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Summarizer Agent Skills: Three Missing Capabilities — Map-Reduce, Keyframe Anchors, and Cross-Source Synthesis

Summarization capability is AI Agent infrastructure. We have youtube-content (transcript to summary), blog-source-content (source to blog), llm-wiki (knowledge digestion), and daily-news-brief (news aggregation), but all share the same bottleneck: reliance on single-pass context window, truncation on long content, inability to fuse multi-source information.

Summarization Capability Gap

Summarization Projects on GitHub

Project Stars Core Function
JimmyLv/bibigpt-skill 73 Video/audio summarization via BibiGPT CLI
keepongo/video-summarizer 25 Video subtitle extraction + structured summary + keyframe screenshots
specstoryai/agent-skills 24 Extract reusable Skill files from execution logs
doudouwer/skills-summarizer 6 Agent execution logs → Skill extraction
HarrisHan/ai-daily-digest 4 RSS → scoring → summary pipeline
jielou/youtube-summarizer 3 YouTube structured interactive summary

All projects are open-source and free, with no paid API dependencies.

Six Summarization Patterns

  1. Map-Reduce: Long document → chunk → summarize each → combine summaries. LangChain classic pattern, the only reliable approach for content exceeding context window.

  2. Refine (Iterative Refinement): First chunk generates initial summary, subsequent chunks progressively refine it, preserving contextual coherence. Output quality typically exceeds Map-Reduce but takes longer.

  3. Chunking + Overlap: Retain overlapping windows when chunking to avoid semantic breaks. Technical detail but impacts summary quality.

  4. Structured Output: Enforce output structure (JSON Schema/Markdown template), e.g., keyframe screenshots + key points + timestamps.

  5. Score-then-Summarize: Score and filter first, then summarize high-value content only. ai-daily-digest pattern for cost reduction.

  6. Hierarchical: Section-level → document-level, multi-level progressive summarization. Suitable for books, papers with clear structure.

Capability Gap Analysis

Dimension Our Existing Competitor Advantage
Video summary youtube-content keepongo adds keyframe screenshots, timestamp anchors
Blog summary blog-source-content Roughly equal
Knowledge digestion llm-wiki PaperPal supports academic paper structured parsing
Daily digest daily-news-brief ai-daily-digest has RSS scoring + multi-channel push
Map-Reduce long docs Missing No chunked summarization; relies on single-pass context
Execution log summary Missing skills-summarizer extracts reusable Skills from logs
Cross-source synthesis Missing No cross-source (video+blog+paper) comprehensive summary
Structured output templates Partial Competitors commonly support JSON Schema enforced output

Three Key Missing Capabilities:

  1. Map-Reduce chunked summarization — The only reliable approach for long content (books, long papers, complete codebases)
  2. Keyframe + timestamp anchors — Video summaries can jump to corresponding positions in the original video, significantly improving UX
  3. Cross-source synthesis — Multi-source information fusion (video + blog + paper + news), producing insights impossible from any single source

Priority and Recommendations

Priority ranking: Map-Reduce > Structured Schema > Keyframe Anchors

Map-Reduce chunked summarization should be implemented first. Simple to implement: chunk → summarize each → combine, no external dependencies. Can be embedded in youtube-content and llm-wiki as a fallback strategy for long content.

Structured output schema second. Unified summary output format (title/key points/citations/tags), enabling downstream skill consumption.

Keyframe anchors require video processing capability (ffmpeg), higher implementation complexity, can be enhanced later.

All three capabilities can be implemented with local LLMs, no paid APIs needed.

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