Add Gitea issue pipelines and prompts using tea CLI
gt-issue-impl, gt-issue-research, gt-issue-rewrite, gt-issue-update pipelines with corresponding prompts. Mirrors the gh-issue-* variants but uses tea CLI with --login librete for Gitea authentication. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.wave/pipelines/gt-issue-research.yaml
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257
.wave/pipelines/gt-issue-research.yaml
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kind: WavePipeline
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metadata:
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name: gt-issue-research
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description: Research a Gitea issue and post findings as a comment
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release: true
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input:
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source: cli
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example: "public/librenotes 42"
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schema:
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type: string
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description: "Gitea repository and issue number (e.g. 'owner/repo number')"
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steps:
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- id: fetch-issue
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persona: gitea-analyst
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workspace:
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type: worktree
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branch: "{{ pipeline_id }}"
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exec:
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type: prompt
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source: |
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Fetch the Gitea issue specified in the input: {{ input }}
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The input format is "owner/repo issue_number" (e.g., "public/librenotes 42").
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Parse the input to extract the repository and issue number.
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Use the tea CLI to fetch the issue:
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tea issues list --repo <owner/repo> --login librete -o json -f index,title,body,labels,state,url --limit 50
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Then filter the JSON output for the issue matching the requested number.
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Parse the output and produce structured JSON with the issue content.
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Include repository information in the output.
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output_artifacts:
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- name: issue-content
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path: .wave/output/issue-content.json
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type: json
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handover:
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contract:
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type: json_schema
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source: .wave/output/issue-content.json
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schema_path: .wave/contracts/issue-content.schema.json
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on_failure: retry
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max_retries: 3
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- id: analyze-topics
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persona: researcher
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dependencies: [fetch-issue]
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memory:
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inject_artifacts:
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- step: fetch-issue
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artifact: issue-content
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as: issue
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workspace:
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type: worktree
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branch: "{{ pipeline_id }}"
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exec:
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type: prompt
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source: |
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Analyze the Gitea issue and extract research topics.
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Identify:
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1. Key technical questions that need external research
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2. Domain concepts that require clarification
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3. External dependencies, libraries, or tools to investigate
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4. Similar problems/solutions that might provide guidance
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For each topic, provide:
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- A unique ID (TOPIC-001, TOPIC-002, etc.)
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- A clear title
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- Specific questions to answer (1-5 questions per topic)
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- Search keywords for web research
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- Priority (critical/high/medium/low based on relevance to solving the issue)
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- Category (technical/documentation/best_practices/security/performance/compatibility/other)
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Focus on topics that will provide actionable insights for the issue author.
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Limit to 10 most important topics.
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output_artifacts:
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- name: topics
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path: .wave/output/research-topics.json
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type: json
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handover:
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contract:
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type: json_schema
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source: .wave/output/research-topics.json
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schema_path: .wave/contracts/research-topics.schema.json
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on_failure: retry
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max_retries: 2
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- id: research-topics
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persona: researcher
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dependencies: [analyze-topics]
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memory:
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inject_artifacts:
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- step: fetch-issue
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artifact: issue-content
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as: issue
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- step: analyze-topics
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artifact: topics
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as: research_plan
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workspace:
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type: worktree
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branch: "{{ pipeline_id }}"
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exec:
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type: prompt
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source: |
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Research the topics identified in the research plan.
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For each topic in the research plan:
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1. Execute web searches using the provided keywords
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2. Evaluate source credibility (official docs > authoritative > community)
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3. Extract relevant findings with key points
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4. Include direct quotes where helpful
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5. Rate your confidence in the answer (high/medium/low/inconclusive)
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For each finding:
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- Assign a unique ID (FINDING-001, FINDING-002, etc.)
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- Provide a summary (20-2000 characters)
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- List key points as bullet items
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- Include source URL, title, and type
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- Rate relevance to the topic (0-1)
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Always include source URLs for attribution.
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If a topic yields no useful results, mark confidence as "inconclusive".
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Document any gaps in the research.
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output_artifacts:
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- name: findings
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path: .wave/output/research-findings.json
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type: json
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handover:
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contract:
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type: json_schema
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source: .wave/output/research-findings.json
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schema_path: .wave/contracts/research-findings.schema.json
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on_failure: retry
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max_retries: 2
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- id: synthesize-report
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persona: summarizer
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dependencies: [research-topics]
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memory:
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inject_artifacts:
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- step: fetch-issue
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artifact: issue-content
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as: original_issue
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- step: research-topics
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artifact: findings
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as: research
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workspace:
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type: worktree
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branch: "{{ pipeline_id }}"
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exec:
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type: prompt
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source: |
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Synthesize the research findings into a coherent report for the Gitea issue.
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Create a well-structured research report that includes:
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1. Executive Summary:
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- Brief overview (50-1000 chars)
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- Key findings (1-7 bullet points)
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- Primary recommendation
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- Confidence assessment (high/medium/low)
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2. Detailed Findings:
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- Organize by topic/section
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- Include code examples where relevant
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- Reference sources using SRC-### IDs
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3. Recommendations:
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- Actionable items with IDs (REC-001, REC-002, etc.)
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- Priority and effort estimates
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- Maximum 10 recommendations
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4. Sources:
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- List all sources with IDs (SRC-001, SRC-002, etc.)
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- Include URL, title, type, and reliability
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5. Pre-rendered Markdown:
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- Generate complete markdown_content field ready for Gitea comment
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- Use proper headers, bullet points, and formatting
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- Include a header: "## Research Findings (Wave Pipeline)"
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- End with sources section
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output_artifacts:
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- name: report
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path: .wave/output/research-report.json
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type: json
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handover:
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contract:
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type: json_schema
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source: .wave/output/research-report.json
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schema_path: .wave/contracts/research-report.schema.json
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on_failure: retry
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max_retries: 2
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- id: post-comment
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persona: gitea-commenter
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dependencies: [synthesize-report]
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memory:
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inject_artifacts:
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- step: fetch-issue
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artifact: issue-content
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as: issue
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- step: synthesize-report
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artifact: report
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as: report
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workspace:
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type: worktree
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branch: "{{ pipeline_id }}"
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exec:
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type: prompt
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source: |
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Post the research report as a comment on the Gitea issue.
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Steps:
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1. Read the issue details to get the repository and issue number
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2. Read the report to get the markdown_content
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3. Write the markdown content to a file, then use tea CLI to post the comment:
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# Write to file to avoid shell escaping issues with large markdown
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cat > /tmp/comment-body.md << 'COMMENT_EOF'
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<markdown_content>
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COMMENT_EOF
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tea comment <number> "$(cat /tmp/comment-body.md)" --repo <owner/repo> --login librete
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4. Add a footer to the comment:
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---
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*Generated by [Wave](https://github.com/re-cinq/wave) issue-research pipeline*
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5. Capture the result and verify success
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6. Clean up temp files
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Record the result with:
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- success: true/false
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- issue_reference: issue number and repository
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- comment: id, url, body_length (if successful)
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- error: code, message, retryable (if failed)
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- timestamp: current time
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output_artifacts:
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- name: comment-result
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path: .wave/output/comment-result.json
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type: json
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outcomes:
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- type: url
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extract_from: .wave/output/comment-result.json
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json_path: .comment.url
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label: "Research Comment"
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handover:
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contract:
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type: json_schema
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source: .wave/output/comment-result.json
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schema_path: .wave/contracts/comment-result.schema.json
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on_failure: retry
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max_retries: 3
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