Install
openclaw skills install talk-normalStop LLM slop. A curated system prompt that cuts verbose, corporate-sounding LLM output by 56-71% (measured) while preserving information. Works bilingually (English + Chinese). Installs into your AGENTS.md as an always-on behavior modifier.
openclaw skills install talk-normalA curated system prompt that stops your LLM from writing like a LinkedIn post. Measured: 71% character reduction on GPT-4o-mini, 56% on GPT-5.4, across 10 prompts in English and Chinese, without losing information.
When invoked, this skill installs the talk-normal rules into your workspace's AGENTS.md file as an always-on behavior modifier. After install, every reply your OpenClaw agent produces follows the rules in prompt.md. The rules live between # --- talk-normal BEGIN --- and # --- talk-normal END --- markers so they do not conflict with your existing rules in AGENTS.md.
This is not a workflow skill you invoke per turn. It is a one-time installer that makes your agent permanently less verbose until you uninstall.
To install or update talk-normal in the current workspace:
bash install.sh
The script is idempotent: running it again replaces the existing talk-normal block in place with the latest rules. Nothing else in your AGENTS.md is touched.
To remove:
bash install.sh --uninstall
The contents of prompt.md get injected into your AGENTS.md. The rules target the specific slop patterns that make LLM output sound corporate and padded, grouped into a few categories:
The exact rule list lives in prompt.md and evolves as new slop patterns get caught in the wild. Every commit to that file is a named slop pattern killed.
clawhub update talk-normal
bash install.sh
The first command pulls the latest skill bundle from ClawHub. The second command re-runs the idempotent installer, replacing the old rule block in AGENTS.md with the new one.
Works on any LLM that honors system prompts or custom instructions: GPT-5.4, GPT-4o-mini, Claude 4.6, Gemini 2.5, Grok 3, Qwen 3, DeepSeek V3, and others. OpenClaw integration is via AGENTS.md injection, which the agent reads on every turn.
Full benchmark data for 10 prompts across GPT-4o-mini and GPT-5.4 is in TEST_RESULTS.md in the upstream repository. Average reduction: 71% on GPT-4o-mini, 56% on GPT-5.4, while preserving the information content of the original response.
CONTRIBUTING.md in the upstream repo for the rule-submission formatMIT