Przejdź do treści
Biblioteka skilli
AI i agenci · Budowanie i kod

new-topic

/new-topic

Tworzy nowy plik YAML tematu Copilot Studio dla przepływu rozmowy lub dialogu agenta.

Licencja
MIT
Ostatnia zmiana repozytorium
27.10.2026

Ocena i bezpieczeństwo

84/ 100Klasa: dobry
Format9 / 25
Frontmatter z nazwą i opisem, poprawna nazwa, opis do 1024 znaków, nazwa zgodna z folderem.
Odkrywalność25 / 25
Opis mówi, kiedy użyć skilla, ma sensowną długość i jest w 3. osobie.
Treść35 / 35
Co najmniej 150 słów i do 500 linii, nagłówki, listy kroków, przykłady albo kod.
Higiena15 / 15
Bez zaślepek typu TODO, bez ścieżek Windows, z licencją.
Ryzykobrak sygnałów w skanie
Pliki w paczce
1
Skrypty
0
Komentarze HTML
0
Adresy URL w SKILL.md
0
Przestarzałość

Brak sygnałów. Repozytorium zmienione 27.10.2026, bez odwołań do wycofanych modeli i API.

Licencja

MIT, otwarta: można używać i zmieniać, z zachowaniem informacji o autorze.

Co robi i kiedy

Tworzy nowy plik YAML tematu Copilot Studio dla przepływu rozmowy lub dialogu agenta.

Argumenty

/new-topic <topic description>
Oryginalny opis (EN)

Create a new Copilot Studio topic YAML file. Use when the user asks to create a new topic, conversation flow, or dialog for their agent.

Jak działa

  1. Instructions
  2. Generative Orchestration Guidelines
  3. Topic-Action Chaining Under Generative Orchestration
  4. Power Fx Quick Reference
Sekcje z nagłówków SKILL.md, w oryginale · 158 linii, 1 031 słów
Podgląd SKILL.md
---
user-invocable: false
description: Create a new Copilot Studio topic YAML file. Use when the user asks to create a new topic, conversation flow, or dialog for their agent.
argument-hint: <topic description>
allowed-tools: Bash(node *schema-lookup.bundle.js *), Bash(node *manage-agent.bundle.js *), Read, Write, Glob
context: fork
agent: copilot-studio-author
---

# Create New Topic

Generate a new Copilot Studio topic YAML file based on user requirements.

## Instructions

1. **Auto-discover the agent directory**:
   ```
   Glob: **/agent.mcs.yml
   ```
   If multiple agents found, ask which one. NEVER hardcode an agent name.

2. **Check for matching templates** in `${CLAUDE_SKILL_DIR}/../../templates/topics/` first:
   - `greeting.topic.mcs.yml` — OnConversationStart greeting
   - `fallback.topic.mcs.yml` — OnUnknownIntent fallback with escalation
   - `arithmeticsum.topic.mcs.yml` — Topic with inputs/outputs and computation
   - `question-topic.topic.mcs.yml` — Question with branching logic
   - `search-topic.topic.mcs.yml` — Generative answers from knowledge
   - `auth-topic.topic.mcs.yml` — Authentication flow
   - `error-handler.topic.mcs.yml` — Error handling
   - `disambiguation.topic.mcs.yml` — Multiple topics matched
   If a template matches, use it as the starting point.

3. **MANDATORY: Verify ALL `kind:` values against the schema** before writing them:
   ```bash
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js kinds                    # List all valid kind values
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve AdaptiveDialog   # Resolve trigger structure
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve <TriggerType>    # Resolve specific trigger
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js search <ActionKind>      # Verify an action kind exists
   ```
   **NEVER write a `kind:` value you haven't verified exists in the schema.** This is the #1 source of hallucination errors. If `schema-lookup.bundle.js search <kind>` returns no results, the kind does NOT exist — do not use it.

4. **Determine the trigger type** from the user's description:
   - `OnRecognizedIntent` — For topics triggered by user phrases (most common)
   - `OnConversationStart` — For welcome/greeting topics
   - `OnUnknownIntent` — For fallback topics
   - `OnEscalate` — For escalation to human agent
   - `OnError` — For error handling

5. **Generate the topic YAML** with:
   - A `# Name:` comment at the top
   - `kind: AdaptiveDialog`
   - Appropriate `beginDialog` with correct trigger
   - **Unique IDs** for ALL nodes (format: `<nodeType>_<6-8 random alphanumeric>`)
   - If using a template, replace ALL `_REPLACE` placeholders with unique IDs

6. **Check settings.mcs.yml** for `GenerativeActionsEnabled`. Read the agent's `settings.mcs.yml` to check.

7. **Save** to the agent's `topics/<topic-name>.topic.mcs.yml` directory

8. **MANDATORY: Validate the generated file** after saving:

   **Step A: Schema validation** — always run this first:
   ```bash
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js validate <saved-file.yml>
   ```

   **Step B: LSP-based validation** — also run this if the agent has `.mcs/conn.json`:
   Read `.mcs/conn.json` to get connection details, then:
   ```bash
   node ${CLAUDE_SKILL_DIR}/../../scripts/manage-agent.bundle.js validate \
     --workspace "<path-to-agent-folder>" \
     --tenant-id "<tenantId>" \
     --environment-id "<envId>" \
     --environment-url "<envUrl>" \
     --agent-mgmt-url "<mgmtUrl>"
   ```

   Both validations are complementary: schema validation checks structural correctness (action kinds, property placement), while LSP validation checks Power Fx expressions, cross-file references, and environment-specific rules. If either fails, fix the issues before reporting success to the user.

## Generative Orchestration Guidelines

When the agent has `GenerativeActionsEnabled: true` in settings:

**Use Topic Inputs** (AutomaticTaskInput) instead of Question nodes to auto-collect user info.
Place `inputs` at the **AdaptiveDialog root level** (NOT inside `beginDialog`):
```yaml
kind: AdaptiveDialog
inputs:                    # <-- at AdaptiveDialog root, NOT inside beginDialog
  - kind: AutomaticTaskInput
    propertyName: userName
    description: "The user's name"
    entity: StringPrebuiltEntity
    shouldPromptUser: true
beginDialog:
  kind: OnRecognizedIntent
  id: main
  actions:
    - ...
```
- The orchestrator auto-collects inputs based on the description — no explicit Question node needed.
- Still use Question nodes when: conditional asks (ask X only if Y), or end-of-flow confirmations.

**Use Topic Outputs** instead of SendActivity for final results.
Use `outputType` at the root level and `SetVariable` to set output values — do NOT use `TaskOutput` (which is only valid in `TaskDialog` connector actions):
```yaml
kind: AdaptiveDialog
inputs:
  - ...
beginDialog:
  kind: OnRecognizedIntent
  id: main
  actions:
    - kind: SetVariable
      id: setVar_abc123
      variable: Topic.result
      value: ="computed value"
outputType:                # <-- at AdaptiveDialog root
  properties:
    result:
      displayName: result
      description: The computed result
      type: String
```
- The orchestrator generates the user-facing message from outputs.
- Do NOT use SendActivity to show final outputs (rare exceptions: precise mid-flow messages).

**Include inputType/outputType schemas** at the AdaptiveDialog root level when using inputs/outputs:
```yaml
inputType:
  properties:
    userName:
      displayName: userName
      description: "The user's name"
      type: String
outputType:
  properties:
    result:
      displayName: result
      type: String
```

## Topic-Action Chaining Under Generative Orchestration

When a topic exists alongside other topics or other actions (i.e. TaskDialog), think carefully about **the topic outputs** — it directly affects whether the orchestrator will chain to an action.

**Two scenarios:**

1. **Topic is self-contained** (e.g., reservation confirmation, calculation result): The topic does the work itself. It can gather the inputs too or just ask for those, but work is executed into the topic. In such case, output a confirmation/status message. The orchestrator treats the task as done or not, depending on the confirmation — this is correct.

2. **Topic gathers data for an action to consume** (e.g., collecting a complaint that a Teams action should send, without manually calling the action into the topic): The topic output must be the **data itself**, not a confirmation. If you output "Your complaint has been sent!", the orchestrator assumes the job is done and will NOT invoke the action which means the complaint will never be sent. Instead, output the complaint text so the orchestrator can see there's an action to send it and feed it to such action.

**Ask yourself:** Does this topic complete the task on its own, or does it prepare data for an action/other topic? If the latter, output the data, not a status message.

**Alternative approaches for data-gathering topics:**
- Remove the topic entirely and tailor the action's input descriptions for the specific use case (simplest). Basically the action will gather the data directly via its inputs, no topic needed. Useful for simple data collection (i.e. one-shot).
- Use a global variable: the topic sets it (one-shot or by asking multiple questions and consolidating into the same variable via PowerFX), and the action's body references it via Power FX. This is more complex but can be useful for multi-turn data gathering that feeds somewhere else, especially if you need to do some logic on the data before sending it.
- Output the collected data in a topic output variable so the orchestrator can pass it to the next action/topic.

## Power Fx Quick Reference

- Expressions start with `=`: `value: =Text(Topic.num1 + Topic.num2)`
- String interpolation uses `{}`: `activity: "Hello {Topic.UserName}"`
- Common functions: `Text()`, `Now()`, `IsBlank()`, `!IsBlank()`, `DateTimeFormat.UTC`
- Variable init: `variable: init:Topic.MyVar` (first assignment uses `init:`)
- **Type coercion to text**: Use `SetTextVariable` instead of `SetVariable` to convert non-text types (Number, DateTime, etc.) to text via template interpolation: `value: "Guests: {Topic.NumberOfGuests}"`

Wersje

W bazie nie ma innych skilli o nazwie new-topic.

Migawka bazy z 28.10.2026 · wersja paczki 4aaa34c6633c (skrót SHA-256 całej paczki)

Pobrania w ostatnich 30 dniach: 0 · ranking

Zgłoś problem z tym skillem: szkodliwa treść, błędny opis albo prośba autora o usunięcie.

Podobne skille

  • 90tune-copilot-studio-bot
    Stroi bota Copilot Studio: wyzwalacze tematów, trafność źródeł wiedzy, przebieg rozmowy i jakość odpowiedzi generatywnych.
  • 84validate
    Waliduje pliki YAML agenta Copilot Studio pełną diagnostyką binarki LSP: struktura YAML, Power Fx, schemat i odwołania między plikami.
  • 100create-agent
    Tworzy agenta Copilot Studio MCS2 i lokalną przestrzeń roboczą przez pac copilot init, a potem dodaje instrukcje, umiejętności, narzędzia, wiedzę i ustawienia przed synchronizacją.
  • 100int-patterns
    Udostępnia bibliotekę wzorców projektowania agentów Copilot Studio z przykładami YAML, do sugerowania wzorców, przeglądu i wskazywania znanych pułapek.
  • 100clone-agent
    Klonuje istniejącego agenta Copilot Studio do nowego lokalnego obszaru roboczego PAC CLI poleceniem pac copilot clone.
  • 100okhp3-copilot-studio-skill-foundry
    Projektuje, pisze, ocenia i pakuje wielokrotnego użytku skill agenta Microsoft Copilot Studio, gdy zadaniową funkcję trzeba współdzielić między agentami opartymi na GitHub Copilot.

Uwagi i zgłoszenia

Wiadomość trafi do Bartosza Sokolińskiego. Adres e-mail jest potrzebny tylko do odpowiedzi. Szczegóły w polityce prywatności.