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.
Instructions your experts follow when using this skill.
Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and file references; see ATTRIBUTION.md in this skill package.
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:
Files installed alongside these instructions.
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.