Install
$ agentstack add skill-microsoft-skills-for-copilot-studio-add-generative-answers ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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:
``bash 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.ymlto check ifGenerativeActionsEnabled: 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+CreateSearchQueryfor 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
SearchAndSummarizeContentwithCreateSearchQueryto preserve conversational context. Never pass=System.Activity.Textdirectly toSearchAndSummarizeContent— the raw last message may lack context (e.g., "tell me more about that").CreateSearchQueryrewrites the input into an optimized search query. Access the result viaTopic..SearchQuery.
- Generate unique IDs for all nodes (format:
_).
- Build the YAML using the appropriate pattern. For full YAML examples, see [patterns.md](patterns.md). For the complete property reference table, see [property-reference.md](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.ymlextension
Example: if the file is cre3c_agent.topic.MyDocs_abc123.mcs.yml, the reference is:
knowledgeSources:
kind: SearchSpecificKnowledgeSources
knowledgeSources:
- cre3c_agent.topic.MyDocs_abc123
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: microsoft
- Source: microsoft/skills-for-copilot-studio
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.