visualize-plan

What this skill does

# Plan Visualization

Render planned changes as structured ASCII visualizations with risk analysis, execution order, and impact metrics. Every section answers a specific reviewer question.

**Core principle:** Encode judgment into visualization, not decoration.

```bash
/ork:visualize-plan                          # Auto-detect from current branch
/ork:visualize-plan billing module redesign  # Describe the plan
/ork:visualize-plan #234                     # Pull from GitHub issue
```

## Argument Resolution

```python
PLAN_INPUT = "$ARGUMENTS"    # Full argument string
PLAN_TOKEN = "$ARGUMENTS[0]" # First token — could be issue "#234" or plan description
# If starts with "#", treat as GitHub issue number. Otherwise, plan description.
# $ARGUMENTS (full string) for multi-word descriptions (CC 2.1.59 indexed access)
```

---

## CRITICAL: Task Tracking

```python
# 1. Create main task IMMEDIATELY
TaskCreate(subject="Visualize plan: {PLAN_INPUT}", description="Plan visualization with ASCII rendering", activeForm="Analyzing plan context")

# 2. Create subtasks for each phase
TaskCreate(subject="Detect or clarify plan context", activeForm="Detecting plan context")          # id=2
TaskCreate(subject="Gather data and explore architecture", activeForm="Gathering plan data")       # id=3
TaskCreate(subject="Render tier 1 header", activeForm="Rendering header")                          # id=4
TaskCreate(subject="Render sections + dispatch to chosen format(s)", activeForm="Rendering sections") # id=5
TaskCreate(subject="Offer actions and store in memory", activeForm="Finalizing visualization")     # id=6

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Data gathering needs context first
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Header needs gathered data
TaskUpdate(taskId="5", addBlockedBy=["4"])  # Sections need header rendered
TaskUpdate(taskId="6", addBlockedBy=["5"])  # Actions need sections done

# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2")  # Verify blockedBy is empty

# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask
```

## STEP -1: Check Memory for Prior Plans

```python
# Search for related prior visualizations
mcp__memory__search_nodes(query="plan visualization {PLAN_INPUT}")
# If found, offer to compare with previous plan
```

## STEP 0: Detect or Clarify Plan Context

**First**, attempt auto-detection by running `scripts/detect-plan-context.sh`:

```bash
bash "$SKILL_DIR/scripts/detect-plan-context.sh"
```

This outputs branch name, issue number (if any), commit count, and file change summary.

**If auto-detection finds a clear plan** (branch with commits diverging from main, or issue number in args), proceed to Step 1.

**If ambiguous**, clarify with AskUserQuestion:

```python
AskUserQuestion(
  questions=[{
    "question": "What should I visualize?",
    "header": "Source",
    "options": [
      {"label": "Current branch changes (Recommended)", "description": "Auto-detect from git diff against main"},
      {"label": "Describe the plan", "description": "I'll explain what I'm planning to change"},
      {"label": "GitHub issue", "description": "Pull plan from a specific issue number"},
      {"label": "Quick file diff only", "description": "Just show the change manifest, skip analysis"}
    ],
    "multiSelect": false
  }]
)
```

---

## STEP 0.5: Choose Output Format (Front Door)

Decide **how** to render before gathering data. First **probe capabilities**, then ask only for what's available. Full procedure: `Read("${CLAUDE_SKILL_DIR}/references/format-dispatch.md")`.

Use the established MCP-probe pattern — `Read("${CLAUDE_SKILL_DIR}/../chain-patterns/references/mcp-detection.md")` — not ad-hoc checks:

```python
# infographic is available IFF the notebooklm studio tool resolves:
ToolSearch(query="select:mcp__notebooklm-mcp__studio_create")
```

Gate the options: **ascii** always (the floor); **playground** if the `playground` skill is installed (ships with ork); **infographic** if `studio_create` resolved above (server reachable + `nlm login` done). If only ASCII is available, skip the question.

If only ASCII is available, **skip the question** and render ASCII. Otherwise ask (hide ungated options, surface a one-line install/auth hint instead):

```python
AskUserQuestion(questions=[{
  "question": "How should I render this plan?",
  "header": "Format",
  "options": [
    {"label": "ASCII + emojis (Recommended)", "description": "Fast, in-chat, zero-dependency. Always the floor — rendered first even if you also pick a richer format."},
    {"label": "Interactive playground", "description": "Single-file HTML explorer written to docs/<branch-dir>/plan-viz.html (also satisfies the PR Playground gate). Delegates to the playground skill."},
    {"label": "NotebookLM infographic", "description": "Stakeholder-ready infographic/slides via notebooklm studio_create. Async — fired and notified, never blocks."},
    {"label": "All available", "description": "ASCII inline now + the richer formats linked as they finish."}
  ],
  "multiSelect": false
}])
```

**ASCII floor rule:** always render ASCII first/inline regardless of choice — never `await` the async NotebookLM job. Record the chosen format(s) as `FORMATS` for STEP 4 dispatch.

---

## STEP 1: Gather Data

Run `scripts/analyze-impact.sh` for precise counts:

```bash
bash "$SKILL_DIR/scripts/analyze-impact.sh"
```

This produces: files by action (add/modify/delete), line counts, test files affected, and dependency changes.

For architecture-level understanding **and the default before/after section [0]**, spawn an Explore agent that maps the component graph at BOTH the base and the head:

```python
Agent(
  subagent_type="Explore",
  prompt="Map component architecture of {affected_directories} at TWO points: (a) base = each file as returned by `git show origin/main:<path>` (NOT the working tree — avoids conflating uncommitted edits), (b) head = current working tree. Return per point: components, dependencies, data flows; mark what is added [+], removed [-], or changed [~] between them. Use the ascii-visualizer skill for diagrams.",
  model="haiku"
)
```

If the diff touches frontend (`*.tsx`/`*.css`/route files), also run a `design-context-extract` pass so the design surface is part of before/after. Patterns: `Read("${CLAUDE_SKILL_DIR}/references/before-after-arch-patterns.md")`.

Build a compact **plan brief** (markdown) from this data — the single interchange every non-ASCII format consumes (see `format-dispatch.md`).

---

## STEP 2: Render Tier 1 Header (Always)

Use `assets/tier1-header.md` template. Load `Read("${CLAUDE_SKILL_DIR}/references/visualization-tiers.md")` for field computation (risk level, confidence, reversibility).

```
PLAN: {plan_name} ({issue_ref})  |  {phase_count} phases  |  {file_count} files  |  +{added} -{removed} lines
Risk: {risk_level}  |  Confidence: {confidence}  |  Reversible until {last_safe_phase}
Branch: {branch} -> {base_branch}

[0] Before/After  [1] Changes  [2] Execution  [3] Risks  [4] Decisions  [5] Impact  [all]
```

---

## STEP 3: Ask Which Sections to Expand

**Section [0] Before/After is rendered automatically as the lead** whenever the Explore map shows structural changes (skipped with a one-line note otherwise) — so it is never buried behind a picker choice. The options below select among the remaining sections [1]–[5]; "All sections" includes [0].

```python
AskUserQuestion(
  questions=[{
    "question": "Which sections to render?",
    "header": "Sections",
    "options": [
      {"label": "All sections", "description": "Full visualization with all 6 core sections"},
      {"label": "Changes + Execution", "description": "File diff tree and execution swimlane"},
      {"label": "Risks + Decisions", "description": "Risk dashboard and deci