skill-slide-critic

name	skill-slide-critic
description	Interactive critique loop for slide presentations. Delegates to slide-critic-agent, presents findings to user, collects accept/reject/modify decisions, produces filtered critique report.
allowed-tools	Agent, Bash, Edit, Read, Write, AskUserQuestion
context	fork
agent	slide-critic-agent

Slide Critic Skill

Interactive critique feedback loop for academic presentations. Delegates to slide-critic-agent for initial material review, parses the structured critique report, presents findings to the user grouped by severity tier, collects accept/reject/modify decisions, loops until all issues are addressed, and produces a final filtered critique report consumable by /plan.

IMPORTANT: This skill implements the skill-internal postflight pattern. After the subagent returns and the interactive loop completes, this skill handles all postflight operations (status update, artifact linking, git commit) before returning.

Context References

Reference (do not load eagerly):

• Path: .claude/context/formats/return-metadata-file.md - Metadata file schema
• Path: .claude/context/patterns/postflight-control.md - Marker file protocol
• Path: .claude/context/patterns/file-metadata-exchange.md - File I/O helpers
• Path: .claude/context/patterns/jq-escaping-workarounds.md - jq escaping patterns (Issue #1132)

Note: This skill runs delegation then interactive Q&A. Context is loaded by the delegated agent.

Trigger Conditions

This skill activates when:

• /critique command with task number input
• /research on a task with task_type: "present:slides" and workflow_type: "slides_critique"
• Present extension is available
• Task has existing slide materials to review (research reports, plans, or assembled slides)

---

Input Parameters

Required Parameters

• task_number - Task number (must exist in state.json with task_type containing "slides")
• session_id - Session ID from orchestrator

Optional Parameters

• focus_categories - Subset of 6 rubric categories to prioritize (e.g., ["Narrative Flow", "Timing Balance"])
• audience_context - Audience description for calibrating review
• materials_to_review - Override default material discovery (array of file paths)

---

Execution Flow

Stage 1: Input Validation

Validate required inputs:

• task_number - Must be provided and exist in state.json
• Verify task_type contains "slides" (supports "present:slides", "slides")

# Lookup task
task_data=$(jq -r --argjson num "$task_number" \
  '.active_projects[] | select(.project_number == $num)' \
  specs/state.json)

# Validate exists
if [ -z "$task_data" ]; then
  return error "Task $task_number not found"
fi

# Extract fields
task_type=$(echo "$task_data" | jq -r '.task_type // ""')
status=$(echo "$task_data" | jq -r '.status')
project_name=$(echo "$task_data" | jq -r '.project_name')
description=$(echo "$task_data" | jq -r '.description // ""')
forcing_data=$(echo "$task_data" | jq -r '.forcing_data // {}')

# Validate task_type (supports "present:slides" or legacy "slides")
if [ "$task_type" != "present:slides" ] && [ "$task_type" != "slides" ]; then
  return error "Task $task_number is not a slides task (task_type=$task_type)"
fi

# Extract talk_type from forcing_data
talk_type=$(echo "$forcing_data" | jq -r '.talk_type // "CONFERENCE"')

---

Stage 2: Preflight Status Update and Postflight Marker

Source skill-base.sh once, then follow @.claude/context/patterns/skill-preflight-flow.md in full for Stage 2 (preflight status update) and Stage 3 (marker creation):

source .claude/scripts/skill-base.sh
padded_num=$(printf "%03d" "$task_number")
task_dir="specs/${padded_num}_${project_name}"
mkdir -p "$task_dir"
skill_name="skill-slide-critic"
operation="research"

operation="research" (not "slides_critique") is required here: update-task-status.sh's target_status vocabulary has no slides_critique value, so this skill maps onto the plain research operation (this skill is invoked from /research on workflow_type: "slides_critique" tasks). The marker's operation field now reads "research" rather than "slides_critique".

---

Stage 3: Prepare Delegation Context

Discover materials to review (if not provided via materials_to_review):

# Auto-discover materials in task directory
report_files=$(ls -1 "${task_dir}/reports/"*.md 2>/dev/null)
plan_files=$(ls -1 "${task_dir}/plans/"*.md 2>/dev/null)

# Check for assembled slides
talk_dir="talks/${task_number}_${project_name}"
slide_files=$(ls -1 "${talk_dir}/slides.md" 2>/dev/null)

Build the materials array from discovered files, or use the materials_to_review parameter if provided.

Delegation context:

{
  "session_id": "{session_id}",
  "delegation_depth": 1,
  "delegation_path": ["orchestrator", "critique", "skill-slide-critic", "slide-critic-agent"],
  "timeout": 3600,
  "task_context": {
    "task_number": N,
    "task_name": "{project_name}",
    "description": "{description}",
    "task_type": "present:slides"
  },
  "workflow_type": "slides_critique",
  "forcing_data": {
    "talk_type": "{talk_type}",
    "materials_to_review": ["{discovered or provided paths}"],
    "focus_categories": "{focus_categories or omitted}",
    "audience_context": "{audience_context or omitted}"
  },
  "metadata_file_path": "specs/{NNN}_{SLUG}/.return-meta.json"
}

---

Stage 4: Invoke Subagent

CRITICAL: Use the Agent tool to spawn the slide-critic-agent. Do NOT use Skill(...).

Tool: Agent (NOT Skill, NOT Plan)
Parameters:
  - subagent_type: "slide-critic-agent"
  - prompt: [Include full delegation context from Stage 3]
  - description: "Critique presentation materials for task {N}"

The slide-critic-agent will:

1. Load the critique rubric and priority matrix
2. Read all review materials
3. Evaluate against 6 rubric categories (or focus_categories)
4. Write a critique report to specs/{NNN}_{SLUG}/reports/{MM}_slide-critique.md
5. Write metadata to specs/{NNN}_{SLUG}/.return-meta.json

DO NOT use Skill(...) - this will FAIL. Always use Task.

---

Stage 4b: Self-Execution Fallback

CRITICAL: If you performed the work above WITHOUT using the Agent tool (i.e., you read files, wrote artifacts, or updated metadata directly instead of spawning a subagent), you MUST write a .return-meta.json file now before proceeding. Use the schema from return-metadata-file.md with status researched.

If you DID use the Agent tool, skip this stage -- the subagent already wrote the metadata.

---

Interactive Critique Loop (Stages 5-7)

Stage 5: Parse Critique Report

After the subagent returns, read the critique report and extract findings.

1. Read metadata file to get the critique report path:

   metadata_file="${task_dir}/.return-meta.json"
   meta_status=$(jq -r '.status' "$metadata_file")
   critique_report_path=$(jq -r '.artifacts[0].path // ""' "$metadata_file")
   findings_count=$(jq -r '.metadata.findings_count // {}' "$metadata_file")
   

2. If agent failed or no findings: Skip the interactive loop. Proceed directly to postflight with the agent's status.

3. Read the critique report and parse findings using these patterns:

   Per-slide heading: ### Slide N ({slide_type}) Finding line: - [{severity}] {category}: {description} Suggestion line: Suggested improvement: {text} (indented under finding) General heading: ### General (Cross-Cutting) Recommendation tiers: ### Must Fix, ### Should Fix, ### Nice to Fix

4. Build numbered issue list:

   issues = [
     { id: 1, slide: "Slide 3", severity: "Critical", category: "Narrative Flow",
       description: "...", suggestion: "...", tier: "Must Fix" },
     { id: 2, slide: "Slide 7", severity: "Critical", category: "Audience Alignment",
       description: "...", suggestion: "...", tier: "Must Fix" },
     { id: 3, slide: "General", severity: "Major", category: "Timing Balance",
       description: "...", suggestion: "...", tier: "Should Fix" },
     ...
   ]
   

   Assign tier based on severity:

   • Critical -> "Must Fix"
   • Major -> "Should Fix"
   • Minor -> "Nice to Fix"

5. If no findings found in report (agent found no issues): Report success with zero findings. Skip interactive loop.

---

Stage 6: Interactive Critique Loop

Present all findings grouped by severity tier in a single consolidated AskUserQuestion. Collect user decisions and loop until all issues are addressed or user exits.

AskUserQuestion format:

Critique findings for your {talk_type} presentation ({N} total issues):

=== MUST FIX ({count}) ===

 1. [Critical] {category} - {slide}: {description}
    Suggested: {suggestion}

 2. [Critical] {category} - {slide}: {description}
    Suggested: {suggestion}

=== SHOULD FIX ({count}) ===

 3. [Major] {category} - {slide}: {description}
    Suggested: {suggestion}

 4. [Major] {category} - {slide}: {description}
    Suggested: {suggestion}

=== NICE TO FIX ({count}) ===

 5. [Minor] {category} - {slide}: {description}
    Suggested: {suggestion}

---
For each issue, respond with its number and action:
  1: A          (accept as-is)
  3: R          (reject/dismiss)
  5: M add comparison to Smith 2024  (modify the suggestion)

Shortcuts: "accept all", "reject all minor", "done" (accept remaining)

Response parsing grammar:

Parse user response line by line. Each line matches one of:

Pattern	Action	Effect
{N}: A	Accept issue N	Mark as accepted, use original suggestion
{N}: R	Reject issue N	Mark as rejected/dismissed
{N}: M {text}	Modify issue N	Mark as modified, store user's text
accept all	Bulk accept	Accept all unaddressed issues
reject all minor	Bulk reject minor	Reject all Minor severity issues
reject all	Bulk reject	Reject all unaddressed issues
done	Finish	Accept all remaining unaddressed issues

Track decisions per issue:

decisions = {
  1: { action: "accepted", modification: null },
  3: { action: "rejected", modification: null },
  5: { action: "modified", modification: "add comparison to Smith 2024" },
  ...
}

Loop continuation:

After processing responses:

• If all issues are addressed (each has an accepted/rejected/modified decision): proceed to Stage 7
• If unaddressed issues remain AND user did not say "done": re-present ONLY unaddressed issues in a follow-up AskUserQuestion
• Maximum 3 loop iterations to prevent infinite cycles. After 3 iterations, auto-accept all remaining unaddressed issues.

---

Stage 7: Generate Filtered Critique Report

Write the final filtered report incorporating user decisions.

Determine artifact number:

next_num=$(jq -r --argjson num "$task_number" \
  '.active_projects[] | select(.project_number == $num) | .next_artifact_number // 2' \
  specs/state.json)
# Use next available number for the filtered report
filtered_num=$(printf "%02d" "$next_num")

Write to specs/{NNN}_{SLUG}/reports/{MM}_filtered-critique.md:

# Filtered Critique Report: {title}