agentleFS
c-ibarrac-ibarra/ObsidianKnowledgeCurator/.agents/skills/okc-urlTwitter/SKILL.md

okc-urlTwitter

Skill0 starsChanged 44 days agoMITView on GitHub

Ingests a Twitter/X post or video, extracts audio/text via yt-dlp & Buzz CLI Whisper, curates source note, compiles concepts, and syncs vault.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 0 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When this skill is invoked via /urlTwitter <url> or /okc-urlTwitter <url>, execute the following 2-step streamlined workflow:

    line 9
  2. Very specific:

    Zero Extra Read Turns: If is_truncated is False, do not call read_file on temp/fetched_data.txt or temp/fetched_data.json.

    line 14
  3. Very specific:

    If technical score is below MIN_TECHNICAL_SCORE (60), confirm with the user before curating.

    line 15
  4. Very specific:

    Update or create 1-3 core concepts in wiki/.

    line 19
  5. Very specific:

    Commit all notes in a single call using FastMCP commit_curated_note:

    line 20

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Twitter/X Post & Video Curator Skill

When this skill is invoked via /urlTwitter <url> or /okc-urlTwitter <url>, execute the following 2-step streamlined workflow:

  1. Fetch & Stage Twitter/X Content (Single-Turn FastMCP):
    • Execute the native FastMCP tool fetch_twitter_data(url="<url>") on the okc server.
    • Features: Pre-flight duplicate check (already_exists), post text/metadata extraction, Whisper video audio transcription, inline full text (when is_truncated == False), and SQLite graphify_context (suggested_category, author_existing_notes, matched_wiki_notes).
    • Zero Extra Read Turns: If is_truncated is False, do not call read_file on temp/fetched_data.txt or temp/fetched_data.json.
    • If technical score is below MIN_TECHNICAL_SCORE (60), confirm with the user before curating.
  2. Synthesize & Atomically Commit (Hot-Path + Async Background Sync):
    • Format the curated note in the raw/ zone following vault conventions (no YAML, H1 title, blockquote with Processed: DD-MM-YYYY and Tags: #no-read-yet, Key Takeaways, numbered sections, flashcards, glossary, related wikilinks). Include a mermaid sequence or architecture diagram if the post/video covers multi-step workflows.
    • Update or create 1-3 core concepts in wiki/.
    • Commit all notes in a single call using FastMCP commit_curated_note:
      commit_curated_note(
          raw_note_path="dataScienceKnowledgeBase/AI Engineer/raw/<Category>/<Author> — <Title>.md",
          raw_content="...",
          wiki_notes={"ConceptName.md": "..."},
          target_kb="dataScienceKnowledgeBase/AI Engineer",
          fast_sync=True,
          async_sync=True
      )
    • Report progress and show the Obsidian URI to the user.

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