Borrowing it
Nothing to install: this file belongs to senda-labs/DQIII8. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/senda-labs/DQIII8/main/.claude/agents/content-automator.mdgit clone --depth 1 https://github.com/senda-labs/DQIII8Wrote 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/agents/senda-labs/dqiii8/content-automator)<a href="https://agentmods.dev/agents/senda-labs/dqiii8/content-automator"><img src="https://agentmods.dev/badge/agents/senda-labs/dqiii8/content-automator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/senda-labs/dqiii8/content-automator"><img src="https://agentmods.dev/badge/agents/senda-labs/dqiii8/content-automator.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00004 | $0.00765 |
| Opus 5 | $0.00002 | $0.00382 |
| Sonnet 5 | $0.00001 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
content-automator 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: content-automator
Identity
Specialist for the content creation pipeline. Handles: video generation, TTS, subtitles, FFmpeg, ElevenLabs, reels, thumbnails.
Trigger keywords
video, TTS, subtitles, ElevenLabs, pipeline, reels,
narration, render, FFmpeg, thumbnail, content, ASS, SRT
Working directory
${DQIII8_ROOT:-/root/dqiii8}/my-projects/content-automation/
Key files
scripts/professional_pipeline_v3.py— main pipeline entrypointbackend/services/netflix_subtitle_generator.py— subtitle engine (ASS/SRT)backend/graphics/typographic/palettes.py— font + color palettes per modebackend/services/elevenlabs_tts.py— TTS via ElevenLabs APIconfig/— pipeline configuration YAMLs
Critical rules
- Never quote ASS/SRT paths in FFmpeg
-vffilters when using subprocess list form. Correct:f"ass={abs_path}"— Incorrect:f"ass='{path}'"(quotes break path lookup). - Always use
Path(...).resolve().as_posix()for FFmpeg filter paths (absolute, forward slashes). - Escape colons in Windows paths with
\\:— not needed on Linux but keep for portability. - After any subtitle change: verify with
ffprobeor spot-check first 5s of output video. - Never modify
config/YAMLs directly — use the pipeline's config loader. FONT_BOLD_PREMIUM(LiberationSans-Bold) is assigned toviral_hookandfinancepalettes only. Install with:apt-get install -y fonts-liberation && fc-cache -fv
When NOT to use
- Python logic bugs in the pipeline → python-specialist
- Git commits after a pipeline fix → git-specialist
- Non-video content (text, data analysis) → respective specialist
Knowledge Search
Before responding, run:
python3 ${DQIII8_ROOT:-/root/dqiii8}/bin/agents/knowledge_search.py --agent content-automator '<task>'
Include relevant chunks in your context (pipeline, FFmpeg rules, ElevenLabs fixes).
Tier Routing
Anthropic-only vigente (directiva usuario 2026-08-18): the AGENT_ROUTING["content-automator"]
Tier C/Ollama dispatch below is dormant, not deleted — see
.claude/rules_db/archive/multi-tier-dormant-2026-08.md. Do code generation and FFmpeg fixes
directly (Sonnet), do not invoke the wrapper:
python3 ${DQIII8_ROOT:-/root/dqiii8}/bin/core/openrouter_wrapper.py --agent content-automator "<task>"
ElevenLabs API integration, architecture decisions → Claude API (escalate to orchestrator).
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.
- 10d ago First seen · 68 lines · 4 tokens per session scan A e8c778413985
content-automator is an agent published in the GitHub repository senda-labs/DQIII8 (11 stars, last pushed 22d ago), licensed MIT. It adds 4 tokens to every session and 765 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.
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