ClaudeClaw is a lightweight personal assistant built into Claude Code that runs tasks in the background on schedules and responds through messaging services. It is for people who want Claude Code to handle recurring work, voice commands, and integrations while a daemon remains active. Its catalogue commands, skills, instructions, and plugin provide the assistant’s setup and operating workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/moazbuilds/claudeclaw/discordgit clone --depth 1 https://github.com/moazbuilds/claudeclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/moazbuilds/claudeclaw/discord)<a href="https://agentmods.dev/commands/moazbuilds/claudeclaw/discord"><img src="https://agentmods.dev/badge/commands/moazbuilds/claudeclaw/discord.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00008 | $0.00240 |
| Opus 5 | $0.00004 | $0.00120 |
| Sonnet 5 | $0.00002 | $0.00048 |
| Haiku 4.5 | $0.00001 | $0.00024 |
Grade A, and why
discord scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Show the Discord bot integration status. Check the following:
-
Configuration: Read
.claude/claudeclaw/settings.jsonand check ifdiscord.tokenis set (show masked token: first 5 chars + "..."). ShowallowedUserIds. -
Global Session: Read
.claude/claudeclaw/session.jsonand show:- Session UUID (first 8 chars)
- Created at
- Last used at
- Note: This session is shared across heartbeat, cron jobs, Telegram, and Discord messages.
-
If $ARGUMENTS contains "clear": Delete
.claude/claudeclaw/session.jsonto reset the global session. Confirm to the user. The next run from any source (heartbeat, cron, Telegram, or Discord) will create a fresh session. -
Running: Check if the daemon is running by reading
.claude/claudeclaw/daemon.pid. The Discord bot runs in-process with the daemon when a token is configured.
Format the output clearly for the user.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 20 lines · 8 tokens per session scan A 64310c1502db
discord is a command published in the GitHub repository moazbuilds/claudeclaw (1,316 stars, last pushed 1mo ago), licensed MIT. It adds 8 tokens to every session and 240 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.