kind: WavePipeline
metadata:
  name: gt-issue-research
  description: Research a Gitea issue and post findings as a comment
  release: true

input:
  source: cli
  example: "public/librenotes 42"
  schema:
    type: string
    description: "Gitea repository and issue number (e.g. 'owner/repo number')"

steps:
  - id: fetch-issue
    persona: gitea-analyst
    workspace:
      type: worktree
      branch: "{{ pipeline_id }}"
    exec:
      type: prompt
      source: |
        Fetch the Gitea issue specified in the input: {{ input }}

        The input format is "owner/repo issue_number" (e.g., "public/librenotes 42").

        Parse the input to extract the repository and issue number.
        Use the tea CLI to fetch the issue:

        tea issues list --repo <owner/repo> --login librete -o json -f index,title,body,labels,state,url --limit 50

        Then filter the JSON output for the issue matching the requested number.

        Parse the output and produce structured JSON with the issue content.
        Include repository information in the output.
    output_artifacts:
      - name: issue-content
        path: .wave/output/issue-content.json
        type: json
    handover:
      contract:
        type: json_schema
        source: .wave/output/issue-content.json
        schema_path: .wave/contracts/issue-content.schema.json
        on_failure: retry
        max_retries: 3

  - id: analyze-topics
    persona: researcher
    dependencies: [fetch-issue]
    memory:
      inject_artifacts:
        - step: fetch-issue
          artifact: issue-content
          as: issue
    workspace:
      type: worktree
      branch: "{{ pipeline_id }}"
    exec:
      type: prompt
      source: |
        Analyze the Gitea issue and extract research topics.

        Identify:
        1. Key technical questions that need external research
        2. Domain concepts that require clarification
        3. External dependencies, libraries, or tools to investigate
        4. Similar problems/solutions that might provide guidance

        For each topic, provide:
        - A unique ID (TOPIC-001, TOPIC-002, etc.)
        - A clear title
        - Specific questions to answer (1-5 questions per topic)
        - Search keywords for web research
        - Priority (critical/high/medium/low based on relevance to solving the issue)
        - Category (technical/documentation/best_practices/security/performance/compatibility/other)

        Focus on topics that will provide actionable insights for the issue author.
        Limit to 10 most important topics.
    output_artifacts:
      - name: topics
        path: .wave/output/research-topics.json
        type: json
    handover:
      contract:
        type: json_schema
        source: .wave/output/research-topics.json
        schema_path: .wave/contracts/research-topics.schema.json
        on_failure: retry
        max_retries: 2

  - id: research-topics
    persona: researcher
    dependencies: [analyze-topics]
    memory:
      inject_artifacts:
        - step: fetch-issue
          artifact: issue-content
          as: issue
        - step: analyze-topics
          artifact: topics
          as: research_plan
    workspace:
      type: worktree
      branch: "{{ pipeline_id }}"
    exec:
      type: prompt
      source: |
        Research the topics identified in the research plan.

        For each topic in the research plan:
        1. Execute web searches using the provided keywords
        2. Evaluate source credibility (official docs > authoritative > community)
        3. Extract relevant findings with key points
        4. Include direct quotes where helpful
        5. Rate your confidence in the answer (high/medium/low/inconclusive)

        For each finding:
        - Assign a unique ID (FINDING-001, FINDING-002, etc.)
        - Provide a summary (20-2000 characters)
        - List key points as bullet items
        - Include source URL, title, and type
        - Rate relevance to the topic (0-1)

        Always include source URLs for attribution.
        If a topic yields no useful results, mark confidence as "inconclusive".
        Document any gaps in the research.
    output_artifacts:
      - name: findings
        path: .wave/output/research-findings.json
        type: json
    handover:
      contract:
        type: json_schema
        source: .wave/output/research-findings.json
        schema_path: .wave/contracts/research-findings.schema.json
        on_failure: retry
        max_retries: 2

  - id: synthesize-report
    persona: summarizer
    dependencies: [research-topics]
    memory:
      inject_artifacts:
        - step: fetch-issue
          artifact: issue-content
          as: original_issue
        - step: research-topics
          artifact: findings
          as: research
    workspace:
      type: worktree
      branch: "{{ pipeline_id }}"
    exec:
      type: prompt
      source: |
        Synthesize the research findings into a coherent report for the Gitea issue.

        Create a well-structured research report that includes:

        1. Executive Summary:
           - Brief overview (50-1000 chars)
           - Key findings (1-7 bullet points)
           - Primary recommendation
           - Confidence assessment (high/medium/low)

        2. Detailed Findings:
           - Organize by topic/section
           - Include code examples where relevant
           - Reference sources using SRC-### IDs

        3. Recommendations:
           - Actionable items with IDs (REC-001, REC-002, etc.)
           - Priority and effort estimates
           - Maximum 10 recommendations

        4. Sources:
           - List all sources with IDs (SRC-001, SRC-002, etc.)
           - Include URL, title, type, and reliability

        5. Pre-rendered Markdown:
           - Generate complete markdown_content field ready for Gitea comment
           - Use proper headers, bullet points, and formatting
           - Include a header: "## Research Findings (Wave Pipeline)"
           - End with sources section
    output_artifacts:
      - name: report
        path: .wave/output/research-report.json
        type: json
    handover:
      contract:
        type: json_schema
        source: .wave/output/research-report.json
        schema_path: .wave/contracts/research-report.schema.json
        on_failure: retry
        max_retries: 2

  - id: post-comment
    persona: gitea-commenter
    dependencies: [synthesize-report]
    memory:
      inject_artifacts:
        - step: fetch-issue
          artifact: issue-content
          as: issue
        - step: synthesize-report
          artifact: report
          as: report
    workspace:
      type: worktree
      branch: "{{ pipeline_id }}"
    exec:
      type: prompt
      source: |
        Post the research report as a comment on the Gitea issue.

        Steps:
        1. Read the issue details to get the repository and issue number
        2. Read the report to get the markdown_content
        3. Write the markdown content to a file, then use tea CLI to post the comment:

           # Write to file to avoid shell escaping issues with large markdown
           cat > /tmp/comment-body.md << 'COMMENT_EOF'
           <markdown_content>
           COMMENT_EOF

           tea comment <number> "$(cat /tmp/comment-body.md)" --repo <owner/repo> --login librete

        4. Add a footer to the comment:
           ---
           *Generated by [Wave](https://github.com/re-cinq/wave) issue-research pipeline*

        5. Capture the result and verify success
        6. Clean up temp files

        Record the result with:
        - success: true/false
        - issue_reference: issue number and repository
        - comment: id, url, body_length (if successful)
        - error: code, message, retryable (if failed)
        - timestamp: current time
    output_artifacts:
      - name: comment-result
        path: .wave/output/comment-result.json
        type: json
    outcomes:
      - type: url
        extract_from: .wave/output/comment-result.json
        json_path: .comment.url
        label: "Research Comment"
    handover:
      contract:
        type: json_schema
        source: .wave/output/comment-result.json
        schema_path: .wave/contracts/comment-result.schema.json
        on_failure: retry
        max_retries: 3