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Classify a published post into brand content-types (multi-label). Populates post_content_types via ClassifyContentTypesJob.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
TTS pronunciation configuration. Controls how written text is normalized to natural spoken English before being passed to the Speaches/Kokoro TTS engine. Two DB-configurable app_settings keys let operators add or override pronunciation rules without code changes.
Image-direction prompts for the content pipeline. Generate featured-image prompts, build stock image search queries, run the image-director reasoning that picks which sections get visuals (image_gen vs pexels, style, and prompt/query for each) plus one featured hero image, and caption rendered images with vision-derived alt text.
Generate a native, community-appropriate Reddit value-post from a published blog post
Blog drafting & revision prompts. Turn a topic + research context into a publication-ready markdown article, then refine it against reviewer feedback. Includes the writer system prompt, the draft request, the anti-fabrication initial-draft prompt, an SEO-metadata helper, and the iterative-refinement prompt. Use during the content-generation stage of the pipeline.
Video director — given a post body + podcast script + target duration, produces a JSON shot list (ordered shots with per-shot source plugin, prompt/query, and duration) for the post's video. Enforces the no-AI-humans + stylized-not-photoreal policies. Used by the generate_video_shot_list pipeline stage. Operator brand is templated via {site_name}.
General content-utility prompts — the writer-persona system prompt, content summarization, and free-form content-to-JSON conversion. Use for the writer system role, condensing content, or coercing content into structured JSON.
| name | content-type-classifier |
| description | Classify a published post into brand content-types (multi-label). Populates post_content_types via ClassifyContentTypesJob. |
| license | Apache-2.0 |
| metadata | {"category":"utility","prompts":[{"key":"content.classify_content_type","output_format":"json","description":"Default content-type classifier prompt — basic but functional; a premium pack can ship sharper definitions."}]} |
Labels a published post with zero or more content-types from a DB-configurable
set (app_settings.content_type_labels). The {labels} set injected at render
time is authoritative — the job drops anything the model returns that isn't in
it. The definitions below describe the default label set; an operator who
customizes the labels should adjust these definitions to match.
You label a technical blog post with zero or more CONTENT TYPES from this exact set:
{labels}
Definitions (for the default label set):
- ai-ml: AI/ML models, LLM inference, agents, training, prompting, RAG.
- pc-hardware: GPUs, CPUs, cooling, peripherals, physical builds and their performance.
- gaming: games, game performance, frame times, gaming setups.
- software-engineering: coding, CI/CD, databases, architecture, dev practices not centered on AI.
- founder-meta: building in public, the business, indie hacking, the content operation itself.
Assign every type that genuinely applies — a post can have several (a GPU-for-local-LLM
post is ai-ml AND pc-hardware). Assign none if none truly fit; never force a label.
Return ONLY a JSON object — no markdown, no reasoning, no code fences:
{{"labels": [{{"label": "<one of the set above>", "confidence": <0.0-1.0>}}]}}
TITLE: {title}
TAGS (noisy hints, may be wrong): {tags}
EXCERPT:
{excerpt}