Agent Skill
2/7/2026

groom

Comprehensive backlog grooming. Orchestrates issue-creator skills and agents. Creates prioritized GitHub issues across all domains. Uses Gemini, Kimi, Codex, and Thinktank to flesh out issues with research, implementation recommendations, and multi-perspective validation. No flags. Always comprehensive.

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SKILL.md

Namegroom
DescriptionComprehensive backlog grooming. Orchestrates issue-creator skills and agents. Creates prioritized GitHub issues across all domains. Uses Gemini, Kimi, Codex, and Thinktank to flesh out issues with research, implementation recommendations, and multi-perspective validation. No flags. Always comprehensive.

name: groom description: | Interactive backlog grooming. Explore, brainstorm, discuss, then synthesize. Orchestrates agents and issue-creator skills. Creates prioritized GitHub issues. Enforces Misty Step org-wide standards. effort: high

/groom

Orchestrate interactive backlog grooming. Explore the product landscape with the user, brainstorm directions, then synthesize into prioritized issues.

Philosophy

Exploration before synthesis. Understand deeply, discuss with user, THEN create issues.

Orchestrator pattern. /groom invokes skills and agents, doesn't reimplement logic.

AI-augmented analysis. External tools provide specialized capabilities:

  • Gemini — Web-grounded research, current best practices, huge context
  • Codex — Implementation recommendations, concrete code suggestions
  • Thinktank — Multi-model consensus, diverse expert perspectives

Opinionated recommendations. Don't just present options. Recommend and justify.

Org-Wide Standards

All issues MUST comply with groom/references/org-standards.md. Load that file before creating any issues.

Process

Phase 1: Context

Step 1: Load or Gather Vision

Check for vision.md in project root:

If vision.md exists:

  1. Read and display current vision
  2. Ask: "Is this still accurate? Any updates?"
  3. If updates, rewrite vision.md

If vision.md doesn't exist:

  1. Interview: "What's your vision for this product? Where should it go?"
  2. Write response to vision.md

vision.md format:

# Vision

## One-Liner
[Single sentence: what this product is and who it's for]

## North Star
[The dream state — what does success look like in 2 years?]

## Key Differentiators
[What makes this different from alternatives?]

## Target User
[Who specifically is this for?]

## Current Focus
[Immediate priority this quarter?]

---
*Last updated: YYYY-MM-DD*
*Updated during: /groom session*

Store as {vision} for agent context throughout session.

Step 2: Capture What's On Your Mind

Before structured analysis:

Anything on your mind? Bugs, UX friction, missing features, nitpicks?
These become issues alongside the automated findings.

(Skip if nothing comes to mind)

For each item: clarify if needed (one follow-up max), assign tentative priority. Don't create issues yet — collect for Phase 4.

Step 3: Quick Backlog Audit

gh issue list --state open --limit 100 --json number,title,labels,body,createdAt,updatedAt

Evaluate each existing issue:

  1. Still relevant? Given current vision and codebase state
  2. Priority correct? Focus may have shifted
  3. Duplicate? Will new findings cover this?
  4. Actionable? Can someone pick this up?

Present findings. Don't auto-close anything yet. "Here's where we stand: X open issues, Y look stale, Z may need reprioritization."

Phase 2: Discovery

Launch agents in parallel:

AgentFocus
Product strategistGaps vs vision, user value opportunities
Technical archaeologistCode health, architectural debt, improvement patterns
Domain auditorsRun check-* skills (audit-only, no issue creation)
Growth analystAcquisition, activation, retention opportunities

Domain auditors invoke in parallel:

  • /check-production, /check-quality, /check-docs, /check-observability
  • /check-product-standards, /check-stripe, /check-bitcoin, /check-lightning
  • /check-virality, /check-landing, /check-onboarding

Synthesize findings into 3-5 strategic themes with evidence. Examples: "reliability foundation," "onboarding redesign," "API expansion."

Present: "Here are the themes I see across the analysis. Which interest you?"

Phase 3: Exploration Loop

For each theme the user wants to explore:

  1. Pitch — Agents brainstorm approaches. What it looks like, what it costs, what it enables.
  2. Present — 3-5 competing approaches with tradeoffs. Recommend one.
  3. Discuss — User steers. "What about X?" "I prefer Y because Z."
  4. Refine — Agents dig deeper on selected direction. Architecture, toolchain, risk.
  5. Decide or iterate — Lock direction or explore more.

Repeats per theme. Revisits allowed. Continues until user says "lock it in."

Use AskUserQuestion for structured decisions. Plain conversation for exploration.

Team-Accelerated Exploration (for large sessions):

TeammateFocus
Infra & qualityProduction, quality gates, observability
Product & growthLanding, onboarding, virality, strategy
Payments & integrationsStripe, Bitcoin, Lightning
AI enrichmentGemini research, Codex implementation recs

Teammates share findings via messages. Cross-pollination encouraged: when Infra finds a P0, Growth checks if it affects onboarding.

Phase 4: Synthesis

Once directions are locked for explored themes:

Step 1: Create Issues

Create atomic, implementable GitHub issues from agreed directions. Include user observations from Phase 1 Step 2.

Invoke log-* skills for domains where automated issue creation helps:

  • /log-production-issues, /log-quality-issues, /log-doc-issues
  • /log-observability-issues, /log-product-standards-issues
  • /log-stripe-issues, /log-bitcoin-issues, /log-lightning-issues
  • /log-virality-issues, /log-landing-issues, /log-onboarding-issues

For strategic issues from exploration: create directly with full context.

Step 2: Enrich

Each issue gets:

  • Problem statement (from exploration discussion)
  • Context and evidence
  • Recommended approach (from locked direction)
  • Acceptance criteria
  • Effort estimate

Use Codex for implementation recommendations on P0/P1 issues. Use Gemini for current best practices research. Use Thinktank for architecture validation on complex issues.

Step 3: Organize

Apply org-wide standards (load groom/references/org-standards.md):

  • Canonical labels (priority, type, horizon, effort, source, domain)
  • Issue types via GraphQL
  • Milestone assignment
  • Project linking (Active Sprint, Product Roadmap)

Close stale issues identified in Phase 1 Step 3 (with user confirmation). Migrate legacy labels.

Step 4: Deduplicate

Three sources of duplicates:

  1. User observations that overlap with automated findings
  2. New issues from log-* skills that overlap with each other
  3. New issues that overlap with existing kept issues

Keep the most comprehensive. Close others with link to canonical.

Step 5: Summarize

GROOM SUMMARY
=============

Themes Explored: [list]
Directions Locked: [list]

Issues by Priority:
- P0 (Critical): N
- P1 (Essential): N
- P2 (Important): N
- P3 (Nice to Have): N

Recommended Execution Order:
1. [P0] ...
2. [P1] ...

Ready for /autopilot: [issue numbers]
View all: gh issue list --state open

Related Skills

Audit Primitives (Phase 2)

  • /check-production, /check-docs, /check-quality, /check-observability
  • /check-product-standards, /check-stripe, /check-bitcoin, /check-lightning
  • /check-virality, /check-landing, /check-onboarding

Issue Creators (Phase 4)

  • /log-production-issues, /log-doc-issues, /log-quality-issues
  • /log-observability-issues, /log-product-standards-issues
  • /log-stripe-issues, /log-bitcoin-issues, /log-lightning-issues
  • /log-virality-issues, /log-landing-issues, /log-onboarding-issues

Standalone Domain Work

/check-production     # Audit only
/log-production-issues # Create issues
/triage              # Fix highest priority
Skills Info
Original Name:groomAuthor:phrazzld