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Add Generative Answers Agent Skill

Add generative answer nodes (SearchAndSummarizeContent or AnswerQuestionWithAI) to a Copilot Studio topic. Use this instead of /add-node when the user asks to add grounded answers, knowledge search, generative answers, or AI-powered responses — these nodes require specific patterns (ConditionGroup follow-up, knowledge source references, autoSend, responseCaptureType) that /add-node does not cover.

5k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
386
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/skills-for-copilot-studio --skill add-generative-answers

What comes with it

12 975 bytes besides the instruction
patterns.md
property-reference.md

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting

The instruction itself

5 sections, as written by the author

Add Generative Answers

Add SearchAndSummarizeContent nodes to generate responses grounded in the agent's knowledge sources.

Important: Do You Even Need a Topic?

When knowledge sources are added to the agent (via /add-knowledge), the AI can directly search them without any topic if it recognizes a QnA-style query. A dedicated topic with SearchAndSummarizeContent is useful when:

  • You want to restrict the search to a subset of knowledge sources (not all of them)
  • You want to control the flow around the answer (e.g., follow-up questions, formatting, adaptive cards)
  • You want to process the response before showing it (e.g., extract content, combine with other data)
  • You want to use a specific input other than the user's last message

If the user just wants the agent to answer questions from its knowledge, adding the knowledge source may be enough.

Instructions

  • Auto-discover the agent directory:
   Glob: **/agent.mcs.yml

NEVER hardcode an agent name.

  • Determine the approach based on what the user needs:
  • Add to existing topic: Read the target topic and insert a SearchAndSummarizeContent node
  • Create new search topic: Generate a complete topic with the search pattern
  • Look up the schema for both nodes:
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve CreateSearchQuery
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve SearchAndSummarizeContent
  • Read settings.mcs.yml to check if GenerativeActionsEnabled: true. This determines the best pattern:
  • GenerativeActionsEnabled: true → prefer Pattern 2 (Orchestrator): use topic inputs/outputs and let the orchestrator handle the response. This is the best approach for generative-orchestrated agents.
  • GenerativeActionsEnabled: false (or not set) → use Pattern 1 (Direct Response): autoSend: false + manual SendActivity, or Pattern 3 (Fallback Search) for a simple all-knowledge fallback.
  • Verbatim/exact content needed → use Pattern 4 (Precision Search): SearchKnowledgeSources + CreateSearchQuery for raw results without AI summarization (insurance policies, HR docs, legal text).
  • Ask the user to clarify the behavior (if not already clear from their request):
  • Should it search all knowledge sources or only specific ones?
  • Should general model knowledge also be used, or only the configured knowledge sources?
  • Should the response be sent automatically to chat, or processed first (e.g., custom formatting, adaptive card, combined with other data)?
  • Always precede SearchAndSummarizeContent with CreateSearchQuery to preserve conversational context. Never pass =System.Activity.Text directly to SearchAndSummarizeContent — the raw last message may lack context (e.g., "tell me more about that"). CreateSearchQuery rewrites the input into an optimized search query. Access the result via Topic.<ResultVar>.SearchQuery.
  • Generate unique IDs for all nodes (format: <nodeType>_<6-8 random alphanumeric>).
  • Build the YAML using the appropriate pattern. For full YAML examples, see patterns.md. For the complete property reference table, see property-reference.md.

SearchAndSummarizeContent vs AnswerQuestionWithAI

| Node | Use When | Data Source | Output |

|------|----------|-------------|--------|

| SearchAndSummarizeContent | You want answers grounded in the agent's knowledge sources (websites, SharePoint, Dataverse) | Agent's configured knowledge | AI-summarized response |

| SearchKnowledgeSources | You need verbatim/exact content — insurance policies, legal text, HR docs — where AI summarization could lose details | Agent's configured knowledge | Raw search results (no AI summary) |

| AnswerQuestionWithAI | You want a response based only on conversation history and general model knowledge | No external data | AI-generated response |

Use SearchAndSummarizeContent for the vast majority of cases (what people call "generative answers"). Use SearchKnowledgeSources when you need raw, unsummarized results for precision scenarios (pair with CreateSearchQuery for better search accuracy). Use AnswerQuestionWithAI only when you explicitly want the model to respond without consulting knowledge sources.

Knowledge Source References

When using knowledgeSources to restrict the search to specific sources:

  • The knowledge source must already exist in the agent (add it first with /add-knowledge)
  • Find the knowledge source filename in the agent's knowledge/ directory
  • Reference it without the .mcs.yml extension

Example: if the file is cre3c_agent.topic.MyDocs_abc123.mcs.yml, the reference is:

knowledgeSources:
  kind: SearchSpecificKnowledgeSources
  knowledgeSources:
    - cre3c_agent.topic.MyDocs_abc123

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How to use it

Copy the folder

Take microsoft/add-generative-answers from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.