* Update skill installation path and document new features Installation path changes: - Update from ~/.claude/skills/bd to bd-issue-tracking - Matches internal skill name for consistency with other skills Documentation additions: - Add 3 new reference files to documentation list - Document compaction survival patterns (critical for Claude Code) - Mention self-check checklists and quality guidelines This prepares the README for upcoming skill content improvements. * Add new reference files for enhanced skill guidance New reference documentation: 1. ISSUE_CREATION.md - When to ask vs create issues - Decision criteria for knowledge work vs technical work - Issue quality guidelines and best practices - Design vs acceptance criteria guidance - Self-check questions for well-scoped issues 2. RESUMABILITY.md - Making issues resumable across sessions - Patterns for complex technical work with APIs - Working code examples and API response samples - When enhanced documentation matters vs simple descriptions - Critical for multi-session work and crash recovery 3. STATIC_DATA.md - Using bd for reference databases - Alternative use case beyond work tracking - Glossaries and terminology management - Dual format patterns (database + markdown) - When bd helps vs when simpler formats suffice These additions emerged from real-world usage patterns and enable Claude to make better decisions about issue structure and resumability. * Enhance existing skill files with real-world usage patterns SKILL.md major enhancements (~194 net lines added): - Add "Test Yourself" decision framework with self-check questions - Document compaction survival patterns (critical for Claude Code) - Add Notes Quality Self-Check (future-me test, stranger test) - Session Start Checklist with copy-paste templates - Field Usage Reference table (when to use each bd field) - Progress Checkpointing triggers and patterns - Issue Creation Checklist with quality self-checks - Enhanced session handoff protocols WORKFLOWS.md enhancements (~114 lines added): - Session handoff workflow with detailed checklists - Collaborative handoff between Claude and user - Compaction survival workflow - Notes format guidelines (current state, not cumulative) - Session start checklist expansion BOUNDARIES.md updates (~49 lines removed): - Streamlined content (moved detailed workflows to WORKFLOWS.md) - Retained core decision criteria - Improved examples and integration patterns CLI_REFERENCE.md minor updates: - Additional command examples - Clarified flag usage These improvements emerged from extensive real-world usage, particularly around crash recovery, compaction events, and multi-session workflows. The additions make the skill more practical for autonomous agent use.
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Making Issues Resumable Across Sessions
When Resumability Matters
Use enhanced documentation for:
- Multi-session technical features with API integration
- Complex algorithms requiring code examples
- Features with specific output format requirements
- Work with "occult" APIs (undocumented capabilities)
Skip for:
- Simple bug fixes with clear scope
- Well-understood patterns (CRUD operations, etc.)
- Single-session tasks
- Work with obvious acceptance criteria
The test: Would a fresh Claude instance (or you after 2 weeks) struggle to resume this work from the description alone? If yes, add implementation details.
Anatomy of a Resumable Issue
Minimal (Always Include)
Description: What needs to be built and why
Acceptance Criteria: Concrete, testable outcomes (WHAT not HOW)
Enhanced (Complex Technical Work)
Notes Field - IMPLEMENTATION GUIDE:
WORKING CODE:
```python
# Tested code that queries the API
service = build('drive', 'v3', credentials=creds)
result = service.about().get(fields='importFormats').execute()
# Returns: {'text/markdown': ['application/vnd.google-apps.document'], ...}
API RESPONSE SAMPLE: Shows actual data structure (not docs description)
DESIRED OUTPUT FORMAT:
# Example of what the output should look like
Not just "return markdown" but actual structure
RESEARCH CONTEXT: Why this approach? What alternatives were considered? Key discoveries that informed the design.
## Real Example: Before vs After
### ❌ Not Resumable
Title: Add dynamic capabilities resources Description: Query Google APIs for capabilities and return as resources Acceptance: Resources return capability info
**Problem:** Future Claude doesn't know:
- Which API endpoints to call
- What the responses look like
- What format to return
### ✅ Resumable
Title: Add dynamic capabilities resources Description: Query Google APIs for system capabilities (import formats, themes, quotas) that aren't in static docs. Makes server self-documenting.
Notes: IMPLEMENTATION GUIDE
WORKING CODE (tested):
from workspace_mcp.tools.drive import get_credentials
from googleapiclient.discovery import build
creds = get_credentials()
service = build('drive', 'v3', credentials=creds)
about = service.about().get(
fields='importFormats,exportFormats,folderColorPalette'
).execute()
# Returns:
# - importFormats: dict, 49 entries like {'text/markdown': [...]}
# - exportFormats: dict, 10 entries
# - folderColorPalette: list, 24 hex strings
OUTPUT FORMAT EXAMPLE:
# Drive Import Formats
Google Drive supports 49 import formats:
## Text Formats
- **text/markdown** → Google Docs ✨ (NEW July 2024)
- text/plain → Google Docs
...
RESEARCH CONTEXT: text/markdown support announced July 2024 but NOT in static Google docs. Google's workspace-developer MCP server doesn't expose this. This is why dynamic resources matter.
Acceptance Criteria:
- User queries workspace://capabilities/drive/import-formats
- Response shows all 49 formats including text/markdown
- Output is readable markdown, not raw JSON
- Queries live API (not static data)
**Result:** Fresh Claude instance can:
1. See working API query code
2. Understand response structure
3. Know desired output format
4. Implement with context
## Optional Template
Copy this into notes field for complex technical features:
```markdown
IMPLEMENTATION GUIDE FOR FUTURE SESSIONS:
WORKING CODE (tested):
```language
# Paste actual code that works
# Include imports and setup
# Show what it returns
API RESPONSE SAMPLE:
{
"actualField": "actualValue",
"structure": "as returned by API"
}
DESIRED OUTPUT FORMAT:
Show what the final output should look like
Not just "markdown" but actual structure/style
RESEARCH CONTEXT:
- Why this approach?
- What alternatives considered?
- Key discoveries?
- Links to relevant docs/examples?
## Anti-Patterns
### ❌ Over-Documenting Simple Work
```markdown
Title: Fix typo in README
Notes: IMPLEMENTATION GUIDE
WORKING CODE: Open README.md, change "teh" to "the"...
Problem: Wastes tokens on obvious work.
❌ Design Details in Acceptance Criteria
Acceptance:
- [ ] Use batchUpdate approach
- [ ] Call API with fields parameter
- [ ] Format as markdown with ## headers
Problem: Locks implementation. Should be in Design/Notes, not Acceptance.
❌ Raw JSON Dumps
API RESPONSE:
{giant unformatted JSON blob spanning 100 lines}
Problem: Hard to read. Extract relevant parts, show structure.
✅ Right Balance
API RESPONSE SAMPLE:
Returns dict with 49 entries. Example entries:
- 'text/markdown': ['application/vnd.google-apps.document']
- 'text/plain': ['application/vnd.google-apps.document']
- 'application/pdf': ['application/vnd.google-apps.document']
When to Add This Detail
During issue creation:
- Already have working code from research? Include it.
- Clear output format in mind? Show example.
During work (update notes):
- Just got API query working? Add to notes.
- Discovered important context? Document it.
- Made key decision? Explain rationale.
Session end:
- If resuming will be hard, add implementation guide.
- If obvious, skip it.
The principle: Help your future self (or next Claude) resume without rediscovering everything.