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Analyze pivot decisions with evidence-based frameworks
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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npx skills add https://github.com/a5c-ai/babysitter --skill pivot-analyzerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SKILL.md を表示中
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SOC 職業分類に基づく
| name | pivot-analyzer |
| description | Analyze pivot decisions with evidence-based frameworks |
| allowed-tools | ["Read","Write","Glob","Grep"] |
| metadata | {"specialization":"entrepreneurship","domain":"business","category":"Strategy","skill-id":"SK-020"} |
| graph | {"domains":["domain:entrepreneurship"],"skillAreas":["skill-area:strategic-analysis","skill-area:business-model-design","skill-area:change-management"],"workflows":["workflow:product-discovery"],"roles":["role:strategic-planner","role:business-analyst","role:product-manager"]} |
The Pivot Analyzer skill provides evidence-based frameworks for analyzing pivot decisions. It enables startups to systematically evaluate whether to pivot or persevere by compiling validation evidence, scoring pivot signals, generating alternatives, and planning pivot execution with documented rationale.
This skill integrates with the following processes:
pivot-decision-framework.js - Primary integration for all phasesproduct-market-fit-assessment.js - PMF signal analysisbusiness-model-canvas.js - Business model iterationmvp-definition-development.js - MVP pivot requirementsSkill: pivot-analyzer
Context: B2C mobile app struggling with retention
Input:
- Current Model: Freemium B2C fitness app
- Evidence:
- DAU declining 10% MoM for 3 months
- 40% of churned users cite "too generic"
- Enterprise inquiries from 5 companies
- High NPS among power users (small segment)
- Runway: 8 months
Output:
- Pivot Signal Score: 7.5/10 (Recommend Pivot)
- Key Signals: Retention trend, market feedback, opportunity signals
- Pivot Alternatives:
1. Customer Segment Pivot: B2C to B2B (enterprise wellness)
2. Zoom-in Pivot: Focus on power user segment
3. Platform Pivot: Become API/SDK for other apps
- Recommended: B2B pivot based on pull signals
- Execution Plan: 12-week pivot roadmap
- Investor Communication: Draft narrative