Feat/new

agentspecs/__version__.py

@@ -4,4 +4,4 @@
 
 """Agentspecs."""
 
-__version__ = "0.0.2"
+__version__ = "0.0.4"

agentspecs/agents/mocks/analyze-campaign-performance.yaml → agentspecs/agents/analyze-campaign-performance.yaml

@@ -7,6 +7,7 @@
 # and optimises budget allocation across advertising channels.
 
 id: analyze-campaign-performance
+version: 0.0.1
 name: Analyze Campaign Performance
 description: >
   A multi-agent team that unifies marketing data from Google Ads, Meta,
@@ -21,11 +22,9 @@ tags:
   - analytics
   - advertising
   - social-media
-  - team
 
-enabled: true
-
-model: "openai-gpt-4-1"
+enabled: false
+model: "bedrock:us.anthropic.claude-sonnet-4-5-20250929-v1:0"
 
 sandbox_variant: jupyter
 
@@ -34,93 +33,26 @@ memory: ephemeral
 
 # MCP servers used by this agent team
 mcp_servers:
-  - filesystem
-  - slack
+  - filesystem:0.0.1
+  - slack:0.0.1
 
 # Skills available to this agent team
 skills:
-  - pdf
-  - crawl
+  - pdf:0.0.1
+  - crawl:0.0.1
+  - events:0.0.1
 
 # Runtime environment
+frontend_tools:
+  - jupyter-notebook:0.0.1
+  - lexical-document:0.0.1
 environment_name: ai-agents-env
 
 # UI customization
 icon: megaphone
 emoji: 📢
 color: "#8250df"  # Purple — media & creativity
 
-# Team configuration
-team:
-  orchestration_protocol: datalayer
-  execution_mode: sequential
-  supervisor:
-    name: Campaign Analytics Orchestrator Agent
-    model: "openai-gpt-4-1"
-  routing_instructions: >
-    Start with Platform Connector to pull data from all ad platforms,
-    then Metrics Normaliser for unified KPIs, then Anomaly Detector for
-    real-time performance monitoring, then Budget Optimiser for reallocation
-    recommendations. Escalate CPA spikes above 50% immediately.
-  validation:
-    timeout: "300s"
-    retry_on_failure: true
-    max_retries: 2
-  agents:
-    - id: cp-1
-      name: Platform Connector Agent
-      role: "Primary · Initiator"
-      goal: Pull campaign data from Google Ads, Meta, TikTok, LinkedIn, GA4, and email platforms
-      model: "openai-gpt-4-1"
-      mcp_server: Ad Platforms MCP
-      tools:
-        - Google Ads Connector
-        - Meta Ads Connector
-        - TikTok Ads Connector
-        - LinkedIn Ads Connector
-        - GA4 Connector
-      trigger: "Schedule: Every 4 hours"
-      approval: auto
-    - id: cp-2
-      name: Metrics Normaliser Agent
-      role: Secondary
-      goal: Normalise CPA, ROAS, CTR, and attribution across all platforms into unified view
-      model: "openai-gpt-4-1"
-      mcp_server: Analytics MCP
-      tools:
-        - Metric Unifier
-        - Currency Converter
-        - Attribution Mapper
-        - Naming Convention Resolver
-      trigger: "On completion of Platform Connector Agent"
-      approval: auto
-    - id: cp-3
-      name: Anomaly Detector Agent
-      role: Secondary
-      goal: Monitor all KPIs for CTR drops, CPA spikes, and budget pacing issues
-      model: "anthropic-claude-sonnet-4"
-      mcp_server: Monitoring MCP
-      tools:
-        - Anomaly Scanner
-        - Budget Pacer
-        - Alert Generator
-        - Campaign Pauser
-      trigger: "On completion of Metrics Normaliser Agent"
-      approval: manual
-    - id: cp-4
-      name: Budget Optimiser Agent
-      role: Final
-      goal: Generate budget reallocation recommendations to maximise ROAS across channels
-      model: "openai-gpt-4-1"
-      mcp_server: Optimisation MCP
-      tools:
-        - ROAS Calculator
-        - Budget Allocator
-        - Scenario Modeller
-        - Report Generator
-      trigger: "On completion of Anomaly Detector Agent"
-      approval: manual
-
 # Goal / Prompt (Step 4 in the UI — the user-facing objective)
 goal: >-
   Unify marketing data from Google Ads, Meta, TikTok, LinkedIn, GA4, and email
@@ -129,7 +61,7 @@ goal: >-
   generate budget reallocation recommendations to maximise ROAS.
 
 # Protocols & UI Extensions
-protocol: ag-ui
+protocol: vercel-ai
 ui_extension: a2ui
 
 # Evals
@@ -167,6 +99,7 @@ trigger:
   type: schedule
   cron: "0 */4 * * *"
   description: Every 4 hours for cross-platform campaign data sync and analysis
+  prompt: "Run the scheduled workflow and produce the configured deliverable."
 
 # Output configuration
 output:

agentspecs/agents/mocks/analyze-support-tickets.yaml → agentspecs/agents/analyze-support-tickets.yaml

@@ -7,6 +7,7 @@
 # Source: UI mock flow id=4 (Agents.tsx)
 
 id: analyze-support-tickets
+version: 0.0.1
 name: Analyze Support Tickets
 description: >
   A multi-agent team that triages incoming support tickets, categorizes by
@@ -18,11 +19,9 @@ tags:
   - data
   - support
   - tickets
-  - team
 
-enabled: true
-
-model: "openai-gpt-4-1"
+enabled: false
+model: "bedrock:us.anthropic.claude-sonnet-4-5-20250929-v1:0"
 
 sandbox_variant: jupyter
 
@@ -31,82 +30,34 @@ memory: ephemeral
 
 # MCP servers used by this agent team
 mcp_servers:
-  - filesystem
-  - slack
+  - filesystem:0.0.1
+  - slack:0.0.1
 
 # Skills available to this agent team
 skills:
-  - pdf
-  - crawl
+  - pdf:0.0.1
+  - crawl:0.0.1
+  - events:0.0.1
 
 # Runtime environment
+frontend_tools:
+  - jupyter-notebook:0.0.1
+  - lexical-document:0.0.1
 environment_name: ai-agents-env
 
 # UI customization
 icon: issue-opened
 emoji: 🎫
 color: "#bf8700"  # Amber — paused status
 
-# Team configuration
-team:
-  orchestration_protocol: datalayer
-  execution_mode: sequential
-  supervisor:
-    name: Support Orchestrator Agent
-    model: "openai-gpt-4-1"
-  routing_instructions: >
-    Route new tickets to the Triage Agent first, then to the Categorizer,
-    then to the Pattern Analyzer. Escalate P1/critical tickets immediately
-    to human support leads.
-  validation:
-    timeout: "180s"
-    retry_on_failure: true
-    max_retries: 2
-  agents:
-    - id: st-1
-      name: Triage Agent
-      role: "Primary · Initiator"
-      goal: Ingest new support tickets and assess urgency level (P1-P4)
-      model: "openai-gpt-4-1"
-      mcp_server: Helpdesk MCP
-      tools:
-        - Ticket Reader
-        - Priority Classifier
-      trigger: "Event: new ticket received"
-      approval: auto
-    - id: st-2
-      name: Categorizer Agent
-      role: Secondary
-      goal: Categorize tickets by topic, product area, and sentiment
-      model: "openai-gpt-4-1"
-      mcp_server: NLP Pipeline MCP
-      tools:
-        - Topic Classifier
-        - Sentiment Analyzer
-        - Product Tagger
-      trigger: "On completion of Triage Agent"
-      approval: auto
-    - id: st-3
-      name: Pattern Analyzer Agent
-      role: Final
-      goal: Identify recurring issues and generate resolution recommendations
-      model: "anthropic-claude-sonnet-4"
-      mcp_server: Analytics MCP
-      tools:
-        - Pattern Detector
-        - Knowledge Base Search
-        - Resolution Generator
-      trigger: "On completion of Categorizer Agent"
-      approval: manual
-
 # Goal / Prompt (Step 4 in the UI — the user-facing objective)
 goal: >-
   Triage incoming support tickets by urgency, categorize by topic and
   sentiment, identify recurring patterns, and generate resolution
   recommendations with escalation paths for critical issues.
 
 # Protocols & UI Extensions
-protocol: ag-ui
+protocol: vercel-ai
 ui_extension: a2ui
 
 # Evals
@@ -139,6 +90,7 @@ trigger:
   type: schedule
   cron: "0 */2 * * *"
   description: Every 2 hours
+  prompt: "Run the scheduled workflow and produce the configured deliverable."
 
 # Output configuration
 output: