Turn search results from chat, email, documents, and other sources into a coherent answer. Merge duplicate information, retain source references, highlight conflicting accounts, and reflect source freshness and authority when expressing confidence in the findings.
中文简介正在准备中。当前显示英语简介。
Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.
安装前可以查看交给 AI 代理的指令内容。
The last mile of enterprise search. Takes raw results from multiple sources and produces a coherent, trustworthy answer.
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)
The same information often appears in multiple places. Identify and merge duplicates:
Signals that results are about the same thing:
How to merge:
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)
Keep as separate items when:
Every claim in the synthesized answer must be attributable to a source.
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 specification
Not all results are equally trustworthy. Assess confidence based on:
| 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.
| 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 |
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.
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.
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]
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]
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?
[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]
Do not:
Do:
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Review a design or web page against WCAG 2.1 AA, an accessibility standard. Organize findings on contrast, keyboard access, forms, and screen reader behavior, with severity levels and practical recommendations for fixes.
中文简介正在准备中。当前显示英语简介。
Build a single brief on an existing customer using available sales records, emails, documents, call notes, and internal chat. Understand the current situation, key contacts, recent discussions, and gaps that may need attention.
中文简介正在准备中。当前显示英语简介。
Build or refresh a strategic customer account plan from sales records, emails, and meeting transcripts. Organize goals, stakeholders, growth opportunities, risks, and dated actions into a document, then sync approved highlights to the customer relationship management system.
中文简介正在准备中。当前显示英语简介。
Research prospective companies and contacts, check existing customer records, and assess fit against your ideal customer profile. Receive a sourced brief covering recent developments, likely priorities, outreach talking points, and a suggested next step.
中文简介正在准备中。当前显示英语简介。
Research sales accounts using Common Room data to summarize company details, buying signals, ownership, and key contacts. Answer specific questions directly or prepare a broader briefing with evidence-based next steps, clearly identifying missing information.
中文简介正在准备中。当前显示英语简介。
Score and group accounts by customer fit and engagement, such as recent contact and open sales opportunities. Identify where to focus sales effort, choose next actions, and spot promising accounts with neglected outreach or missing information.
中文简介正在准备中。当前显示英语简介。