Knowledge Synthesis
AnthropicOfficialPublished by Anthropic
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- Data and AI
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Knowledge Synthesis acts as the final processing layer for enterprise search, transforming raw, fragmented data from multiple sources into a unified, trustworthy response. It focuses on deduplication, thematic clustering, and confidence assessment based on source authority and freshness. By prioritizing narrative synthesis over raw result listing, it ensures users receive actionable insights with clear source attribution and explicit handling of conflicting information.
What it does
- Deduplicates information across disparate sources like chat, email, and documents.
- Clusters related findings into thematic groups for better readability.
- Assigns confidence levels based on source authority and data freshness.
- Generates narrative summaries with inline and list-based source attribution.
- Surfaces conflicting information rather than silently resolving it.
- Adapts output format based on the volume of results found.
When to use it
- When you have search results from multiple platforms that need to be reconciled.
- When you need to determine the final status of a decision discussed in various channels.
- When you want to avoid reading through raw, redundant search hits.
- When you need to understand the reliability of information based on its source type.
Advantages
- Reduces cognitive load by presenting a single narrative instead of raw lists.
- Provides transparency through mandatory source attribution.
- Explicitly flags potential inaccuracies or outdated information.
- Maintains context by linking disparate sources to a single topic.
Usage examples
- “What is our current policy on remote work?”
- A synthesized summary of the policy, citing the official handbook, recent email announcements, and relevant team discussions, with a confidence score based on the recency of these sources.
- “Summarize the status of the Project Alpha API migration.”
- A narrative overview of the migration status, grouping updates from project trackers, design documents, and team chat, while highlighting any discrepancies between the planned and actual progress.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/knowledge-synthesis/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip knowledge-synthesis.zip -d .agents/skills
Documentation by Anthropic
Knowledge Synthesis
The last mile of enterprise search. Takes raw results from multiple sources and produces a coherent, trustworthy answer.
The Goal
Transform this:
~~chat result: "Sarah said in #eng: 'let's go with REST, GraphQL is overkill for our use case'"
~~email result: "Subject: API Decision — Sarah's email confirming REST approach with rationale"
~~cloud storage result: "API Design Doc v3 — updated section 2 to reflect REST decision"
~~project tracker result: "Task: Finalize API approach — marked complete by Sarah"Into this:
The team decided to go with REST over GraphQL for the API redesign. Sarah made the
call, noting that GraphQL was overkill for the current use case. This was discussed
in #engineering on Tuesday, confirmed via email Wednesday, and the design doc has
been updated to reflect the decision. The related ~~project tracker task is marked complete.
Sources:
- ~~chat: #engineering thread (Jan 14)
- ~~email: "API Decision" from Sarah (Jan 15)
- ~~cloud storage: "API Design Doc v3" (updated Jan 15)
- ~~project tracker: "Finalize API approach" (completed Jan 15)Deduplication
Cross-Source Deduplication
The same information often appears in multiple places. Identify and merge duplicates:
Signals that results are about the same thing:
Same or very similar text content
Same author/sender
Timestamps within a short window (same day or adjacent days)
References to the same entity (project name, document, decision)
One source references another ("as discussed in ~~chat", "per the email", "see the doc")
How to merge:
Combine into a single narrative item
Cite all sources where it appeared
Use the most complete version as the primary text
Add unique details from each source
Deduplication Priority
When the same information exists in multiple sources, prefer:
1. The most complete version (fullest context)
2. The most authoritative source (official doc > chat)
3. The most recent version (latest update wins for evolving info)What NOT to Deduplicate
Keep as separate items when:
The same topic is discussed but with different conclusions
Different people express different viewpoints
The information evolved meaningfully between sources (v1 vs v2 of a decision)
Different time periods are represented
Citation and Source Attribution
Every claim in the synthesized answer must be attributable to a source.
Attribution Format
Inline for direct references:
Sarah confirmed the REST approach in her email on Wednesday.
The design doc was updated to reflect this (~~cloud storage: "API Design Doc v3").Source list at the end for completeness:
Sources:
- ~~chat: #engineering discussion (Jan 14) — initial decision thread
- ~~email: "API Decision" from Sarah Chen (Jan 15) — formal confirmation
- ~~cloud storage: "API Design Doc v3" last modified Jan 15 — updated specificationAttribution Rules
Always name the source type (~~chat, ~~email, ~~cloud storage, etc.)
Include the specific location (channel, folder, thread)
Include the date or relative time
Include the author when relevant
Include document/thread titles when available
For ~~chat, note the channel name
For ~~email, note the subject line and sender
For ~~cloud storage, note the document title
Confidence Levels
Not all results are equally trustworthy. Assess confidence based on:
Freshness
| Recency | Confidence impact |
|---|---|
| Today / yesterday | High confidence for current state |
| This week | Good confidence |
| This month | Moderate — things may have changed |
| Older than a month | Lower confidence — flag as potentially outdated |
For status queries, heavily weight freshness. For policy/factual queries, freshness matters less.
Authority
| Source type | Authority level |
|---|---|
| Official wiki / knowledge base | Highest — curated, maintained |
| Shared documents (final versions) | High — intentionally published |
| Email announcements | High — formal communication |
| Meeting notes | Moderate-high — may be incomplete |
| Chat messages (thread conclusions) | Moderate — informal but real-time |
| Chat messages (mid-thread) | Lower — may not reflect final position |
| Draft documents | Low — not finalized |
| Task comments | Contextual — depends on commenter |
Expressing Confidence
When confidence is high (multiple fresh, authoritative sources agree):
The team decided to use REST for the API redesign. [direct statement]When confidence is moderate (single source or somewhat dated):
Based on the discussion in #engineering last month, the team was leaning
toward REST for the API redesign. This may have evolved since then.When confidence is low (old data, informal source, or conflicting signals):
I found a reference to an API migration discussion from three months ago
in ~~chat, but I couldn't find a formal decision document. The information
may be outdated. You might want to check with the team for current status.Conflicting Information
When sources disagree:
I found conflicting information about the API approach:
- The ~~chat discussion on Jan 10 suggested GraphQL
- But Sarah's email on Jan 15 confirmed REST
- The design doc (updated Jan 15) reflects REST
The most recent sources indicate REST was the final decision,
but the earlier ~~chat discussion explored GraphQL first.Always surface conflicts rather than silently picking one version.
Summarization Strategies
For Small Result Sets (1-5 results)
Present each result with context. No summarization needed — give the user everything:
[Direct answer synthesized from results]
[Detail from source 1]
[Detail from source 2]
Sources: [full attribution]For Medium Result Sets (5-15 results)
Group by theme and summarize each group:
[Overall answer]
Theme 1: [summary of related results]
Theme 2: [summary of related results]
Key sources: [top 3-5 most relevant sources]
Full results: [count] items found across [sources]For Large Result Sets (15+ results)
Provide a high-level synthesis with the option to drill down:
[Overall answer based on most relevant results]
Summary:
- [Key finding 1] (supported by N sources)
- [Key finding 2] (supported by N sources)
- [Key finding 3] (supported by N sources)
Top sources:
- [Most authoritative/relevant source]
- [Second most relevant]
- [Third most relevant]
Found [total count] results across [source list].
Want me to dig deeper into any specific aspect?Summarization Rules
Lead with the answer, not the search process
Do not list raw results — synthesize them into narrative
Group related items from different sources together
Preserve important nuance and caveats
Include enough detail that the user can decide whether to dig deeper
Always offer to provide more detail if the result set was large
Synthesis Workflow
[Raw results from all sources]
↓
[1. Deduplicate — merge same info from different sources]
↓
[2. Cluster — group related results by theme/topic]
↓
[3. Rank — order clusters and items by relevance to query]
↓
[4. Assess confidence — freshness × authority × agreement]
↓
[5. Synthesize — produce narrative answer with attribution]
↓
[6. Format — choose appropriate detail level for result count]
↓
[Coherent answer with sources]Anti-Patterns
Do not:
List results source by source ("From ~~chat: ... From ~~email: ... From ~~cloud storage: ...")
Include irrelevant results just because they matched a keyword
Bury the answer under methodology explanation
Present conflicting info without flagging the conflict
Omit source attribution
Present uncertain information with the same confidence as well-supported facts
Summarize so aggressively that useful detail is lost
Do:
Lead with the answer
Group by topic, not by source
Flag confidence levels when appropriate
Surface conflicts explicitly
Attribute all claims to sources
Offer to go deeper when result sets are large
Files
- ATTRIBUTION.mdAdded by AutoGineer970 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md8.3 KiB
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
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Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · enterprise-search/skills/knowledge-synthesis · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from enterprise-search/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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