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 skills/howdeploy/deploychan_mcp/zaebalnpx skills add howdeploy/deploychan_mcp --skill zaebalgit clone --depth 1 https://github.com/howdeploy/deploychan_mcpWrote 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/skills/howdeploy/deploychan_mcp/zaebal)<a href="https://agentmods.dev/skills/howdeploy/deploychan_mcp/zaebal"><img src="https://agentmods.dev/badge/skills/howdeploy/deploychan_mcp/zaebal.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 | $0.00071 | $0.01595 |
| Opus 5 | $0.00036 | $0.00797 |
| Sonnet 5 | $0.00014 | $0.00319 |
| Haiku 4.5 | $0.00007 | $0.00160 |
Grade A, and why
Z.A.E.B.A.L.: profanity-triggered self-audit for agents scanned grade A with 1 finding 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 4d 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.
Reads agent configuration directorieslowAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
| Claude Code | `UserPromptSubmit` in `~/.claude/settings.json` | `claude -p`, read-only tools | Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Z.A.E.B.A.L. — profanity-triggered self-audit
Zaebal? Audit. Errors. Break. Analyze. Leave no assumption.
When a coding agent gets stuck, it repeats the same action with small variations — because
one underlying belief about the task or the codebase is wrong, and the agent treats that
belief as fact. Another self-check just reproduces the mistake. Z.A.E.B.A.L. plugs a
feedback loop into the user-message boundary: profanity and direct complaints become an
audit signal, repeated signals escalate, and at the top level an external auditor
(the same or a cross-vendor CLI) reads the transcript and repository evidence from outside.
Public: howdeploy/Z.A.E.B.A.L, MIT, Python standard library only.
Why it works
The named failure patterns (collected from real session postmortems):
- The wrong belief is invisible to the agent. It doesn't loop from inattention — it has sincerely stopped understanding the problem. The protocol's goal is not "find the mismatch" but find and disprove the wrong belief.
- "Written ≠ took effect". A classic: the agent wrote a config/hook/instruction file and assumes it works because the file exists — while the harness reads it from a different path. Verify the act of consumption, not the act of writing.
- Sycophancy vs hallucinated correctness. Agreeing under pressure and abandoning a working solution — or defending broken code with invented facts. The cure for both: execution over intuition. Defending code with verbal arguments is forbidden — only a micro-test, a run, logs. This kills ~90% of "lying" cases.
- First plausible hypothesis. A lone agent fixates on the first version. That's why auditors get raw artifacts, not the agent's interpretation — and why there are two of them: independent versions disprove each other's dead ends.
The escalation ladder
Detection covers Russian, English and Chinese profanity (including leetspeak), classifies intent (praise with profanity like "fucking great, it works" closes an incident instead of opening one), and tracks a per-session streak in a 30-minute window:
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.
- 4d ago First seen · 116 lines · 71 tokens per session scan A ecd855de3ae7
Z.A.E.B.A.L.: profanity-triggered self-audit for agents is a skill published in the GitHub repository howdeploy/deploychan_mcp (11 stars, last pushed 5d ago), licensed MIT. It adds 71 tokens to every session and 1,595 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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