Explore Feature

Analyze the current codebase and workflow state to recommend what to build next.

Arguments

$ARGUMENTS - Optional focus area (for example: "performance", "refactoring", "cost", "usability", "security")

OpenSpec Execution Preference

Use OpenSpec-generated runtime assets first, then CLI fallback:

• Claude: .claude/commands/opsx/*.md or .claude/skills/openspec-*/SKILL.md
• Codex: .codex/skills/openspec-*/SKILL.md
• Gemini: .gemini/commands/opsx/*.toml or .gemini/skills/openspec-*/SKILL.md
• Fallback: direct openspec CLI commands

Coordinator Integration (Optional)

Use docs/coordination-detection-template.md as the shared detection preamble.

• Detect transport and capability flags at skill start
• Execute hooks only when the matching CAN_* flag is true
• If coordinator is unavailable, continue with standalone behavior

Steps

0. Detect Coordinator and Recall Memory

At skill start, run the coordination detection preamble and set:

• COORDINATOR_AVAILABLE
• COORDINATION_TRANSPORT (mcp|http|none)
• CAN_LOCK, CAN_QUEUE_WORK, CAN_HANDOFF, CAN_MEMORY, CAN_GUARDRAILS

If CAN_MEMORY=true, recall relevant history before analysis:

• MCP path: call recall with tags like ["feature-discovery", "<focus-area>"]
• HTTP path: use scripts/coordination_bridge.py try_recall(...)

On recall failure/unavailability, continue normally and log informationally.

1. Gather Current State

openspec list --specs
openspec list

Collect:

• Existing capabilities and requirement density
• Active changes already in progress
• Gaps between specs and current priorities

2. Analyze Architecture and Code Signals

test -f docs/architecture-analysis/architecture.summary.json || make architecture

Use:

• docs/architecture-analysis/architecture.summary.json
• docs/architecture-analysis/architecture.diagnostics.json (if present)
• docs/architecture-analysis/parallel_zones.json

Look for:

• Structural bottlenecks and high-impact nodes
• Refactoring opportunities and coupling hotspots
• Code smell clusters and maintainability risks
• Usability gaps, reliability risks, performance/cost hotspots

3. Produce Ranked Opportunities

Generate a ranked shortlist (3-7 items), each with:

• Problem statement
• User/developer impact
• Estimated effort (S/M/L)
• Risk level (low/med/high)
• Strategic fit (low/med/high)
• Weighted score using a reproducible formula:
  • score = impact*0.4 + strategic_fit*0.25 + (4-effort)*0.2 + (4-risk)*0.15
  • Use numeric mapping: low=1, med=2, high=3; S=1, M=2, L=3
• Category bucket:
  • quick-win (high score, low effort/risk)
  • big-bet (high potential impact with medium/high effort)
• Suggested OpenSpec change-id prefix (add-, update-, refactor-, remove-)
• blocked-by dependencies (existing change-ids, missing infra, unresolved design decisions)
• Recommended next action (/plan-feature now, or defer)

4. Recommend Next Execution Path

For the top recommendation, include:

• Why now
• Dependencies or blockers
• Suggested starter command:
  • /plan-feature <description>
  • or /iterate-on-plan <change-id> if a related proposal exists

5. Persist Discovery Artifacts

Write/update machine-readable discovery artifacts:

• docs/feature-discovery/opportunities.json (current ranked opportunities)
• docs/feature-discovery/history.json (recent top recommendations with timestamps/status)

Rules:

• If an opportunity from recent history is still deferred and unchanged, lower its default priority unless new evidence justifies reranking
• Include stable IDs so /prioritize-proposals can reference opportunities without text matching

Output

• Prioritized feature opportunity list with rationale
• One recommended next feature and concrete follow-up command
• Machine-readable discovery output path(s) and whether recommendation history altered ranking