| name | banthis |
| description | Use when user asks to ban, stop, or remember not to repeat an agent behavior ('stop doing X', 'you keep doing X', 'ban this'). Immediately capture it as a permanent negative rule using the banthis tool. |
| version | 0.3.1 |
| argument-hint | [behavior description] |
| allowed-tools | Bash(banthis:*), Bash(npx:*) |
Banthis Skill
When the user shows clear frustration with a recurring agent behavior, you must capture it using banthis so the rule persists across all future sessions.
Parse Arguments
Use $ARGUMENTS as the behavior to ban. If it is empty, infer the behavior from the most recent explicit correction in the conversation.
$ARGUMENTS
When to Activate
- User says variations of: "stop doing X", "you always do X", "don't ever X again", "ban this", "remember not to..."
- User corrects the same class of mistake multiple times in one session
- You notice a pattern of low-value behavior (excessive hedging, over-explaining, touching files you were told not to, etc.)
How to Use
- Do not ask for permission. The user has already signaled they want this behavior banned.
- Craft two strings:
- title: Short (under 60 chars), framed as a prohibition (e.g.
No 'let me be honest' preambles)
- rule: 1–2 sentences in the form
Do not X — reason.
- Call the tool (prefer the local
banthis if available, otherwise npx --yes github:agent-sh/banthis):
banthis add "<title>" "<rule>"
Use --global only for behaviors that should apply to every project (verbal tics, generic LLM habits).
- Confirm in one short line:
Banned: <title>
Important Notes
- Rules added by
banthis go into a managed section of CLAUDE.md or AGENTS.md and have higher priority than normal instructions.
- This is the negative counterpart to positive memory tools (e.g.
axiom).
- Run
banthis init in projects if the meta-rule is not yet present (it teaches agents to invoke banthis automatically).
Related Tools
axiom (positive knowledge capture)agnix (validates the resulting CLAUDE.md / AGENTS.md)skill-curator and system-prompt-curator (for improving how agents are instructed)
