[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FJ979ZPZGTTK9PFWHT3C9Q":3,"skill-same:01M6FJ979ZPZGTTK9PFWHT3C9Q":86},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M6FJ979ZPZGTTK9PFWHT3C9Q","ln-230-story-prioritizer",null,"RICE-scores Stories with market research and generates prioritization table. Use when Stories need business priority ranking for sprint planning.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"ln","ln 230 story prioritizer","Story","when","research","stories","for","with","ranking","sprint","sprint planning","Market","use","business","rice","table","market research","prioritization","planning","2026-11-10 10:12:30",false,"---\nname: ln-230-story-prioritizer\ndescription: \"RICE-scores Stories with market research and generates prioritization table. Use when Stories need business priority ranking for sprint planning.\"\nlicense: MIT\n---\n\n> **Paths:** File paths (`shared\u002F`, `references\u002F`, `..\u002Fln-*`) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If `shared\u002F` is missing, fetch files via WebFetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Flevnikolaevich\u002Fclaude-code-skills\u002Fmaster\u002Fskills\u002F{path}`.\n\n# Story Prioritizer\n\n**Type:** L3 Worker\n**Category:** 2XX Planning\n\nEvaluate Stories using RICE scoring with market research. Generate consolidated prioritization table for Epic.\n\n## Purpose & Scope\n\n- Prioritize Stories AFTER ln-220 creates them\n- Triage all Stories cheaply before doing deep research\n- Research market size and competition only where it changes prioritization confidence\n- Calculate RICE score for each Story\n- Generate prioritization table (P0\u002FP1\u002FP2\u002FP3)\n- Output: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n\n## When to Use\n\n**Use this skill when:**\n- Stories created by ln-220, need business prioritization\n- Planning sprint with limited capacity (which Stories first?)\n- Stakeholder review requires data-driven priorities\n- Evaluating feature ROI before implementation\n\n**Do NOT use when:**\n- Epic has no Stories yet (run ln-220 first)\n- Stories are purely technical (infrastructure, refactoring)\n- Prioritization already exists in docs\u002Fmarket\u002F\n\n---\n\n## Input Parameters\n\n| Parameter | Required | Description | Default |\n|-----------|----------|-------------|---------|\n| epic | Yes | Epic ID or \"Epic N\" format | - |\n| stories | No | Specific Story IDs to prioritize | All in Epic |\n| depth | No | Research depth (quick\u002Fstandard\u002Fdeep) | \"standard\" |\n\n**depth options:**\n- `quick` - 2-3 min\u002FStory, 1 WebSearch per type\n- `standard` - 5-7 min\u002FStory, 2-3 WebSearches per type\n- `deep` - 8-10 min\u002FStory, comprehensive research\n\n---\n\n## Output Structure\n\n```\ndocs\u002Fmarket\u002F[epic-slug]\u002F\n└── prioritization.md    # Consolidated table + RICE details + sources\n```\n\n## Runtime Contract\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fplanning_worker_runtime_contract.md`, `shared\u002Freferences\u002Fcoordinator_summary_contract.md`\n\nRuntime family: `planning-worker-runtime`\n\nIdentifier:\n- `epic-{epicId}`\n\nPhases:\n1. `PHASE_0_CONFIG`\n2. `PHASE_1_DISCOVERY`\n3. `PHASE_2_LOAD_STORY_METADATA`\n4. `PHASE_3_ANALYZE_STORIES`\n5. `PHASE_4_GENERATE_PRIORITIZATION`\n6. `PHASE_5_WRITE_SUMMARY`\n7. `PHASE_6_SELF_CHECK`\n\nSummary contract:\n- `summary_kind=story-prioritization-worker`\n- payload includes `epic_id`, `depth`, `stories_analyzed`, `priority_distribution`, `top_story_ids`, `prioritization_path`, `warnings`\n- managed mode writes to caller-provided `summaryArtifactPath`\n- default managed artifact path pattern: `.hex-skills\u002Fruntime-artifacts\u002Fruns\u002F{parent_run_id}\u002Fstory-prioritization-worker\u002Fln-230--{identifier}.json`\n\n**Table columns (from user requirements):**\n\n| Priority | Customer Problem | Feature | Solution | Rationale | Impact | Market | Sources | Competition |\n|----------|------------------|---------|----------|-----------|--------|--------|---------|-------------|\n| P0 | User pain point | Story title | Technical approach | Why important | Business impact | $XB | [Link] | Blue 1-3 \u002F Red 4-5 |\n\n---\n\n## Inputs\n\n| Input | Required | Source | Description |\n|-------|----------|--------|-------------|\n| `epicId` | Yes | args, kanban, user | Epic to process |\n\n**Resolution:** Epic Resolution Chain.\n**Status filter:** Active (planned\u002Fstarted)\n\n## Tools Config\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fenvironment_state_contract.md`, `shared\u002Freferences\u002Fstorage_mode_detection.md`, `shared\u002Freferences\u002Finput_resolution_pattern.md`\n\nExtract: `task_provider` = Task Management → Provider\n\n## Research Tools\n\n| Tool | Purpose | Example Query |\n|------|---------|---------------|\n| **WebSearch** | Market size, competitors | \"[domain] market size {current_year}\" |\n| **mcp__Ref** | Industry reports | \"[domain] market analysis report\" |\n| **Task provider** | Load Stories | IF linear: list_issues \u002F ELSE: Glob story.md |\n| **Glob** | Check existing | \"docs\u002Fmarket\u002F[epic]\u002F*\" |\n\n---\n\n## Workflow\n\n### Phase 1: Discovery (2 min)\n\n**Objective:** Validate input and prepare context.\n\n**Process:**\n\n1. **Resolve epicId:** Run Epic Resolution Chain per guide.\n\n2. **Load Epic details:**\n   - **IF task_provider == \"linear\":** `get_project(query=epicId)`\n   - **ELSE IF task_provider == \"github\":** `gh issue view {epicId} -R {REPO} --json number,title,body`\n   - **ELSE:** `Read(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fepic.md\")`\n   - Extract: Epic ID, title, description\n\n3. **Auto-discover configuration:**\n   - Read `docs\u002Ftasks\u002Fkanban_board.md` for Team ID\n   - Slugify Epic title for output path\n\n4. **Check existing prioritization:**\n   ```\n   Glob: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n   ```\n   - If exists: Ask \"Update existing or create new?\"\n   - If new: Continue\n\n5. **Create output directory:**\n   ```bash\n   mkdir -p docs\u002Fmarket\u002F[epic-slug]\u002F\n   ```\n\n**Output:** Epic metadata, output path, existing check result\n\n---\n\n### Phase 2: Load Stories Metadata (3 min)\n\n**Objective:** Build Story queue with metadata only and prepare rough scoring inputs for all Stories.\n\n**Process:**\n\n1. **Query Stories from Epic:**\n   **IF task_provider == \"linear\":**\n   ```\n   list_issues(project=Epic.id, label=\"user-story\")\n   ```\n   **ELSE IF task_provider == \"github\":**\n   ```\n   gh api \u002Frepos\u002F{O}\u002F{R}\u002Fissues\u002F{epic_num}\u002Fsub_issues --jq '.[].number'\n   → for each: gh issue view {num} -R {REPO} --json number,title,state,labels\n   → filter: label \"user-story\"\n   ```\n   **ELSE (file mode):**\n   ```\n   Glob(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fstories\u002F*\u002Fstory.md\")\n   ```\n\n2. **Extract metadata only:**\n   - Story ID, title, status\n   - minimal Epic context if available\n   - **DO NOT** load full descriptions yet\n\n3. **Filter Stories:**\n   - Exclude: Done, Cancelled, Archived\n   - Include: Backlog, Todo, In Progress\n\n4. **Build processing queue:**\n   - Order by: existing priority (if any), then by ID\n   - Count: N Stories to process\n\n**Output:** Story queue (ID + title + minimal context), ~50-80 tokens\u002FStory\n\n---\n\n### Phase 3: Two-Pass Story Analysis\n\n**Objective:** Score all Stories cheaply first, then spend deep research only on candidates where it changes the decision.\n\n**Critical:** Keep maximum context to one full Story at a time even during deep research.\n\n#### Pass A: Cheap Triage For All Stories\n\nFor each Story, load only enough detail to estimate:\n- customer problem\n- rough solution shape\n- likely reach\n- likely impact\n- likely effort\n- initial confidence tier\n\n##### Step 3.1: Load Story Description\n\n**IF task_provider == \"linear\":**\n```\nget_issue(id=storyId, includeRelations=false)\n```\n\n**ELSE IF task_provider == \"github\":**\n```\ngh issue view {storyId} -R {REPO} --json number,title,body,state,labels\n```\n\n**ELSE (file mode):**\n```\nRead(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fstories\u002Fus{NNN}-*\u002Fstory.md\")\n```\n\n**Extract from Story:**\n- **Feature:** Story title\n- **Customer Problem:** From \"So that [value]\" + Context section\n- **Solution:** From Technical Notes (implementation approach)\n- **Rationale:** From AC + Success Criteria\n\n##### Step 3.2: Build rough RICE estimate\n\nUse Story + Epic context to assign:\n- rough `Reach`\n- rough `Impact`\n- rough `Effort`\n- initial `Confidence`\n\nMark one of:\n- `full_research_required`\n- `rough_estimate_ok`\n- `borderline_needs_review`\n\n**Send to Pass B only if:**\n- candidate looks P0\u002FP1 on rough score\n- confidence is low\n- Story is near a priority threshold\n- Story has strategic or market-sensitive uncertainty\n\n#### Pass B: Selective Deep Research\n\nOnly for Stories selected in Pass A, run full external research.\n\n##### Step 3.3: Research Market Size\n\n**WebSearch queries (based on depth):**\n```\n\"[customer problem domain] market size TAM {current_year}\"\n\"[feature type] industry market forecast\"\n```\n\n**mcp__Ref query:**\n```\n\"[domain] market analysis Gartner Statista\"\n```\n\n**Extract:**\n- Market size: $XB (with unit: B=Billion, M=Million)\n- Growth rate: X% CAGR\n- Sources: URL + date\n\n**Confidence mapping:**\n- Industry report (Gartner, Statista) → Confidence 0.9-1.0\n- News article → Confidence 0.7-0.8\n- Blog\u002FForum → Confidence 0.5-0.6\n\n##### Step 3.4: Research Competition\n\n**WebSearch queries:**\n```\n\"[feature] competitors alternatives {current_year}\"\n\"[solution approach] market leaders\"\n```\n\n**Count competitors and classify:**\n\n| Competitors Found | Competition Index | Ocean Type |\n|-------------------|-------------------|------------|\n| 0 | 1 | Blue Ocean |\n| 1-2 | 2 | Emerging |\n| 3-5 | 3 | Growing |\n| 6-10 | 4 | Mature |\n| >10 | 5 | Red Ocean |\n\n##### Step 3.5: Calculate final RICE Score\n\n```\nRICE = (Reach x Impact x Confidence) \u002F Effort\n```\n\n**Reach (1-10):** Users affected per quarter\n| Score | Users | Indicators |\n|-------|-------|------------|\n| 1-2 | \u003C500 | Niche, single persona |\n| 3-4 | 500-2K | Department-level |\n| 5-6 | 2K-5K | Organization-wide |\n| 7-8 | 5K-10K | Multi-org |\n| 9-10 | >10K | Platform-wide |\n\n**Impact (0.25-3.0):** Business value\n| Score | Level | Indicators |\n|-------|-------|------------|\n| 0.25 | Minimal | Nice-to-have |\n| 0.5 | Low | QoL improvement |\n| 1.0 | Medium | Efficiency gain |\n| 2.0 | High | Revenue driver |\n| 3.0 | Massive | Strategic differentiator |\n\n**Confidence (0.5-1.0):** Data quality (from Step 3.2)\n\n**Data Confidence Assessment:**\n\nFor each RICE factor, assess data confidence level:\n\n| Confidence | Criteria | Score Modifier |\n|------------|----------|----------------|\n| HIGH | Multiple authoritative sources (Gartner, Statista, SEC filings) | Factor used as-is |\n| MEDIUM | 1-2 sources, mixed quality (blog + report) | Factor ±25% range shown |\n| LOW | No sources, team estimate only | Factor ±50% range shown |\n\n**Output:** Show confidence per factor in prioritization table + RICE range (optimistic\u002Fpessimistic) to make uncertainty explicit.\n\n**Effort (1-10):** Person-months\n| Score | Time | Story Indicators |\n|-------|------|------------------|\n| 1-2 | \u003C2 weeks | 3 AC, simple CRUD |\n| 3-4 | 2-4 weeks | 4 AC, integration |\n| 5-6 | 1-2 months | 5 AC, complex logic |\n| 7-8 | 2-3 months | External dependencies |\n| 9-10 | 3+ months | New infrastructure |\n\n##### Step 3.6: Determine Priority\n\n| Priority | RICE Threshold | Competition Override |\n|----------|----------------|---------------------|\n| P0 (Critical) | >= 30 | OR Competition = 1 (Blue Ocean monopoly) |\n| P1 (High) | >= 15 | OR Competition \u003C= 2 (Emerging market) |\n| P2 (Medium) | >= 5 | - |\n| P3 (Low) | \u003C 5 | Competition = 5 (Red Ocean) forces P3 |\n\n##### Step 3.7: Store and Clear\n\n- Append row to in-memory results table\n- Mark whether row is `full-research` or `rough-estimate`\n- Clear Story description from context\n- Move to next Story in queue\n\n**Output per Story:** Complete row for prioritization table with confidence tier\n\n---\n\n### Phase 4: Generate Prioritization Table (5 min)\n\n**Objective:** Create consolidated markdown output.\n\n**Process:**\n\n1. **Sort results:**\n   - Primary: Priority (P0 → P3)\n   - Secondary: RICE score (descending)\n\n2. **Generate markdown:**\n   - Use template from references\u002Fprioritization_template.md\n   - Fill: Priority Summary, Main Table, RICE Details, Sources\n   - Explicitly show whether each Story used full research or rough estimate\n\n3. **Save file:**\n   ```\n   Write: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n   ```\n\n**Output:** Saved prioritization.md\n\n---\n\n### Phase 5: Summary & Next Steps (1 min)\n\n**Objective:** Display results and recommendations.\n\n**Output format:**\n```\n## Prioritization Complete\n\n**Epic:** [Epic N - Name]\n**Stories analyzed:** X\n**Time elapsed:** Y minutes\n\n### Priority Distribution:\n- P0 (Critical): X Stories - Implement ASAP\n- P1 (High): X Stories - Next sprint\n- P2 (Medium): X Stories - Backlog\n- P3 (Low): X Stories - Consider deferring\n\n### Top 3 Priorities:\n1. [Story Title] - RICE: X, Market: $XB, Competition: Blue\u002FRed\n\n### Saved to:\ndocs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n\n### Next Steps:\n1. Review table with stakeholders\n2. Run ln-300 for P0\u002FP1 Stories first\n3. Consider cutting P3 Stories\n```\n\n---\n\n## Time-Box Constraints\n\n| Depth | Per-Story | Total (10 Stories) |\n|-------|-----------|-------------------|\n| quick | 2-3 min | 20-30 min |\n| standard | 5-7 min | 50-70 min |\n| deep | 8-10 min | 80-100 min |\n\n**Time management rules:**\n- If Story exceeds time budget: keep rough estimate, mark lower confidence\n- If total exceeds budget: reserve deep research only for high-potential or borderline Stories\n- Parallel WebSearch where possible (market + competition)\n\n---\n\n## Token Efficiency\n\n**Loading pattern:**\n- Phase 2: Metadata only (~50 tokens\u002FStory)\n- Phase 3: Full description ONE BY ONE (~3,000-5,000 tokens\u002FStory)\n- After each Story: Clear description, keep only result row (~100 tokens)\n\n**Memory management:**\n- Sequential processing (not parallel)\n- Maximum context: 1 Story description at a time\n- Results accumulate as compact table rows\n\n---\n\n## Integration with Ecosystem\n\n**Position in workflow:**\n```\nln-210 (Scope → Epics)\n     ↓\nln-220 (Epic → Stories)\n     ↓\nln-230 (RICE per Story → prioritization table) ← THIS SKILL\n     ↓\nln-300 (Story → Tasks)\n```\n\n**Dependencies:**\n- WebSearch, mcp__Ref (market research)\n- Task provider: Linear MCP or file mode (load Epic, Stories)\n- Glob, Write, Bash (file operations)\n\n**Downstream usage:**\n- Sprint planning uses P0\u002FP1 to select Stories\n- ln-300 processes Stories in priority order\n- Stakeholders review before implementation\n\nStructured worker output:\n- return the prioritization summary envelope even in standalone mode\n- write the same JSON artifact when `summaryArtifactPath` is provided\n\n---\n\n## Critical Rules\n\n1. **Triage first** - do cheap scoring across all Stories before deep research\n2. **Source all deep-research data** - every Market number needs source + date\n3. **Prefer recent data** - last 2 years, warn if older\n4. **Cross-reference when depth justifies it** - use 2+ sources for market-sensitive Stories\n5. **Time-box strictly** - keep rough estimates when deeper research will not change the decision\n6. **Confidence levels** - mark High\u002FMedium\u002FLow and whether score is rough or full-research\n7. **No speculation** - only sourced claims, note \"[No data]\" gaps\n8. **One Story at a time** - token efficiency critical\n9. **Preserve language** - if user asks in Russian, respond in Russian\n\n---\n\n## Definition of Done\n\n- [ ] Epic validated (Linear or file mode)\n- [ ] All Stories loaded through metadata-first queue\n- [ ] Pass A rough triage completed for all Stories\n- [ ] Deep research limited to high-potential or low-confidence Stories\n- [ ] RICE score calculated for each Story\n- [ ] Competition index assigned (1-5)\n- [ ] Priority assigned (P0\u002FP1\u002FP2\u002FP3)\n- [ ] Confidence tier and research depth visible in output\n- [ ] Table sorted by Priority + RICE\n- [ ] File saved to docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n- [ ] Summary with top priorities and next steps\n- [ ] Structured `story-prioritization-worker` summary returned\n- [ ] Summary artifact written when `summaryArtifactPath` is provided\n- [ ] Total time within budget\n\n---\n\n## Example Usage\n\n**Basic usage:**\n```\nln-230-story-prioritizer epic=\"Epic 7\"\n```\n\n**With parameters:**\n```\nln-230-story-prioritizer epic=\"Epic 7: Translation API\" depth=\"deep\"\n```\n\n**Specific Stories:**\n```\nln-230-story-prioritizer epic=\"Epic 7\" stories=\"US001,US002,US003\"\n```\n\n**Example output (docs\u002Fmarket\u002Ftranslation-api\u002Fprioritization.md):**\n\n| Priority | Customer Problem | Feature | Solution | Rationale | Impact | Market | Sources | Competition |\n|----------|------------------|---------|----------|-----------|--------|--------|---------|-------------|\n| P0 | \"Repeat translations cost GPU\" | Translation Memory | Redis cache, 5ms lookup | 70-90% GPU cost reduction | High | $2B+ | [M&M](link) | 3 |\n| P0 | \"Can't translate PDF\" | PDF Support | PDF parsing + layout | Enterprise blocker | High | $10B+ | [Eden](link) | 5 |\n| P1 | \"Need video subtitles\" | SRT\u002FVTT Support | Timing preservation | Blue Ocean opportunity | Medium | $5.7B | [GMI](link) | 2 |\n\n---\n\n## Phase 6: Meta-Analysis\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fmeta_analysis_protocol.md`\n\nSkill type: `planning-worker`. Run after all phases complete. Output to chat using the `planning-worker` format.\n\n## Reference Files\n\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fenvironment_state_contract.md`\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fstorage_mode_detection.md`\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fresearch_tool_fallback.md`\n\n| File | Purpose |\n|------|---------|\n| [prioritization_template.md](references\u002Fprioritization_template.md) | Output markdown template |\n| [rice_scoring_guide.md](references\u002Frice_scoring_guide.md) | RICE factor scales and examples |\n| [research_queries.md](references\u002Fresearch_queries.md) | WebSearch query templates by domain |\n| [competition_index.md](references\u002Fcompetition_index.md) | Blue\u002FRed Ocean classification rules |\n\n---\n\n**Version:** 2.0.0\n**Last Updated:** 2026-04-05\n","> **Paths:** File paths (`shared\u002F`, `references\u002F`, `..\u002Fln-*`) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If `shared\u002F` is missing, fetch files via WebFetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Flevnikolaevich\u002Fclaude-code-skills\u002Fmaster\u002Fskills\u002F{path}`.\n\n# Story Prioritizer\n\n**Type:** L3 Worker\n**Category:** 2XX Planning\n\nEvaluate Stories using RICE scoring with market research. Generate consolidated prioritization table for Epic.\n\n## Purpose & Scope\n\n- Prioritize Stories AFTER ln-220 creates them\n- Triage all Stories cheaply before doing deep research\n- Research market size and competition only where it changes prioritization confidence\n- Calculate RICE score for each Story\n- Generate prioritization table (P0\u002FP1\u002FP2\u002FP3)\n- Output: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n\n## When to Use\n\n**Use this skill when:**\n- Stories created by ln-220, need business prioritization\n- Planning sprint with limited capacity (which Stories first?)\n- Stakeholder review requires data-driven priorities\n- Evaluating feature ROI before implementation\n\n**Do NOT use when:**\n- Epic has no Stories yet (run ln-220 first)\n- Stories are purely technical (infrastructure, refactoring)\n- Prioritization already exists in docs\u002Fmarket\u002F\n\n---\n\n## Input Parameters\n\n| Parameter | Required | Description | Default |\n|-----------|----------|-------------|---------|\n| epic | Yes | Epic ID or \"Epic N\" format | - |\n| stories | No | Specific Story IDs to prioritize | All in Epic |\n| depth | No | Research depth (quick\u002Fstandard\u002Fdeep) | \"standard\" |\n\n**depth options:**\n- `quick` - 2-3 min\u002FStory, 1 WebSearch per type\n- `standard` - 5-7 min\u002FStory, 2-3 WebSearches per type\n- `deep` - 8-10 min\u002FStory, comprehensive research\n\n---\n\n## Output Structure\n\n```\ndocs\u002Fmarket\u002F[epic-slug]\u002F\n└── prioritization.md    # Consolidated table + RICE details + sources\n```\n\n## Runtime Contract\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fplanning_worker_runtime_contract.md`, `shared\u002Freferences\u002Fcoordinator_summary_contract.md`\n\nRuntime family: `planning-worker-runtime`\n\nIdentifier:\n- `epic-{epicId}`\n\nPhases:\n1. `PHASE_0_CONFIG`\n2. `PHASE_1_DISCOVERY`\n3. `PHASE_2_LOAD_STORY_METADATA`\n4. `PHASE_3_ANALYZE_STORIES`\n5. `PHASE_4_GENERATE_PRIORITIZATION`\n6. `PHASE_5_WRITE_SUMMARY`\n7. `PHASE_6_SELF_CHECK`\n\nSummary contract:\n- `summary_kind=story-prioritization-worker`\n- payload includes `epic_id`, `depth`, `stories_analyzed`, `priority_distribution`, `top_story_ids`, `prioritization_path`, `warnings`\n- managed mode writes to caller-provided `summaryArtifactPath`\n- default managed artifact path pattern: `.hex-skills\u002Fruntime-artifacts\u002Fruns\u002F{parent_run_id}\u002Fstory-prioritization-worker\u002Fln-230--{identifier}.json`\n\n**Table columns (from user requirements):**\n\n| Priority | Customer Problem | Feature | Solution | Rationale | Impact | Market | Sources | Competition |\n|----------|------------------|---------|----------|-----------|--------|--------|---------|-------------|\n| P0 | User pain point | Story title | Technical approach | Why important | Business impact | $XB | [Link] | Blue 1-3 \u002F Red 4-5 |\n\n---\n\n## Inputs\n\n| Input | Required | Source | Description |\n|-------|----------|--------|-------------|\n| `epicId` | Yes | args, kanban, user | Epic to process |\n\n**Resolution:** Epic Resolution Chain.\n**Status filter:** Active (planned\u002Fstarted)\n\n## Tools Config\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fenvironment_state_contract.md`, `shared\u002Freferences\u002Fstorage_mode_detection.md`, `shared\u002Freferences\u002Finput_resolution_pattern.md`\n\nExtract: `task_provider` = Task Management → Provider\n\n## Research Tools\n\n| Tool | Purpose | Example Query |\n|------|---------|---------------|\n| **WebSearch** | Market size, competitors | \"[domain] market size {current_year}\" |\n| **mcp__Ref** | Industry reports | \"[domain] market analysis report\" |\n| **Task provider** | Load Stories | IF linear: list_issues \u002F ELSE: Glob story.md |\n| **Glob** | Check existing | \"docs\u002Fmarket\u002F[epic]\u002F*\" |\n\n---\n\n## Workflow\n\n### Phase 1: Discovery (2 min)\n\n**Objective:** Validate input and prepare context.\n\n**Process:**\n\n1. **Resolve epicId:** Run Epic Resolution Chain per guide.\n\n2. **Load Epic details:**\n   - **IF task_provider == \"linear\":** `get_project(query=epicId)`\n   - **ELSE IF task_provider == \"github\":** `gh issue view {epicId} -R {REPO} --json number,title,body`\n   - **ELSE:** `Read(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fepic.md\")`\n   - Extract: Epic ID, title, description\n\n3. **Auto-discover configuration:**\n   - Read `docs\u002Ftasks\u002Fkanban_board.md` for Team ID\n   - Slugify Epic title for output path\n\n4. **Check existing prioritization:**\n   ```\n   Glob: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n   ```\n   - If exists: Ask \"Update existing or create new?\"\n   - If new: Continue\n\n5. **Create output directory:**\n   ```bash\n   mkdir -p docs\u002Fmarket\u002F[epic-slug]\u002F\n   ```\n\n**Output:** Epic metadata, output path, existing check result\n\n---\n\n### Phase 2: Load Stories Metadata (3 min)\n\n**Objective:** Build Story queue with metadata only and prepare rough scoring inputs for all Stories.\n\n**Process:**\n\n1. **Query Stories from Epic:**\n   **IF task_provider == \"linear\":**\n   ```\n   list_issues(project=Epic.id, label=\"user-story\")\n   ```\n   **ELSE IF task_provider == \"github\":**\n   ```\n   gh api \u002Frepos\u002F{O}\u002F{R}\u002Fissues\u002F{epic_num}\u002Fsub_issues --jq '.[].number'\n   → for each: gh issue view {num} -R {REPO} --json number,title,state,labels\n   → filter: label \"user-story\"\n   ```\n   **ELSE (file mode):**\n   ```\n   Glob(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fstories\u002F*\u002Fstory.md\")\n   ```\n\n2. **Extract metadata only:**\n   - Story ID, title, status\n   - minimal Epic context if available\n   - **DO NOT** load full descriptions yet\n\n3. **Filter Stories:**\n   - Exclude: Done, Cancelled, Archived\n   - Include: Backlog, Todo, In Progress\n\n4. **Build processing queue:**\n   - Order by: existing priority (if any), then by ID\n   - Count: N Stories to process\n\n**Output:** Story queue (ID + title + minimal context), ~50-80 tokens\u002FStory\n\n---\n\n### Phase 3: Two-Pass Story Analysis\n\n**Objective:** Score all Stories cheaply first, then spend deep research only on candidates where it changes the decision.\n\n**Critical:** Keep maximum context to one full Story at a time even during deep research.\n\n#### Pass A: Cheap Triage For All Stories\n\nFor each Story, load only enough detail to estimate:\n- customer problem\n- rough solution shape\n- likely reach\n- likely impact\n- likely effort\n- initial confidence tier\n\n##### Step 3.1: Load Story Description\n\n**IF task_provider == \"linear\":**\n```\nget_issue(id=storyId, includeRelations=false)\n```\n\n**ELSE IF task_provider == \"github\":**\n```\ngh issue view {storyId} -R {REPO} --json number,title,body,state,labels\n```\n\n**ELSE (file mode):**\n```\nRead(\"docs\u002Ftasks\u002Fepics\u002Fepic-{N}-*\u002Fstories\u002Fus{NNN}-*\u002Fstory.md\")\n```\n\n**Extract from Story:**\n- **Feature:** Story title\n- **Customer Problem:** From \"So that [value]\" + Context section\n- **Solution:** From Technical Notes (implementation approach)\n- **Rationale:** From AC + Success Criteria\n\n##### Step 3.2: Build rough RICE estimate\n\nUse Story + Epic context to assign:\n- rough `Reach`\n- rough `Impact`\n- rough `Effort`\n- initial `Confidence`\n\nMark one of:\n- `full_research_required`\n- `rough_estimate_ok`\n- `borderline_needs_review`\n\n**Send to Pass B only if:**\n- candidate looks P0\u002FP1 on rough score\n- confidence is low\n- Story is near a priority threshold\n- Story has strategic or market-sensitive uncertainty\n\n#### Pass B: Selective Deep Research\n\nOnly for Stories selected in Pass A, run full external research.\n\n##### Step 3.3: Research Market Size\n\n**WebSearch queries (based on depth):**\n```\n\"[customer problem domain] market size TAM {current_year}\"\n\"[feature type] industry market forecast\"\n```\n\n**mcp__Ref query:**\n```\n\"[domain] market analysis Gartner Statista\"\n```\n\n**Extract:**\n- Market size: $XB (with unit: B=Billion, M=Million)\n- Growth rate: X% CAGR\n- Sources: URL + date\n\n**Confidence mapping:**\n- Industry report (Gartner, Statista) → Confidence 0.9-1.0\n- News article → Confidence 0.7-0.8\n- Blog\u002FForum → Confidence 0.5-0.6\n\n##### Step 3.4: Research Competition\n\n**WebSearch queries:**\n```\n\"[feature] competitors alternatives {current_year}\"\n\"[solution approach] market leaders\"\n```\n\n**Count competitors and classify:**\n\n| Competitors Found | Competition Index | Ocean Type |\n|-------------------|-------------------|------------|\n| 0 | 1 | Blue Ocean |\n| 1-2 | 2 | Emerging |\n| 3-5 | 3 | Growing |\n| 6-10 | 4 | Mature |\n| >10 | 5 | Red Ocean |\n\n##### Step 3.5: Calculate final RICE Score\n\n```\nRICE = (Reach x Impact x Confidence) \u002F Effort\n```\n\n**Reach (1-10):** Users affected per quarter\n| Score | Users | Indicators |\n|-------|-------|------------|\n| 1-2 | \u003C500 | Niche, single persona |\n| 3-4 | 500-2K | Department-level |\n| 5-6 | 2K-5K | Organization-wide |\n| 7-8 | 5K-10K | Multi-org |\n| 9-10 | >10K | Platform-wide |\n\n**Impact (0.25-3.0):** Business value\n| Score | Level | Indicators |\n|-------|-------|------------|\n| 0.25 | Minimal | Nice-to-have |\n| 0.5 | Low | QoL improvement |\n| 1.0 | Medium | Efficiency gain |\n| 2.0 | High | Revenue driver |\n| 3.0 | Massive | Strategic differentiator |\n\n**Confidence (0.5-1.0):** Data quality (from Step 3.2)\n\n**Data Confidence Assessment:**\n\nFor each RICE factor, assess data confidence level:\n\n| Confidence | Criteria | Score Modifier |\n|------------|----------|----------------|\n| HIGH | Multiple authoritative sources (Gartner, Statista, SEC filings) | Factor used as-is |\n| MEDIUM | 1-2 sources, mixed quality (blog + report) | Factor ±25% range shown |\n| LOW | No sources, team estimate only | Factor ±50% range shown |\n\n**Output:** Show confidence per factor in prioritization table + RICE range (optimistic\u002Fpessimistic) to make uncertainty explicit.\n\n**Effort (1-10):** Person-months\n| Score | Time | Story Indicators |\n|-------|------|------------------|\n| 1-2 | \u003C2 weeks | 3 AC, simple CRUD |\n| 3-4 | 2-4 weeks | 4 AC, integration |\n| 5-6 | 1-2 months | 5 AC, complex logic |\n| 7-8 | 2-3 months | External dependencies |\n| 9-10 | 3+ months | New infrastructure |\n\n##### Step 3.6: Determine Priority\n\n| Priority | RICE Threshold | Competition Override |\n|----------|----------------|---------------------|\n| P0 (Critical) | >= 30 | OR Competition = 1 (Blue Ocean monopoly) |\n| P1 (High) | >= 15 | OR Competition \u003C= 2 (Emerging market) |\n| P2 (Medium) | >= 5 | - |\n| P3 (Low) | \u003C 5 | Competition = 5 (Red Ocean) forces P3 |\n\n##### Step 3.7: Store and Clear\n\n- Append row to in-memory results table\n- Mark whether row is `full-research` or `rough-estimate`\n- Clear Story description from context\n- Move to next Story in queue\n\n**Output per Story:** Complete row for prioritization table with confidence tier\n\n---\n\n### Phase 4: Generate Prioritization Table (5 min)\n\n**Objective:** Create consolidated markdown output.\n\n**Process:**\n\n1. **Sort results:**\n   - Primary: Priority (P0 → P3)\n   - Secondary: RICE score (descending)\n\n2. **Generate markdown:**\n   - Use template from references\u002Fprioritization_template.md\n   - Fill: Priority Summary, Main Table, RICE Details, Sources\n   - Explicitly show whether each Story used full research or rough estimate\n\n3. **Save file:**\n   ```\n   Write: docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n   ```\n\n**Output:** Saved prioritization.md\n\n---\n\n### Phase 5: Summary & Next Steps (1 min)\n\n**Objective:** Display results and recommendations.\n\n**Output format:**\n```\n## Prioritization Complete\n\n**Epic:** [Epic N - Name]\n**Stories analyzed:** X\n**Time elapsed:** Y minutes\n\n### Priority Distribution:\n- P0 (Critical): X Stories - Implement ASAP\n- P1 (High): X Stories - Next sprint\n- P2 (Medium): X Stories - Backlog\n- P3 (Low): X Stories - Consider deferring\n\n### Top 3 Priorities:\n1. [Story Title] - RICE: X, Market: $XB, Competition: Blue\u002FRed\n\n### Saved to:\ndocs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n\n### Next Steps:\n1. Review table with stakeholders\n2. Run ln-300 for P0\u002FP1 Stories first\n3. Consider cutting P3 Stories\n```\n\n---\n\n## Time-Box Constraints\n\n| Depth | Per-Story | Total (10 Stories) |\n|-------|-----------|-------------------|\n| quick | 2-3 min | 20-30 min |\n| standard | 5-7 min | 50-70 min |\n| deep | 8-10 min | 80-100 min |\n\n**Time management rules:**\n- If Story exceeds time budget: keep rough estimate, mark lower confidence\n- If total exceeds budget: reserve deep research only for high-potential or borderline Stories\n- Parallel WebSearch where possible (market + competition)\n\n---\n\n## Token Efficiency\n\n**Loading pattern:**\n- Phase 2: Metadata only (~50 tokens\u002FStory)\n- Phase 3: Full description ONE BY ONE (~3,000-5,000 tokens\u002FStory)\n- After each Story: Clear description, keep only result row (~100 tokens)\n\n**Memory management:**\n- Sequential processing (not parallel)\n- Maximum context: 1 Story description at a time\n- Results accumulate as compact table rows\n\n---\n\n## Integration with Ecosystem\n\n**Position in workflow:**\n```\nln-210 (Scope → Epics)\n     ↓\nln-220 (Epic → Stories)\n     ↓\nln-230 (RICE per Story → prioritization table) ← THIS SKILL\n     ↓\nln-300 (Story → Tasks)\n```\n\n**Dependencies:**\n- WebSearch, mcp__Ref (market research)\n- Task provider: Linear MCP or file mode (load Epic, Stories)\n- Glob, Write, Bash (file operations)\n\n**Downstream usage:**\n- Sprint planning uses P0\u002FP1 to select Stories\n- ln-300 processes Stories in priority order\n- Stakeholders review before implementation\n\nStructured worker output:\n- return the prioritization summary envelope even in standalone mode\n- write the same JSON artifact when `summaryArtifactPath` is provided\n\n---\n\n## Critical Rules\n\n1. **Triage first** - do cheap scoring across all Stories before deep research\n2. **Source all deep-research data** - every Market number needs source + date\n3. **Prefer recent data** - last 2 years, warn if older\n4. **Cross-reference when depth justifies it** - use 2+ sources for market-sensitive Stories\n5. **Time-box strictly** - keep rough estimates when deeper research will not change the decision\n6. **Confidence levels** - mark High\u002FMedium\u002FLow and whether score is rough or full-research\n7. **No speculation** - only sourced claims, note \"[No data]\" gaps\n8. **One Story at a time** - token efficiency critical\n9. **Preserve language** - if user asks in Russian, respond in Russian\n\n---\n\n## Definition of Done\n\n- [ ] Epic validated (Linear or file mode)\n- [ ] All Stories loaded through metadata-first queue\n- [ ] Pass A rough triage completed for all Stories\n- [ ] Deep research limited to high-potential or low-confidence Stories\n- [ ] RICE score calculated for each Story\n- [ ] Competition index assigned (1-5)\n- [ ] Priority assigned (P0\u002FP1\u002FP2\u002FP3)\n- [ ] Confidence tier and research depth visible in output\n- [ ] Table sorted by Priority + RICE\n- [ ] File saved to docs\u002Fmarket\u002F[epic-slug]\u002Fprioritization.md\n- [ ] Summary with top priorities and next steps\n- [ ] Structured `story-prioritization-worker` summary returned\n- [ ] Summary artifact written when `summaryArtifactPath` is provided\n- [ ] Total time within budget\n\n---\n\n## Example Usage\n\n**Basic usage:**\n```\nln-230-story-prioritizer epic=\"Epic 7\"\n```\n\n**With parameters:**\n```\nln-230-story-prioritizer epic=\"Epic 7: Translation API\" depth=\"deep\"\n```\n\n**Specific Stories:**\n```\nln-230-story-prioritizer epic=\"Epic 7\" stories=\"US001,US002,US003\"\n```\n\n**Example output (docs\u002Fmarket\u002Ftranslation-api\u002Fprioritization.md):**\n\n| Priority | Customer Problem | Feature | Solution | Rationale | Impact | Market | Sources | Competition |\n|----------|------------------|---------|----------|-----------|--------|--------|---------|-------------|\n| P0 | \"Repeat translations cost GPU\" | Translation Memory | Redis cache, 5ms lookup | 70-90% GPU cost reduction | High | $2B+ | [M&M](link) | 3 |\n| P0 | \"Can't translate PDF\" | PDF Support | PDF parsing + layout | Enterprise blocker | High | $10B+ | [Eden](link) | 5 |\n| P1 | \"Need video subtitles\" | SRT\u002FVTT Support | Timing preservation | Blue Ocean opportunity | Medium | $5.7B | [GMI](link) | 2 |\n\n---\n\n## Phase 6: Meta-Analysis\n\n**MANDATORY READ:** Load `shared\u002Freferences\u002Fmeta_analysis_protocol.md`\n\nSkill type: `planning-worker`. Run after all phases complete. Output to chat using the `planning-worker` format.\n\n## Reference Files\n\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fenvironment_state_contract.md`\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fstorage_mode_detection.md`\n- **MANDATORY READ:** Load `shared\u002Freferences\u002Fresearch_tool_fallback.md`\n\n| File | Purpose |\n|------|---------|\n| [prioritization_template.md](references\u002Fprioritization_template.md) | Output markdown template |\n| [rice_scoring_guide.md](references\u002Frice_scoring_guide.md) | RICE factor scales and examples |\n| [research_queries.md](references\u002Fresearch_queries.md) | WebSearch query templates by domain |\n| [competition_index.md](references\u002Fcompetition_index.md) | Blue\u002FRed Ocean classification rules |\n\n---\n\n**Version:** 2.0.0\n**Last Updated:** 2026-04-05\n",{"name":5,"license":14,"description":7},"main","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Flevn-ln-230-story-prioritizer",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-11 14:20:43",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add boisenoise\u002Fskills-collections --skill ln-230-story-prioritizer",{"tool":60,"command":61},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill ln-230-story-prioritizer",{"tool":63,"command":64},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill ln-230-story-prioritizer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002FSKILL.md",true,[68,74,78,82],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fcompetition_index.md","REFERENCE","参考文档",6073,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002Freferences\u002Fcompetition_index.md",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fprioritization_template.md",4016,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002Freferences\u002Fprioritization_template.md",{"path":79,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fresearch_queries.md",7348,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002Freferences\u002Fresearch_queries.md",{"path":83,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Frice_scoring_guide.md",6784,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Flevn-ln-230-story-prioritizer\u002Freferences\u002Frice_scoring_guide.md",[87,116,140,163,182,204],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":92,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":42},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,26,115],"web","access","web access","official","development","browser","search","data","Tools","read","fetch","only","youtube","free 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budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[118,127,128,129,99,100,130,104,131,132,133,134,35,135,136,111,137,30,138,139,115],"MCP","cli","github","license","Budget","credentials","team","catalog","call","discover","external tools","scoped","blob",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":144,"sourceHtmlUrl":145,"categories":146,"keywords":152,"publishedAt":42},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":150,"taxonomyTypeDesc":151},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[142,129,99,130,153,154,155,36,156,157,111,31,158,159,30,29,160,161,162,139,118,115],"https","email","enrichment","evidence","phone","post","job description","person","candidate","reason",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":170,"publishedAt":42},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[171,172,173,129,99,130,174,175,153,176,131,177,178,179,156,111,180,181,139,118,115],"growth","rea","rea 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ugc","create","Video","Captions","ads","short","hooks","head","style","niche","trending","talking-head","approve",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":91,"licenseFileUrl":208,"sourceHtmlUrl":209,"categories":210,"keywords":211,"publishedAt":42},"01M6WN10ARAA63Y8K1YNGXJ2RM","lead-signals","Find and qualify public buying signals through Treg, rank fitting business accounts and optionally enrich authorized business contact 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