luma-content-research

Original：🇺🇸 English
Translated

Use when a Luma / 拾光 / 拾光智能体 / 拾光工具 agent needs content research, topic discovery, keyword tables, persona-based search, or Excel-friendly research outputs for short-video planning.

6installs
Sourcezl007700/luma-cli
Added on

NPX Install

npx skill4agent add zl007700/luma-cli luma-content-research

Tags

Translated version includes tags in frontmatter
luma-agentcontent-researchshort-video-planningpersona-based-searchexcel-export

SKILL.md Content

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Luma Content Research

Use this skill when the user wants to find short-video topics, analyze references, export search results, or prepare a keyword table for later script writing with Luma / 拾光.
Read 
../luma-shared/SKILL.md
first for common auth, project, and artifact rules.

When To Use

  • The user wants topic ideas, competitor references, or viral angles.
  • The user provides a role/persona and asks what content to make.
  • The user asks for an Excel-style research table.
  • A workflow needs 
    step0_content_research.json
    , 
    step0_content_research.csv
    , or 
    step0_keywords.json
    .
Do not use this skill for script rewriting after the topic is already selected. Use script or workflow skills for that step.

Standard Flow

  1. Run backend content research:
    bash
    luma-cli research run --role "<role_or_persona_description>" --mode precise --date-range 7d --output step0_content_research.json
  2. Export an Excel-friendly table:
    bash
    luma-cli research export --input step0_content_research.json --output step0_content_research.csv
  3. Extract keywords and topic rows:
    bash
    luma-cli research keywords --input step0_content_research.json --output step0_keywords.json --csv step0_keywords.csv

Persona Reuse

Save a reusable persona when the user describes a stable account or role:
bash
luma-cli research persona save ai_founder --role "AI tool founder making short videos for operators"
luma-cli research run --persona ai_founder --output step0_content_research.json

Agent Rules

  • Keep the raw research JSON even when exporting CSV.
  • Use CSV for human inspection; use JSON for agent decisions.
  • Pick at most 3 high-potential references or one topic cluster explicitly before moving to rewrite.
  • Research results are discovery data, not source scripts. For viral remix workflows, only download and ASR references that are likely to contain reusable spoken copy; skip non-口播 or low-signal videos. See 
    ../luma-workflow-viral-remix/SKILL.md
    .
  • Do not fabricate metrics that are not present in the research result.
  • If research returns too few results, retry with 
    --mode expanded
    or a broader role description.