land

odysseus0/symphony · updated Apr 8, 2026

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$npx skills add https://github.com/odysseus0/symphony --skill land
0 commentsdiscussion
summary

Preferred: use the asyncio watcher to monitor review comments, CI, and head

  • updates in parallel:
skill.md

Land

Goals

  • Ensure the PR is conflict-free with main.
  • Keep CI green and fix failures when they occur.
  • Squash-merge the PR once checks pass.
  • Do not yield to the user until the PR is merged; keep the watcher loop running unless blocked.
  • No need to delete remote branches after merge; the repo auto-deletes head branches.

Preconditions

  • gh CLI is authenticated.
  • You are on the PR branch with a clean working tree.

Steps

  1. Locate the PR for the current branch.
  2. Confirm the full gauntlet is green locally before any push.
  3. If the working tree has uncommitted changes, commit with the commit skill and push with the push skill before proceeding.
  4. Check mergeability and conflicts against main.
  5. If conflicts exist, use the pull skill to fetch/merge origin/main and resolve conflicts, then use the push skill to publish the updated branch.
  6. Ensure Codex review comments (if present) are acknowledged and any required fixes are handled before merging.
  7. Watch checks until complete.
  8. If checks fail, pull logs, fix the issue, commit with the commit skill, push with the push skill, and re-run checks.
  9. When all checks are green and review feedback is addressed, squash-merge and delete the branch using the PR title/body for the merge subject/body.
  10. Context guard: Before implementing review feedback, confirm it does not conflict with the user’s stated intent or task context. If it conflicts, respond inline with a justification and ask the user before changing code.
  11. Pushback template: When disagreeing, reply inline with: acknowledge + rationale + offer alternative.
  12. Ambiguity gate: When ambiguity blocks progress, use the clarification flow (assign PR to current GH user, mention them, wait for response). Do not implement until ambiguity is resolved.
    • If you are confident you know better than the reviewer, you may proceed without asking the user, but reply inline with your rationale.
  13. Per-comment mode: For each review comment, choose one of: accept, clarify, or push back. Reply inline (or in the issue thread for Codex reviews) stating the mode before changing code.
  14. Reply before change: Always respond with intended action before pushing code changes (inline for review comments, issue thread for Codex reviews).

Commands

# Ensure branch and PR context
branch=$(git branch --show-current)
pr_number=$(gh pr view --json number -q .number)
pr_title=$(gh pr view --json title -q .title)
pr_body=$(gh pr view --json body -q .body)

# Check mergeability and conflicts
mergeable=$(gh pr view --json mergeable -q .mergeable)

if [ "$mergeable" = "CONFLICTING" ]; then
  # Run the `pull` skill to handle fetch + merge + conflict resolution.
  # Then run the `push` skill to publish the updated branch.
fi

# Preferred: use the Async Watch Helper below. The manual loop is a fallback
# when Python cannot run or the helper script is unavailable.
# Wait for review feedback: Codex reviews arrive as issue comments that start
# with "## Codex Review — <persona>". Treat them like reviewer feedback: reply
# with a `[codex]` issue comment acknowledging the findings and whether you're
# addressing or deferring them.
while true; do
  gh api repos/{owner}/{repo}/issues/"$pr_number"/comments \
    --jq '.[] | select(.body | startswith("## Codex Review")) | .id' | rg -q '.' \
    && break
  sleep 10
done

# Watch checks
if ! gh pr checks --watch; then
  gh pr checks
  # Identify failing run and inspect logs
  # gh run list --branch "$branch"
  # gh run view <run-id> --log
  exit 1
fi

# Squash-merge (remote branches auto-delete on merge in this repo)
gh pr merge --squash --subject "$pr_title" --body "$pr_body"

Async Watch Helper

Preferred: use the asyncio watcher to monitor review comments, CI, and head updates in parallel:

python3 .agents/skills/land/land_watch.py

Exit codes:

  • 2: Review comments detected (address feedback)
  • 3: CI checks failed
  • 4: PR head updated (autofix commit detected)

Failure Handling

  • If checks fail, pull details with gh pr checks and gh run view --log, then fix locally, commit with the commit skill, push with the push skill, and re-run the watch.
  • Use judgment to identify flaky failures. If a failure is a flake (e.g., a timeout on only one platform), you may proceed without fixing it.
  • If CI pushes an auto-fix commit (authored by GitHub Actions), it does not trigger a fresh CI run. Detect the updated PR head, pull locally, merge origin/main if needed, add a real author commit, and force-push to retrigger CI, then restart the checks loop.
  • If all jobs fail with corrupted pnpm lockfile errors on the merge commit, the remediation is to fetch latest origin/main, merge, force-push, and rerun CI.
  • If mergeability is UNKNOWN, wait and re-check.
  • Do not merge while review comments (human or Codex review) are outstanding.
  • Codex review jobs retry on failure and are non-blocking; use the presence of ## Codex Review — <persona> issue comments (not job status) as the signal that review feedback is available.
  • Do not enable auto-merge; this repo has no required checks so auto-merge can skip tests.
  • If the remote PR branch advanced due to your own prior force-push or merge, avoid redundant merges; re-run the formatter locally if needed and git push --force-with-lease.

Review Handling

  • Codex reviews now arrive as issue comments posted by GitHub Actions. They start with ## Codex Review — <persona> and include the reviewer’s methodology + guardrails used. Treat these as feedback that must be acknowledged before merge.
  • Human review comments are blocking and must be addressed (responded to and resolved) before requesting a new review or merging.
  • If multiple reviewers comment in the same thread, respond to each comment (batching is fine) before closing the thread.
  • Fetch review comments via gh api and reply with a prefixed comment.
  • Use review comment endpoints (not issue comments) to find inline feedback:
    • List PR review comments:
      gh api repos/{owner}/{repo}/pulls/<pr_number>/comments
      
    • PR issue comments (top-level discussion):
      gh api repos/{owner}/{repo}/issues/<pr_number>/comments
      
    • Reply to a specific review comment:
      gh api -X POST /repos/{owner}/{repo}/pulls/<pr_number>/comments \
        -f body='[codex] <response>' -F in_reply_to=<comment_id>
      
  • in_reply_to must be the numeric review comment id (e.g., 2710521800), not the GraphQL node id (e.g., PRRC_...), and the endpoint must include the PR number (/pulls/<pr_number>/comments).
  • If GraphQL review reply mutation is forbidden, use REST.
  • A 404 on reply typically means the wrong endpoint (missing PR number) or insufficient scope; verify by listing comments first.
  • All GitHub comments generated by this agent must be prefixed with [codex].
  • For Codex review issue comments, reply in the issue thread (not a review thread) with [codex] and state whether you will address the feedback now or defer it (include rationale).
  • If feedback requires changes:
    • For inline review comments (human), reply with intended fixes ([codex] ...) as an inline reply to the original review comment using the review comment endpoint and in_reply_to (do not use issue comments for this).
    • Implement fixes, commit, push.
    • Reply with the fix details and commit sha ([codex] ...) in the same place you acknowledged the feedback (issue comment for Codex reviews, inline reply for review comments).
    • The land watcher treats Codex review issue comments as unresolved until a newer [codex] issue comment is posted acknowledging the findings.
  • Only request a new Codex review when you need a rerun (e.g., after new commits). Do not request one without changes since the last review.
    • Before requesting a new Codex review, re-run the land watcher and ensure there are zero outstanding review comments (all have [codex] inline replies).
    • After pushing new commits, the Codex review workflow will rerun on PR synchronization (or you can re-run the workflow manually). Post a concise root-level summary comment so reviewers have the latest delta:
      [codex] Changes since last review:
      - <short bullets of deltas>
      Commits: <sha>, <sha>
      Tests: <commands run>
      
    • Only request a new review if there is at least one new commit since the previous request.
    • Wait for the next Codex review comment before merging.

Scope + PR Metadata

  • The PR title and description should reflect the full scope of the change, not just the most recent fix.
  • If review feedback expands scope, decide whether to include it now or defer it. You can accept, defer, or decline feedback. If deferring or declining, call it out in the root-level [codex] update with a brief reason (e.g., out-of-scope, conflicts with intent, unnecessary).
  • Correctness issues raised in review comments should be addressed. If you plan to defer or decline a correctness concern, validate first and explain why the concern does not apply.
  • Classify each review comment as one of: correctness, design, style, clarification, scope.
  • For correctness feedback, provide concrete validation (test, log, or reasoning) before closing it.
  • When accepting feedback, include a one-line rationale in the root-level update.
  • When declining feedback, offer a brief alternative or follow-up trigger.
  • Prefer a single consolidated "review addressed" root-level comment after a batch of fixes instead of many small updates.
  • For doc feedback, confirm the doc change matches behavior (no doc-only edits to appease review).
how to use land

How to use land on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add land
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/odysseus0/symphony --skill land

The skills CLI fetches land from GitHub repository odysseus0/symphony and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/land

Reload or restart Cursor to activate land. Access the skill through slash commands (e.g., /land) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.636 reviews
  • Ishan Mensah· Dec 28, 2024

    Keeps context tight: land is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Gill· Dec 12, 2024

    We added land from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Shikha Mishra· Dec 8, 2024

    Registry listing for land matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Yash Thakker· Nov 27, 2024

    Solid pick for teams standardizing on skills: land is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 19, 2024

    land reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ishan Perez· Nov 19, 2024

    land has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hana Abebe· Nov 7, 2024

    I recommend land for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Omar Ghosh· Nov 3, 2024

    land fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kabir Jain· Oct 26, 2024

    Solid pick for teams standardizing on skills: land is focused, and the summary matches what you get after install.

  • Olivia Martinez· Oct 22, 2024

    land has been reliable in day-to-day use. Documentation quality is above average for community skills.

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