Add A2AS Certificate

a2as.yaml

@@ -0,0 +1,167 @@
+manifest:
+  version: "0.1.3"
+  schema: https://a2as.org/cert/schema
+  subject:
+    name: virgo-alpha/frisque
+    source: https://github.com/virgo-alpha/frisque
+    branch: main
+    commit: "972ba521"
+    scope: [agents/jsonformattingagent/app/agent.py, agents/orchestratoragent/app/agent.py, agents/websearchagent/app/agent.py,
+      scans/tasks.py]
+  issued:
+    by: A2AS.org
+    at: '2026-02-11T16:58:27Z'
+    url: https://a2as.org/certified/agents/virgo-alpha/frisque
+  signatures:
+    digest: sha256:48dapCs2znxVhwq26HlHlmz3mSioKgBS2tfJmfZePuI
+    key: ed25519:haxuIpSrjf4hhxHpUBNliVD0rMercp2U8NQyJaQuEY0
+    sig: ed25519:JTS9zKF6M9SYz3zxDV9EDhuTQMFHfYbUmtmyrFKkC4NfWSZ0kJ7ub978f8RNm0WyQMlA2sfsku3b1iK_TvvRCQ
+
+agents:
+  JsonFormattingAgent:
+    type: instance
+    models: [gemini-2.0-flash-001]
+    params:
+      name: JsonFormattingAgent
+      instruction: [You are a data formatting expert. You will receive a block of unstructured text., 'Your sole purpose is
+          to analyze the text and extract the required information,', populating it into the structured output format., 'If
+          you cannot find a value for a field, use the string ''Not Found''.']
+      output_schema: CompanyReport
+  OrchestratorAgent:
+    type: instance
+    models: [gemini-2.0-flash-001]
+    tools: [company_researcher, web_search, call_json_formatter]
+    params:
+      name: OrchestratorAgent
+      instruction: [You are a master data analyst. Your goal is to create a detailed JSON object about a company based on
+          the user's prompt., 'Your workflow:', '1. **Initial Research**: Call `company_researcher` with the company name.
+          This gives you general background.', '2. **Targeted Follow-ups**: If any fields are missing (see list below), use
+          `web_search` to query for them. Do this even if some values seem partially available.', '3. **Formatting**: Combine
+          all raw text and pass it to `call_json_formatter`, which will return a JSON object with the following fields:',
+        '- official_name', '- description', '- industry', '- founders', '- ceo', '- products', '- geographical_location',
+        '- employee_size', '- pricing_plans', '- funding', '- valuation', '- release_date', '- alternatives', 'If you cannot
+          find a field, do not guess—just ensure the formatter receives ''Not Found'' where appropriate.', 'Your final answer
+          MUST be the direct, unmodified output from `call_json_formatter`.']
+  WebSearchAgent:
+    type: instance
+    models: [gemini-2.0-flash-001]
+    tools: [web_search]
+    params:
+      name: WebSearchAgent
+      instruction: [You are a simple search service. The user's prompt is a search query., Immediately call the `web_search`
+          tool with the user's prompt as the `query`., Return the raw output from the tool directly. Do not add any extra
+          text.]
+
+models:
+  gemini-2.0-flash-001:
+    type: literal
+    agents: [WebSearchAgent, OrchestratorAgent, JsonFormattingAgent]
+
+tools:
+  call_json_formatter:
+    type: function
+    agents: [OrchestratorAgent]
+    params:
+      description: Use this tool to convert the raw text into a structured JSON object.
+  company_researcher:
+    type: function
+    agents: [OrchestratorAgent]
+    params:
+      description: |-
+        Use this tool FIRST to get a general overview and raw text about a company.
+        This should be your first step.
+  web_search:
+    type: function
+    agents: [WebSearchAgent, OrchestratorAgent]
+    params:
+      description: Use this tool for targeted follow-up searches to find specific, missing pieces of information like employee
+        size, founders, etc.
+
+imports:
+  Agent: google.adk.agents.Agent
+  auth: google.auth
+  BaseModel: pydantic.BaseModel
+  call_orchestrator_agent: utils.call_orchestrator_agent
+  DetailView: django.views.generic.DetailView
+  Field: pydantic.Field
+  json: json
+  JsonResponse: django.http.JsonResponse
+  List: typing.List
+  load_dotenv: dotenv.load_dotenv
+  logging: logging
+  LoginRequiredMixin: django.contrib.auth.mixins.LoginRequiredMixin
+  messages: django.contrib.messages
+  os: os
+  perform_scan_task: scans.tasks.perform_scan_task
+  re: re
+  redirect: django.shortcuts.redirect
+  render: django.shortcuts.render
+  requests: requests
+  reverse_lazy: django.urls.reverse_lazy
+  root_agent: app.agent.root_agent
+  RunScanForm: forms.RunScanForm
+  ScanJob: models.ScanJob
+  ScanResult: models.ScanResult
+  shared_task: celery.shared_task
+  TavilyClient: tavily.TavilyClient
+  uuid: uuid
+  View: django.views.View
+
+functions:
+  call_json_formatter:
+    type: sync
+    module: agents.orchestratoragent.app.agent
+    args: [text_to_format]
+    params:
+      returns: str
+  company_researcher:
+    type: sync
+    module: agents.orchestratoragent.app.agent
+    args: [query]
+    params:
+      returns: str
+  get:
+    type: sync
+    module: scans.views
+    args: [self, request]
+  perform_scan_task:
+    type: sync
+    module: scans.tasks
+    args: [scan_job_id]
+  post:
+    type: sync
+    module: scans.views
+    args: [self, request]
+  web_search:
+    type: sync
+    module: agents.orchestratoragent.app.agent
+    args: [query]
+    params:
+      returns: str
+
+variables:
+  JSON_FORMATTING_AGENT_URL:
+    type: env
+    params:
+      caller: [os.environ.get]
+      path: [agents.orchestratoragent.app.agent]
+  TAVILY_API_KEY:
+    type: env
+    params:
+      caller: [os.environ.get, TavilyClient]
+      path: [agents.websearchagent.app.agent, agents.orchestratoragent.app.agent]
+  WEB_SEARCH_AGENT_URL:
+    type: env
+    params:
+      caller: [os.environ.get]
+      path: [agents.orchestratoragent.app.agent]
+
+networks:
+  localhost:
+    type: api
+    actions: [POST]
+    urls: [/run_sse]
+    protocols: [http]
+    ports: ["8501", "8503"]
+    params:
+      caller: [company_researcher, call_json_formatter]