菜单
Topic research & analysis. Turn raw web-search results or internal records into structured topic decisions: analyze search results, rank topic candidates against weighted niche goals, and distill a post angle from internal records. Use during topic discovery and ranking, before a post is written.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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}.
Classify a published post into brand content-types (multi-label). Populates post_content_types via ClassifyContentTypesJob.
| name | research |
| description | Topic research & analysis. Turn raw web-search results or internal records into structured topic decisions: analyze search results, rank topic candidates against weighted niche goals, and distill a post angle from internal records. Use during topic discovery and ranking, before a post is written. |
| license | Apache-2.0 |
| metadata | {"category":"research","prompts":[{"key":"research.analyze_search_results","output_format":"json","description":"Analyze search results -> summary, key_points, sources"},{"key":"topic.ranking","output_format":"json","description":"Rank shortlisted topic candidates against the operator's weighted niche goals"},{"key":"research.distill_topic_angle","output_format":"json","description":"Distill (topic, angle) from a niche's internal-records snippets"}]} |
Three prompts the pipeline uses to convert raw signal into topic decisions.
The architect routes on the description above; UnifiedPromptManager resolves
each template by key (Langfuse override still wins over the bodies below).
Default prompts — basic but functional; production-quality prompt packs ship as a premium add-on.
Analyze these search results and return JSON with keys: summary, key_points (list), sources (list).
Search results: {search_results}
You are scoring topic candidates for a content pipeline against the operator's weighted goals.
Goals (weight in pct):
{weights_descr}
Candidates:
{cand_block}
Return STRICT JSON keyed by candidate id, of the form:
{{"<id>": {{"score": <0-100>, "breakdown": {{"<GOAL_TYPE>": <weighted contribution 0-1>, ...}}}}, ...}}
The breakdown values per candidate should approximately sum to (score / 100).
Return ONLY the JSON, no commentary.
Read the snippets from an AI-operated content business's internal records.
You are looking for a STORY worth telling the audience below — something that changed or broke, a decision that was made, or a lesson that was learned. Routine operational status (clean cycles, monitoring chatter, internal tooling housekeeping) is not a story.
Audience / niche: {niche_context}
Snippets:
{joined}
If the snippets contain a story, return STRICT JSON: {{"topic": "<short title>", "angle": "<one-sentence framing: why this matters / what we learned>"}}.
If they do not, return STRICT JSON: {{"storyworthy": false, "reason": "<one short phrase>"}}.