source-command-gsd-review-backlog
Review and promote backlog items to active milestone
Best use case
source-command-gsd-review-backlog is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review and promote backlog items to active milestone
Teams using source-command-gsd-review-backlog should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/source-command-gsd-review-backlog/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How source-command-gsd-review-backlog Compares
| Feature / Agent | source-command-gsd-review-backlog | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Review and promote backlog items to active milestone
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# source-command-gsd-review-backlog
Use this skill when the user asks to run the migrated source command `gsd-review-backlog`.
## Command Template
<objective>
Review all 999.x backlog items and optionally promote them into the active
milestone sequence or remove stale entries.
</objective>
<process>
1. **List backlog items:**
```bash
ls -d .planning/phases/999* 2>/dev/null || echo "No backlog items found"
```
2. **Read ROADMAP.md** and extract all 999.x phase entries:
```bash
cat .planning/ROADMAP.md
```
Show each backlog item with its description, any accumulated context (CONTEXT.md, RESEARCH.md), and creation date.
3. **Present the list to the user** via AskUserQuestion:
- For each backlog item, show: phase number, description, accumulated artifacts
- Options per item: **Promote** (move to active), **Keep** (leave in backlog), **Remove** (delete)
4. **For items to PROMOTE:**
- Find the next sequential phase number in the active milestone
- Rename the directory from `999.x-slug` to `{new_num}-slug`:
```bash
NEW_NUM=$(gsd-sdk query phase.add "${DESCRIPTION}" --raw)
```
- Move accumulated artifacts to the new phase directory
- Update ROADMAP.md: move the entry from `## Backlog` section to the active phase list
- Remove `(BACKLOG)` marker
- Add appropriate `**Depends on:**` field
5. **For items to REMOVE:**
- Delete the phase directory
- Remove the entry from ROADMAP.md `## Backlog` section
6. **Commit changes:**
```bash
gsd-sdk query commit "docs: review backlog — promoted N, removed M" .planning/ROADMAP.md
```
7. **Report summary:**
```
## 📋 Backlog Review Complete
Promoted: {list of promoted items with new phase numbers}
Kept: {list of items remaining in backlog}
Removed: {list of deleted items}
```
</process>Related Skills
llm-wiki-source-extraction-coverage
Doc-type-aware extraction contract for llm-wiki source ingestion with measurable coverage and source-anchored traceability. Use when (1) ingesting a PDF, DOCX, XLSX, PPTX, HTML, or scanned-image source into a wiki `sources/` page, (2) computing the pre-extraction estimate (what fraction of the source we expect to recover) and post-extraction yield (what fraction we actually recovered), (3) anchoring wiki claims back to specific page / paragraph / cell / slide positions in the source so a reviewer can re-verify or revise against the actual document, (4) deciding whether OCR fallback or manual transcription is needed. Codifies workspace-hub's existing OCR fallback chain and python-docx / openpyxl / trafilatura patterns into a format-specific routing table. Companion to research/llm-wiki-page-shape-contract (Rule 7 input-layer pages) and research/llm-wiki — this skill is the defense against silent extraction failure.
worldenergydata-source-readiness
Route agents to the canonical worldenergydata source-readiness skill and summary script. Use when asked for worldenergydata data completeness, data locations, latest known data dates, scheduler freshness, source-readiness status, or acceptance-criteria inputs across the repo ecosystem.
plan-review-prompt-refresh-after-plan-edits
Refresh reviewer prompt files from the latest on-disk plan before every adversarial re-review. Prevents Codex/Gemini from critiquing stale plan text after local edits.
tdd-verification-and-adversarial-review
Verify pre-written TDD tests pass, conduct adversarial code review on committed diffs, and route findings to existing issues
multi-source-tax-document-reconciliation
Verify generated tax forms against source documents by line-by-line comparison, not just totals
multi-role-agent-contract-review-pipeline
Execute a 4-role agent team (Planner/Architect/Reviewer/Integrator) pipeline for self-reviewing knowledge artifacts before delivery
gsd-adversarial-review-pattern
Catch hidden test failures by running adversarial review on sparse-data edge cases before final push
adversarial-code-review-tdd
Systematic adversarial review pattern to identify breaking assumptions in already-passing test suites
adversarial-code-review-for-committed-diffs
Systematic process for reviewing already-committed code changes to catch type inconsistencies, edge cases, and docstring gaps
adversarial-code-review-and-fix
Systematic pattern for catching design flaws in already-passing code through adversarial review, then fixing them with TDD confirmation.
learned-git-worktree-hook-path-and-real-hook-shape-review
Catch hook-installation and governance bugs that only appear in linked git worktrees or against the real generated hook shape, not simplified test fixtures.
ten-agent-pre-plan-review-wave
Launch and verify a 10-agent planning-only wave that moves open GitHub issues into status:plan-review using one isolated worktree per issue, wave-specific continuation cron, and post-run artifact-reconciliation checks.