productivity-skill intermediate active

Context7

Long sessions eat your context window and the agent forgets early instructions. Critical facts vanish before you finish the task. The openclaw context management skill keeps memory intact.

openclaw context memory context-window management long-context

What breaks without openclaw context management skill

Lost early instructions. Forgotten facts mid-session. Context window exhausted too fast.

→
Persistent key facts across long sessions × 175-star context skill ÷ 10–15 minutes ÷ no manual summarisation = research tasks that stay accurate start to finish.

openclaw context management skill — what it actually does

01
Summarises and prunes old conversation turns to free up context budget.
02
Reloads saved summaries with /ctx reload for session continuity.
03
Reports current context usage with /ctx status before critical tasks.
04
Configurable auto-prune threshold via CONTEXT7_THRESHOLD in .env.

Security check — openclaw context management skill

Privacy score: 7/10 — accesses connected platform APIs only. Lock it: review OAuth scopes before install, confirm macOS, Linux; OpenClaw ≥1.0 compatibility.

Quick start — openclaw context management skill in 10–15 minutes

Setup time: 10–15 minutes

!
You need: OpenClaw core

Install the package:

# Install via ClawhHub
clawhub install thesethrose/context7
1
Install the skill
2
Run /ctx status to see current context usage
3
Use /ctx summarise to compress old turns
4
Use /ctx reload <summary_id> to restore key context

Troubleshooting openclaw context management skill

1
1. Summarisation is lossy — critical instructions in early turns may be lost
2
2. Auto-prune threshold is conservative by default — tune CONTEXT7_THRESHOLD in .env

Compatibility & status

Works with: macOS, Linux; OpenClaw ≥1.0 intermediate Last updated: Nov 2025 ★ 175 on GitHub MIT

Official docs →

View on GitHub →

Related — more like openclaw context management skill

Self Improving Agent
Let OpenClaw agents evaluate their own outputs and iteratively improve responses through self-critique loops.
Self Improving
Enable iterative self-reflection and output improvement loops within OpenClaw agent sessions.
Proactive Agent
Enable OpenClaw to run proactively in the background — monitoring conditions and initiating actions without user prompts.
Openclaw Supermemory
From stateless bot responses to persistent agent memory — Supermemory gives your OpenClaw bot long-term recall.
OpenClaw Local Memory
Persistent agent memory without a cloud API — local SQLite-backed memory for OpenClaw.

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Let the agent forget half your instructions and redo work from scratch. Install context management before the next long research session.

Get it on GitHub →