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 skills add hamzaPixl/pixl-ai --skill content-pipelinegit clone --depth 1 https://github.com/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/content-pipeline)<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/content-pipeline"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/content-pipeline/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/skills/hamzapixl/pixl-ai/content-pipeline"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/content-pipeline.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 | $0.00069 | $0.00469 |
| Opus 5.5 | $0.00028 | $0.00188 |
| Sonnet 5.5 | $0.00014 | $0.00094 |
| Haiku 4.5 | $0.00007 | $0.00047 |
Grade A, and why
content-pipeline 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 1mo 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
Overview
Generates content through a structured pipeline: requirements discovery, research, parallel content generation, optional translation, quality validation, and final assembly.
Required References
Before starting, read references/writing/writing-plans.md for content structure, tone guidelines, and editorial workflow patterns.
Step 1: Discovery
- Understand content requirements (type, audience, tone, length)
- Identify brand voice and style guidelines
- Determine output format (markdown, HTML, JSON)
- Check for multilingual requirements
Step 2: Research
- Analyze existing content for style and patterns
- Gather domain knowledge relevant to the topic
- Identify key messages and value propositions
- Create content outline
Step 3: Generate (Parallel)
Generate content sections in parallel:
- Each section gets its own generation pass
- Follow the brand voice and style guidelines
- Apply humanizer patterns from
references/writing/humanizer.mdto avoid AI-sounding text - Include SEO keywords where applicable
Step 4: Translate (Conditional)
Only if multilingual content is requested.
- Translate content to target locales
- Adapt idioms and cultural references (not word-for-word)
- Preserve formatting and structure
- Flag untranslatable terms
Step 5: Validate
- Check content against requirements
- Verify tone and voice consistency
- Check for factual accuracy
- Validate links and references
- Run spelling and grammar checks
Step 6: Assemble
- Combine all content sections into final output
- Apply consistent formatting
- Add metadata (author, date, tags)
- Generate table of contents if applicable
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.
- 1mo ago First seen · 63 lines · 69 tokens per session scan A 049ce0b1b0fe
content-pipeline is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 69 tokens to every session and 469 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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