti-signal for your next task. Don't carry it.
8. **Leave breadcrumbs.** Before switching: write what you did, what's next, and where the files are to `memory/YYYY-MM-DD.md`. Future-you needs a trailhead, not a transcript.

### When Delegating Work

9. **Spawn fresh-context sub-agents for complex sub-tasks.** Your context is noise for their work....

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