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/seeleai/thoth/autogit clone --depth 1 https://github.com/SeeleAI/ThothWhat 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 | $0.00026 | $0.01031 |
| Opus 5 | $0.00013 | $0.00515 |
| Sonnet 5 | $0.00005 | $0.00206 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
thoth:auto scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/thoth:auto
Generated Surface
This file is generated from thoth.command_specs.COMMAND_SPECS. Do not hand edit.
Real Runtime Execution
The repo-local Thoth runtime command for this slash command has already been executed before Claude sees this prompt.
THOTH_AUTO_ARGUMENTS_FILE="$(mktemp -t thoth-auto-arguments.XXXXXX)"
trap 'rm -f "$THOTH_AUTO_ARGUMENTS_FILE"' EXIT
cat > "$THOTH_AUTO_ARGUMENTS_FILE" <<'THOTH_AUTO_ARGUMENTS_EOF'
$ARGUMENTS
THOTH_AUTO_ARGUMENTS_EOF
"${CLAUDE_PLUGIN_ROOT}/scripts/thoth-claude-command.sh" auto --host claude --thoth-arguments-file "$THOTH_AUTO_ARGUMENTS_FILE"
Response Contract
- Treat the structured bridge payload above as the only authority for this command invocation.
- If
bridge_successisfalse, report the exact bridge failure and stop. - If
runorloopis missing--work-id, show returned candidates and stop; do not invent, create, compile, or guess a work item. - If
bridge_successistrueand runtime events are present, summarize progress, terminal status, and risk from those events only. - Do not hand-edit
.thothor manually call runtime protocol commands; the Thoth RuntimeDriver advances phases. - If
packet.dispatch_modeisexternal_worker, do not duplicate the work locally; report the run id, worker mode, and the correct follow-up only. - If you only describe what should happen next instead of reporting the executed runtime result, treat that as failure.
- Substantive execution must flow through
packet.executor; by default this matches the host unless the user explicitly supplied--executor. - Runtime lifecycle is
plan -> execute -> validate -> reflect; execute owns the official validator receipt, validate confirms it mechanically. - Execute must actively produce canonical acceptance evidence: missing artifacts, metrics, logs, receipts, benchmark output, service state, or files are execution work until produced or until a concrete root cause, blocker, or budget boundary is captured.
- For long-running work, first-artifact evidence proves startup only; continue or resume canonical execution until acceptance evidence, a concrete blocker, or a real budget boundary exists.
- Do not let healthy work be stopped just because a short observation window has not yet produced canonical evidence; stop or restart only as explicit debugging or cleanup with logs and a next action.
- If runtime budget expires before acceptance closes, preserve continuation evidence and the exact next command instead of presenting the work as passed.
- Live monitor should observe sparsely around every 288s; on clear runtime/env mistakes, append or interrupt guidance instead of only narrating.
- Trailing text/live corrections are temporary guidance only; never rewrite authority or validators.
- If the bridge payload exposes
body.monitor_command, observe that command instead of executing work directly in the Claude session. - Prefer the Claude Monitor tool with
persistent=trueforbody.monitor_commandwhen available; otherwise use Bash to run the same watch command in the foreground. - Treat the monitor/watch JSONL stream as the only live progress authority; summarize progress and risks from those events only.
- If the live observer is interrupted, do not stop the auto controller unless the user explicitly requests
/thoth:auto --stop <controller_id>.
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.
- yesterday First seen · 76 lines · 26 tokens per session scan A 293cad2a10de
thoth:auto is a command published in the GitHub repository SeeleAI/Thoth (51 stars, last pushed 22d ago), licensed MIT. It adds 26 tokens to every session and 1,031 once invoked, about $0.0001 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
build
Bulk-build ArcKit artefacts in parallel via subagent-orchestrated waves with resumable state.
delegate-review
Run OCR in delegation mode — OCR handles file selection and rules, the host agent performs the actual review.
qabot-report
Regenerate the QA report from existing review files.
ci-report
Generate a CI failure report for PR $PRNUMORURL (or current branch if no argument given).
autobot-list
List all autobot sessions with status information.
os-meeting
快速创建和管理AI团队会议.