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docs: add comprehensive documentation for summary attachments feature
Adds detailed documentation section explaining the summary attachments feature including: - Configuration options and their defaults - What gets included in each type of summary - Markdown examples showing dispatched and running comments - When to enable/disable the feature - Workflow integration guide for status.json Complements the implementation from the previous commit. Co-authored-by: Shelley <shelley@exe.dev>
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model = "claude-sonnet-4.5"
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```
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## Summary Attachments in GitHub Comments
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By default, gh-pm posts minimal status comments to GitHub issues. You can optionally enable **summary attachments** to include detailed analysis breakdowns and execution progress directly in the comments.
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### Configuration
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Add these settings to your `~/.gh-pm/gh-pm.toml`:
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```toml
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[settings]
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# Enable summary attachments (default: false)
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attach_summaries = true
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# Include LLM analysis breakdown in dispatch comments (default: true when attach_summaries=true)
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summary_analyze = true
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# Include execution progress in status update comments (default: true when attach_summaries=true)
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summary_running = true
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# Summary display format: "detailed" or "compact" (default: "detailed")
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summary_format = "detailed"
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# Wrap summaries in collapsible <details> sections (default: true)
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summary_use_collapsible = true
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# Maximum summary length in characters (default: 10000)
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summary_max_length = 10000
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```
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### What Gets Included
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**Analysis Summaries** (when `summary_analyze = true`):
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- Added to the "Task Dispatched" comment
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- Contains the full LLM-generated analysis from `task.json`
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- Includes task interpretation, sub-tasks, dependencies, and approach
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- Wrapped in collapsible `<details>` section by default
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**Running Summaries** (when `summary_running = true`):
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- Added to "Task In Progress" comments
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- Contains the `summary` field from `status.json` if present
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- Shows completed steps, current status, remaining work
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- Useful for tracking workflow execution progress
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### Example: Dispatched Comment With Analysis
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```markdown
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## 🤖 gh-pm: Task Dispatched
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| Field | Value |
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|-------|-------|
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| Task ID | `owner-repo-issue-42` |
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| Status | ⏳ Running |
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| Started | 2026-03-31 10:00:00 UTC |
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_Managed by gh-pm. Updates will follow._
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### 📋 Analysis
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<details>
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<summary>View Analysis Details</summary>
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# 🎯 Project Manager Analysis: Issue #42
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## Issue Summary
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**Feature Request**: Add new API endpoint...
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## Sub-Task Breakdown
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### Task 1: Requirements Definition
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- Define API contract
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- Document input/output schemas
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...
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</details>
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```
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### Example: Status Update With Running Summary
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```markdown
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## 🤖 gh-pm: Task In Progress
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| Field | Value |
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|-------|-------|
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| Task ID | `owner-repo-issue-42` |
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| Status | ⏳ Running |
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| Updated | 2026-03-31 10:15:00 UTC |
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### Progress
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Executing workflow step 3 of 5
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### 📈 Execution Summary
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<details>
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<summary>View Execution Details</summary>
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## Execution Progress
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**Completed**:
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- ✓ Step 1: Requirements analyzed
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- ✓ Step 2: Implementation started
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**Current**:
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- ⏳ Step 3: Writing tests
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**Remaining**:
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- Step 4: Code review
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- Step 5: Documentation
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</details>
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```
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### When to Enable
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**Enable summaries when:**
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- You want full visibility into task analysis and progress
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- Your team reviews GitHub comments for context
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- You're debugging workflow execution
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- You need audit trails of agent reasoning
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**Keep summaries disabled when:**
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- You prefer minimal comment threads
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- Analysis is very verbose and clutters discussions
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- You primarily track progress through other tools
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- You're concerned about GitHub API rate limits
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### Workflow Integration
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For workflows to provide running summaries, they should write a `status.json` file with a `summary` field:
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```json
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{
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"message": "Executing step 3",
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"summary": "## Execution Progress\n\n- Completed: Step 1, Step 2\n- Current: Step 3\n- Remaining: Step 4, Step 5"
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}
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```
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gh-pm will automatically include this summary in status update comments when `summary_running = true`.
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## Using Shelley as LLM Backend
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On [exe.dev](https://exe.dev) VMs, you can use **shelley** as your LLM backend. Shelley is a local AI agent that runs on the same host, eliminating the need for external API keys and enabling fully offline operation.

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