synthesize

name	synthesize
description	Content synthesis skill for InsightEngine — gathers, merges, and structures multi-source content into coherent documents. Called by the orchestrator orchestrator agent or directly via synthesize. Handles the core content pipeline: gather → compose → gen-[format] with auto quality review. Default content depth is COMPREHENSIVE (expert-level, rich content). Supports session resume via save_state.py and chained outputs (e.g., Excel data → chart → slide). NOTE: Orchestration (intent classification, routing) is handled by orchestrator agent. This skill focuses purely on content synthesis workflows.
argument-hint	[content request in Vietnamese or English]
version	2
compatibility	{"requires":["Python >= 3.10","All sub-skill dependencies (see setup)"],"tools":["run_in_terminal","read_file","fetch_webpage (for gather)","vscode-websearchforcopilot_webSearch (for gather)"]}

Tổng Hợp — Content Synthesis Skill

Governance: Read and follow .github/RULE.md — it overrides all instructions below.

Role: Pure content synthesis. Orchestration is handled by orchestrator agent. When user says synthesize, orchestrator intercepts, classifies as synthesis, and routes here.

References: references/pipeline-ux.md | references/session-summary.md | references/output-chaining.md | references/auto-escalation.md | references/file-placement-rules.md | references/agent-context-schema.md | references/decision-maps.md | references/final-audit-rollback.md | references/conditional-skill-forge.md | references/public-skill-clone.md | references/agent-mode.md | references/request-analysis.md | references/quality-gates.md | references/workflow-templates/ Agents: .github/agents/auditor.agent.md (quality gate) State: tmp/.session-state.json (written after each step via scripts/save_state.py)

Note: Orchestration agents (strategist, advisory) are called by orchestrator, not by this skill. This skill receives a pre-classified synthesis request and executes the content pipeline.

---

Quality mechanisms: (1) Deep Analysis at Step 1.5 with HARD GATE, (2) Auto Quality Review after each sub-skill (max 2 retries), (3) Comprehensive by default (5000-15000 words). All Vietnamese. Shows plan → waits approval → executes.

File rules (references/file-placement-rules.md): reusable utilities→/scripts, one-time scripts+temp→/tmp, output→/output, input→/input. Validated at pipeline start + after each step.

---

Step 0: Resume Check

Owner: orchestrator agent — when synthesize is called via orchestrator, resume check is already handled. Only run Step 0 if synthesize is called DIRECTLY by the user (not routed through orchestrator).

Run python3 scripts/save_state.py check.

• NO_STATE/COMPLETED → skip to Step 1
• IN_PROGRESS → show summary in Vietnamese:
  • Report: raw_prompt (first 150 chars), session_mode, steps done/pending
  • Ask: "Tiếp tục hay bắt đầu lại?"
  • Tiếp tục: save_state.py resume-plan → restore session_mode/autonomy_mode → skip completed steps
  • Bắt đầu lại: save_state.py archive → Step 1
• No state file or non-resume trigger: skip to Step 1 silently.

---

Step 1: Parse Request

╔══════════════════════════════════════════════════════════════════════╗
║  💾 MANDATORY FIRST ACTION: Save raw prompt to state file NOW       ║
║  EXCEPTION: If called via orchestrator, state is already saved.     ║
║  Check first: python3 scripts/save_state.py check                  ║
║  → If IN_PROGRESS with matching prompt → skip init, use existing   ║
║  → If NO_STATE or COMPLETED → run init below                        ║
╚══════════════════════════════════════════════════════════════════════╝

If no existing state (DIRECT call, not via orchestrator):

python3 scripts/save_state.py init "<user's exact request>" "<detected intent>"
python3 scripts/save_state.py extract-requirements '<structured_json>'  # Phase 13

If state already IN_PROGRESS with matching prompt → skip init, proceed to Step 1 analysis.

1. Extract input sources: file paths, URLs, web-search topics, inline text
2. Determine processing type: synthesis (default) | translation | summary
3. Determine output format: word (default) | excel | slides | pdf | html
4. Detect style: corporate | academic | minimal | dark-modern | creative (see references/pipeline-ux.md)
5. Detect if request implies chained outputs (see references/output-chaining.md)
6. Detect REQUEST_TYPE (details: references/request-analysis.md):
   • research: "tổng hợp về", "báo cáo", topic understanding → gather → compose → gen-<format>
   • data_collection: "tìm tất cả", "liệt kê", specific fields/entities → gather (platform) → gen-excel
   • mixed: both collection + analysis → gather → gen-excel → compose → gen-<format>
7. Extract REQUIRED_FIELDS (data_collection/mixed): scan prompt for fields, auto-add direct_url + source_platform.
8. Visual design routing: poster/cover/cert/banner → design | charts → gen-image | both possible.
9. Research depth: deep if 3+ dimensions, temporal range, comparison, exhaustive data; else standard.
10. Content depth: default comprehensive (5000-15000 words). Only standard if user says "tóm tắt"/"ngắn gọn".

---

Step 1.5: Request Deep Analysis (CRITICAL — DO NOT SKIP)

Supplementary examples: references/request-analysis.md

YOU MUST EXECUTE THIS STEP. Analyze the user's prompt deeply, expand implicit dimensions, and present the analysis to the user. DO NOT proceed to Step 2 without completing this analysis.

1.5.1: Expand Dimensions (by REQUEST_TYPE)

⚠️ Full protocol with examples: references/request-analysis.md — READ IT.

If research: Expand 6 dimensions: CORE_QUESTION, IMPLICIT_SUBTOPICS, CONTEXT_DIMENSIONS, DATA_NEEDS, ANALYTICAL_ANGLES, SCOPE_BOUNDARIES.

If data_collection/mixed: Expand 6 fields: TARGET_ENTITIES, SEARCH_PLATFORMS (platform-specific, NOT generic Google), FILTER_CRITERIA, REQUIRED_FIELDS (+direct_url), SEARCH_QUERIES (site:X.com format), QUANTITY_EXPECTATION.

1.5.2: Present Analysis to User (MANDATORY — HARD GATE)

╔══════════════════════════════════════════════════════════════╗
║  🛑 HARD GATE: YOU MUST STOP HERE AND WAIT FOR USER INPUT  ║
║  Do NOT proceed to Step 2, 3, or 4.                        ║
║  Do NOT skip this step. Do NOT summarize and continue.      ║
║  SHOW the analysis below. WAIT for user response.           ║
║                                                              ║
║  AUTO-BYPASS — skip gate AUTOMATICALLY when ANY is true:    ║
║  • session_mode = silent (frustration signal detected)      ║
║  • Pipeline is RESUMED (autonomy_mode already true)         ║
║  • Called via orchestrator agent AND orchestrator already   ║
║    presented + got user approval at FLOW step 4              ║
║    (autonomy_mode set by orchestrator before delegating)     ║
║                                                              ║
║  CHECK BEFORE STOPPING: read `autonomy_mode` from            ║
║    tmp/.session-state.json. If true → SKIP this gate.        ║
║  → In bypass cases: proceed silently with best assumptions. ║
║  → In gate-active cases: STOP and wait for user response.   ║
╚══════════════════════════════════════════════════════════════╝

Display the analysis output in Vietnamese. The format depends on REQUEST_TYPE:

For research:

🔍 Phân tích yêu cầu:
Yêu cầu gốc: {original_request}
Tôi đề xuất mở rộng phạm vi:
📌 Các khía cạnh: 1. {dim_1} 2. {dim_2} ...
📊 Dữ liệu sẽ thu thập: {data_needs}
🎯 Góc phân tích: {analytical_angles}
⚠️ Không bao gồm: {scope_boundaries}
Đầu ra: {format} kiểu {style}, độ sâu: {content_depth}
👉 Bạn đồng ý? Muốn thêm/bớt khía cạnh nào?

For data_collection/mixed:

🔍 Phân tích yêu cầu thu thập dữ liệu:
Đối tượng: {target_entities} | Tiêu chí lọc: {filters}
📌 Nền tảng sẽ tìm: 1. {platform_1} 2. {platform_2} ...
📊 Thông tin/item: {field_1}, {field_2}, ..., direct_url
🔢 Mục tiêu: ~{quantity} items
{if mixed: "📝 Sau đó sẽ phân tích và tạo {analysis_format}"}
👉 Bạn đồng ý? Muốn thêm/bớt trường nào?

Analysis MUST include: request_type, detected dimensions/fields, planned steps, content_depth. Then STOP. WAIT. Do not generate any further output until user responds.

1.5.3: Handle User Response (ONLY after user replies)

USER_RESPONSE:
  approved: ["ok", "đồng ý", "tiếp tục", "được", "yes"] → SET autonomy_mode=true → Proceed to Step 2
  modified: User adjusts → update analysis, re-present if major changes
  no_response: DO NOT PROCEED. The pipeline is paused until user confirms.

1.5.4: Autonomy Mode (MANDATORY after user approval)

AUTONOMY_MODE:
  # Set when user confirms at Step 1.5. Persists for the entire pipeline run.
  activate_on: User response matching approved signals above
  
  WHEN_ACTIVE:
    - Execute ALL remaining steps (2→7) without stopping or asking
    - Auto-decide ALL technical choices: libraries, query strategy, platform order,
      file format details, retry counts, batch size
    - SUPPRESS all confirmation prompts between steps
    - SUPPRESS "Bạn có muốn tiếp tục?" style questions
    - Only 2 allowed interruptions:
        a. CONTENT ambiguity: scope or field is genuinely unclear from the request
           (max 1 clarifying question — ask it inline, proceed with best assumption if not answered)
           ⚠️ Classification guide: references/content-only-filter.md
        b. TOTAL failure: all retry attempts exhausted and data collection completely failed
    - Show periodic progress updates (non-interactive): "✅ Step 3 done — 45 items collected"
    
  CONTENT_VS_TECHNICAL:
    # Full classification: references/content-only-filter.md
    CONTENT (may ask once): which companies to include, output language, which provinces, data cutoff date
    TECHNICAL (never ask): which library, how many retries, confirm seed generation,
      approve each batch, confirm file format, confirm query strategy, install dependencies,
      step transitions ("Step 1 done, proceed?"), batch size, retry approval
      
  PROGRESS_FORMAT:
    each_step: "⚙️ {step_name}... ✅ done ({summary})"
    data_collection: "🔍 {source}: ✅ {count} items"
    file_generation: "📄 Đang tạo {file_type}..."
    completion: Single delivery summary message (see Step 7)

---

Step 2: Pre-flight Check

1. Run: python3 scripts/check_deps.py --silent
2. If exit 0 → continue to Step 3
3. If exit 1 → respond in Vietnamese: "⚠️ Một số thư viện chưa được cài đặt. Gõ setup để cài đặt tự động." — STOP

---

Step 3: Present Execution Plan

Present the plan in Vietnamese with sources, processing, output format, and steps.

ROUTING: # Choose based on Step 1 parse results
  single_output:    gather → compose → gen-<format>
  translation_only: gather → compose (translation mode)
  chained_output:   gather → compose → gen-excel → gen-image → gen-slide
  search_and_out:   gather (web search) → compose → gen-<format>
  design:           gather → compose → design (poster/cover/certificate/banner)
  data_collection:  gather (platform-specific) → extract → gen-excel → verify
  mixed_collection: gather → extract → gen-excel → compose → gen-<format> → verify

APPROVAL_GATE:
  guided_mode: Present plan → WAIT for user approval before Step 4
  standard_mode: Present plan for info only → proceed immediately (autonomy_mode is active)
  silent_mode: SKIP plan presentation entirely → proceed directly to Step 4

After plan is accepted (or skipped in autonomy/silent mode), save state:

python3 scripts/save_state.py save '{"raw_prompt": "...", "intent_classification": "...", "generated_plan": {}, "step_states": [], "status": "IN_PROGRESS"}'
# Or just update the existing state (already initialized at Step 1):
python3 scripts/save_state.py set-mode standard  # if user just approved

Step 3.5: Print Pipeline Step Trace (MANDATORY)

After user approves, print numbered step list. Update after each step: ✅ {name} — {summary} | ⏭️ {name} — {reason} | ❌ {name} — {error}

---

Step 4: Execute Sub-Skills (with Auto Quality Review Loop)

Update the step trace (Step 3.5) after EVERY sub-skill completes. For chained outputs and intermediate files, see references/output-chaining.md.

CRITICAL: Every step now has an automatic quality review. After each sub-skill completes, the orchestrator reviews the output against quality criteria. If quality is insufficient, the step is re-executed with specific improvement instructions. Maximum 2 retries per step — if quality is still poor after 2 retries, proceed with a warning to the user.

VERIFY-OR-LOOP Protocol (applies to EVERY sub-skill below)

╔══════════════════════════════════════════════════════════════════════════╗
║  🔴 AFTER EVERY SCRIPT/SUB-SKILL: YOU MUST READ THE ACTUAL OUTPUT     ║
║                                                                        ║
║  Script exit code 0 ≠ success. A script can "succeed" but produce:    ║
║  • Empty/thin content (500 words instead of 5000)                      ║
║  • Broken URLs (404, search pages, wrong items)                        ║
║  • Generic text (no names, numbers, dates, specifics)                  ║
║  • Missing sections (3 of 8 headings present)                          ║
║                                                                        ║
║  YOU MUST: read_file the output → CHECK content → LOOP if bad          ║
╚══════════════════════════════════════════════════════════════════════════╝

After EVERY sub-skill completes, execute this sequence:

1. READ the output file with read_file — not just the terminal log
2. COUNT: words, sections, rows, slides (depending on format)
3. SAMPLE: read 3-5 actual data points/paragraphs — are they specific or generic?
4. VERIFY URLs (if any): pick 2-3 URLs → fetch_webpage → is it the right page?
5. JUDGE: does output genuinely satisfy user's request? (not just "file exists")
6. If quality fails → re-run sub-skill with specific fix instructions (max 2 retries)
7. If still fails after 2 retries → report honestly to user, proceed with warning

Minimum quality thresholds:

Sub-skill	Verify	Minimum	Fail action
gather	Read collected content	≥5K chars (standard), ≥15K (deep)	Re-search with expanded queries
gather (DC)	Open 3 URLs with fetch_webpage	URLs are item pages, not search	Re-fetch from platform
compose	Read synthesized text	≥300 words/section, ≥3 data points each	Re-synthesize with depth flag
gen-word	read_file the .docx (via markitdown)	≥1000 words, all sections present	Re-generate
gen-excel	Read output rows + open 2 URLs	Data in cells, URLs work, formulas correct	Re-generate + re-fetch bad URLs
gen-slide	Read slide JSON/content	≥8 slides, each has ≥3 bullet points with data	Re-generate
gen-pdf	Read content	Matches source, Vietnamese renders	Re-generate
gen-html	Read HTML source	All sections, reveal.js works, styles applied	Re-generate

---

4.1: gather (with quality gate)

Execute:

• Input: sources from user request + expanded dimensions from Step 1.5
• If request_type = data_collection or mixed: pass mode: data_collection with:
  • required_fields: list of fields to extract per item
  • search_platforms: platform-specific search targets from Step 1.5
  • filter_criteria: filters (location, experience level, etc.)
  • Thu-thap must search PLATFORM-SPECIFIC (e.g., site:itviec.com) not generic Google
  • Thu-thap must fetch INDIVIDUAL ITEM PAGES, not search result pages
  • Thu-thap must extract structured data fields from each page
  • See references/data-collection-mode.md for detailed protocol
• If research_depth = deep: pass this flag so gather uses the Deep Research Protocol (query decomposition → multi-round search → gap analysis → targeted deep dives). Thu-thap will return content organized by research dimensions with coverage assessment.
• If research_depth = standard: single-query search as usual
• Output: combined Markdown text (with dimension headers if deep research)
• Report: "✅ Thu thập hoàn tất — {N} nguồn, {total_chars} ký tự"
• Save state: python3 scripts/save_state.py update --step gather

⚠️ VERIFY (mandatory): Read the collected content. For data_collection: open 3 URLs with fetch_webpage — are they real item pages? Do titles match? If search/listing pages → re-fetch.

4.2: Analysis loop (ALWAYS — not just deep research)

After gather returns, always analyze the gathered content quality:

• Review each dimension from Step 1.5 analysis against collected data
• If compose identifies critical information gaps:
  • Generate specific follow-up queries targeting the gaps
  • Route back to gather for supplementary search
  • Maximum 2 supplementary rounds (up from 1)
• This loop ensures the synthesis is based on substantive data, not thin scraps

4.3: compose (with quality gate)

Execute:

• Input: Markdown from gather
• Options: enrich: true (always) | include_notes: true (if output = presentation)
• content_depth: pass the detected depth level (standard | comprehensive)
  • Default: comprehensive (produces 5000-15000 words — expert-level depth)
  • Only standard if user explicitly asked for brevity
• Output: structured Markdown content
• Report: "✅ Biên soạn hoàn tất — {sections} phần, {total_words} từ"
• Save state: python3 scripts/save_state.py update --step compose

⚠️ VERIFY (mandatory): Read the synthesized content. Count words per section. Are there specific numbers, names, examples? If any section is < 200 words or purely generic → re-synthesize.

4.3b: Pre-Output URL Validation (data_collection/mixed ONLY — HARD GATE)

Run BEFORE gen-excel generates the output file, NOT after.

python3 scripts/validate_urls.py --urls "url1" "url2" ... --json

URL_VALIDATION_GATE:
  1. Extract all collected direct_url values from gather output
  2. Run validate_urls.py → classify each as DIRECT/SEARCH/LISTING/AMBIGUOUS
  3. For SEARCH or LISTING URLs:
     a. Auto re-fetch: search for specific item on the same platform
     b. Replace invalid URL with valid item page URL
  4. After re-fetch, re-validate remaining URLs
  5. Report: "🔗 URL validation: {valid}/{total} URLs verified as direct links"
  6. IF >50% URLs still invalid after re-fetch:
     ⚠️ STOP — ask user: "Chỉ {X}% URLs là link trực tiếp. Bạn muốn tiếp tục hay tìm lại?"
  7. Flag remaining invalid URLs with ⚠️ in Excel output

4.4: gen-<format> (with quality gate)

Execute:

• Mapping: word → gen-word | excel → gen-excel | slides → gen-slide | pdf → gen-pdf | html → gen-html
• Input: synthesized content from compose
• Output: final file
• Report: "✅ Xuất file hoàn tất — {path} ({size})"
• Save state: python3 scripts/save_state.py update --step gen-<format> --output-file "<path>"

⚠️ VERIFY (mandatory): read_file the output file. For Excel: are rows populated with real data? Open 2 URLs — do pages match? For Word: ≥1000 words? For Slides: ≥8 slides with content? For any format: if output is thin/empty/broken → re-generate. Do NOT move on just because script exited 0.

4.5: gen-image (conditional — if charts requested OR output is slides with data)

• Report: "✅ Tạo {N} biểu đồ hoàn tất"
• Save state: python3 scripts/save_state.py update --step gen-image --output-file "<chart_path>"

4.6: design (conditional — visual design: poster, cover, certificate, banner)

• Input: content + user design intent | Output: PNG/PDF | Save state after completion

4.7: Output Audit — verify (ALWAYS RUN — INTELLIGENCE-DRIVEN)

╔════════════════════════════════════════════════════════════════════╗
║  🔍 INTELLIGENCE AUDIT: READ output, OPEN URLs, COMPARE content  ║
║  Do NOT just run scripts. FETCH URLs with fetch_webpage.          ║
║  COMPARE reported data against what pages actually say.           ║
╚════════════════════════════════════════════════════════════════════╝

Phase 13 note (US-13.2.1): If called via orchestrator, per-step auditor calls are already managed by orchestrator FLOW step 6d (structured per-requirement scoring). This step 4.7 is the FINAL intelligence-driven audit (URL verification + content reasoning). Pass structured_requirements from state when running final check for completeness. NOTE: verify skill ≠ auditor agent — verify does URL fetching + reasoning, auditor does scoring. Both serve different purposes and can run in the same pipeline.

After ALL output files generated:

1. READ actual output content (not just file metadata)
2. OPEN URLs from output using fetch_webpage — verify they are real item pages
3. COMPARE output fields (title, salary, company) against actual page content
4. REASON about whether output genuinely matches user's request
5. For research: verify 5 key claims against sources
6. Phase 13: Call auditor with structured_requirements for per-requirement scoring
   • Pass: all output files, structured_requirements from state
   • Check: ALL requirements met?
   • On fail: targeted fix + re-audit (1 retry, budget permitting)

Inputs: original_request, required_fields, expanded_analysis, output_files. On failure: report with evidence → specific re-fetch instructions → max 1 fix cycle.

---

Error Recovery

1. Retry once — transient errors often resolve on retry
2. Partial delivery — save completed work if retry fails
3. Skip non-critical — gen-image optional; deliver main doc without charts
4. Save state before each step for resume | Report clearly — what failed, options

---

Step 5: Final Delivery (Single Summary Message)

FINAL_DELIVERY:
  # This step produces ONE consolidated summary message at pipeline end.
  # No partial updates here — only the single final message.
  # Templates: references/progress-messages.md → FINAL_DELIVERY section
  
  RULE: Deliver in ONE message. No drip updates after this point.
  
  COMPOSE_FINAL_MESSAGE:
    1. Collect all output files (path + size) from pipeline run
    2. Summarize content metrics (word count, item count, slide count, etc.)
    3. Apply jargon-shield to all text
    4. Format using appropriate template from progress-messages.md
    
  TEMPLATE_SELECTION:
    single_file:           "✅ **Hoàn thành!** 📄 {file_type}: [{name}]({path}) ({size})"
    multiple_files:        "✅ **Hoàn thành! {count} file đã tạo:** ..."
    data_collection:       "✅ **Hoàn thành thu thập!** 📊 [{name}]({path}) — {rows} {entity}, {cols} trường"
    with_quality:          Include quality score if auditor ran
    
  CONTENT_SUMMARY_RULES:
    include:
      - File path(s) as clickable markdown links
      - File size
      - Key metric (row count for Excel, slide count for PPTX, word count for Word)
      - Source count or platform count for research/data_collection
    exclude:
      - Library names
      - Script names
      - Technical decisions made during run
      - Step-by-step details (user doesn't need the recipe)
      
  QUALITY_DISPLAY:
    if_auditor_ran:
      90-100: "⭐⭐⭐ xuất sắc"
      75-89:  "⭐⭐ tốt"
      60-74:  "⭐ chấp nhận được"
    if_no_audit: omit quality line
    
  FOLLOW_UP_HINT:
    show: True (unless silent_mode)
    format: "💡 {relevant_next_action}"
    examples:
      - "💡 Gõ 'cải thiện' để tối ưu thêm, hoặc 'tạo slide từ file này' để tiếp tục."
      - "💡 Gõ 'kiểm tra chất lượng' nếu muốn tôi audit kỹ hơn."
    silent_mode: omit follow_up_hint

  EXAMPLE_OUTPUTS:
    single_word: |
      ✅ **Hoàn thành!**
      📄 Word: [bao-cao-thi-truong-ai.docx](output/bao-cao-thi-truong-ai.docx) (48 KB · 22 trang)
      📊 Nội dung: 8,400 từ từ 5 nguồn
      💡 Gõ 'tạo slide từ file này' nếu muốn tôi tạo thuyết trình.
      
    data_plus_slide: |
      ✅ **Hoàn thành! 2 file đã tạo:**
      📊 Excel: [jobs-fresher-js-hcm.xlsx](output/jobs-fresher-js-hcm.xlsx) (32 KB · 28 jobs)
      🎯 Slide: [phan-tich-viec-lam.pptx](output/phan-tich-viec-lam.pptx) (1.2 MB · 15 slide)
      🔍 Nguồn: ITViec, TopCV, LinkedIn
      
    research_report: |
      ✅ **Hoàn thành!**
      📄 Word: [xu-huong-ai-2026.docx](output/xu-huong-ai-2026.docx) (62 KB · 32 trang)
      📊 Nội dung: 12,500 từ từ 8 nguồn · ⭐⭐ tốt

After delivering the final message, mark pipeline complete:

python3 scripts/save_state.py complete

---

Step 6: Session Summary & View Suggestions

After every completed pipeline run:

1. Append session entry to output/session-summary.md
2. Show how to open the output file(s)

See references/session-summary.md for full format and view suggestion specs.

---

Examples

#	Input	Flow	Output
1	"Tổng hợp 3 file PDF thành báo cáo Word corporate"	Analysis → gather → compose → gen-word	output/bao-cao.docx (20pp)
2	"Search AI trends 2026, slide dark-modern"	Analysis → Expand 5 dims → gather (deep) → compose → gen-slide	output/ai-trends.pptx (22 slides)
3	"Excel sales_data.xlsx → chart → Word"	gather → compose → gen-image → gen-word	report + charts
4	"Tìm jobs fresher JS HCM, Excel + slide phân tích"	type=mixed → gather (platform) → gen-excel → compose → gen-slide → verify	xlsx + pptx

---

What This Skill Does NOT Do

• Does NOT generate content — delegates to sub-skills
• Does NOT install deps — redirects to setup
• Does NOT skip execution plan — always shows plan first