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Rapid context extractor

Skill thevibethinker/vibe-thinker-skills/rapid-context-extractor

Portable, standalone Zo Computer skills you can install, reuse, and share.

Install
npx -y skills add thevibethinker/vibe-thinker-skills --skill rapid-context-extractor

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What its author says it does

Copied from the file, not written here

Extract and teach key points from a source using seed context, then force active engagement. Use when analyzing articles, documents, transcripts, video/audio transcripts, or mixed media where output must preserve chronological idea flow, include concept explanation, and prompt user reflection.

SKILL.md

3.6 KB, 729 tokens by cl100k_base, as published. Nobody here has run it

Rapid Context Extractor

Normalize source mechanics with the script, then do semantic analysis in chat.

Quick Start

python3 Skills/rapid-context-extractor/scripts/prepare_payload.py \
  --seed-file "./Research/topic-frame.md" \
  --source-url "https://example.com/article" \
  --auto-semantic \
  --output "/home/.z/workspaces/<conversation-id>/extraction_packet.md"

Replace <conversation-id> with your active conversation workspace, or use any other writable output path.

Use one source input per run:

  • --source-url for web pages
  • --source-file for local docs/transcripts/subtitles
  • --source-text for pasted text

Optional seed context:

  • --seed-file or --seed-text

Optional semantic memory anchoring:

  • --semantic-query to retrieve relevant prior concepts from your semantic memory
  • --auto-semantic to generate semantic query from source title + extracted terms (recommended default)
  • --semantic-limit (default 5) to control number of memory anchors
  • --provenance to force frontmatter provenance (otherwise inferred from output path conversation ID)

Workflow

  1. Prepare packet
  • Run scripts/prepare_payload.py to produce a markdown packet containing seed context + chronological source chunks.
  • For media files, require transcript sidecar or transcribe first.
  1. Adopt analyst frame
  • Read seed context first.
  • State the frame in 1-2 lines before distillation.
  • If missing background blocks understanding, perform targeted research before summarizing.
  1. Distill in chronological order
  • Produce bullet points in the order ideas appear in source.
  • Avoid regrouping by theme if it breaks chronology.
  • Keep claims faithful to the source.
  1. Include image meaning
  • If visuals exist, summarize what each visual contributes to the argument.
  • Note if visuals reinforce, contradict, or extend text claims.
  1. Integrate for learning
  • Explain key terms, concepts, and implications in plain language.
  • Connect key claims to Semantic Memory Anchors where relevant (agreements, tensions, extensions).
  • Explicitly classify each integration claim as aligns, extends, or conflicts/tension.
  • Ask clarifying questions that advance interpretation or decisions.
  1. Force active engagement
  • Ask for immediate reaction (1-3 lines acceptable).
  • Ask for one agreement and one challenge.
  • Offer optional ingestion: only ingest if user explicitly says yes.

Standard Output Shape

Use this structure in responses:

  1. Analytical Frame
  2. Chronological Distillation
  3. Visual Layer (if applicable)
  4. Semantic Integration (link to user- or project-specific anchors when available) : include explicit aligns / extends / conflicts labels
  5. Concept Decoder
  6. Clarifying Questions
  7. Your Reaction (collect user response)
  8. Optional Next Step (ingest yes/no)

Content Library Ingestion

Only after explicit approval, and only in workspaces that include the N5 ingestion helper:

python3 N5/scripts/content_ingest.py "<artifact_path>" --move

Confirm with: Ingested to Content Library as <type>.

Resources

  • scripts/prepare_payload.py: deterministic intake/normalization for seed + source.
  • references/output-template.md: copyable response template for consistent execution.

What ships with it: 3 files

19.7 KB alongside SKILL.md, 1 of them executable

agents/

references/

scripts/

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