git clone https://github.com/affaan-m/ECC.git--- name: blueprint description: >- Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it". metadata: origin: community --- # Blueprint — Construction Plan Generator Turn a one-line objective into a step-by-step construction plan that any coding agent can execute cold. ## When to Use - Breaking a large feature into multiple PRs with clear dependency order - Planning a refactor or migration that spans multiple sessions - Coordinating parallel workstreams across sub-agents - Any task where context loss between sessions would cause rework **Do not use** for tasks completable in a single PR, fewer than 3 tool calls, or when the user says "just do it." ## How It Works Blueprint runs a 5-phase pipeline: 1. **Research** — Pre-flight checks (git, gh auth, remote, default branch), then reads project structure, existing plans, and memory files to gather context. 2. **Design** — Breaks the objective into one-PR-sized steps (3–12 typical). Assigns dependency edges, parallel/serial ordering, model tier (strongest vs default), and rollback strategy per step. 3. **Draft** — Writes a self-contained Markdown plan file to `plans/`. Every step includes a context brief, task list, verification commands, and exit criteria — so a fresh agent can execute any step without reading prior steps. 4. **Review** — Delegates adversarial review to a strongest-model sub-agent (e.g., Opus) against a checklist and anti-pattern catalog. Fixes all critical findings before finalizing. 5. **Register** — Saves the plan, updates memory index, and presents the step count and parallelism summary to the user. Blueprint detects git/gh availability automatically. With git + GitHub CLI, it generates full branch/PR/CI workflow plans. Without them, it switches to direct mode (edit-in-place, no branches). ## Examples ### Basic usage ``` /blueprint myapp "migrate database to PostgreSQL" ``` Produces `plans/myapp-migrate-database-to-postgresql.md` with steps like: - Step 1: Add PostgreSQL driver and connection config - Step 2: Create migration scripts for each table - Step 3: Update repository layer to use new driver - Step 4: Add integration tests against PostgreSQL - Step 5: Remove old database code and config ### Multi-agent project ``` /blueprint chatbot "extract LLM providers into a plugin system" ``` Produces a plan with parallel steps where possible (e.g., "implement Anthropic plugin" and "implement OpenAI plugin" run in parallel after the plugin interface step is done), model tier assignments (strongest for the interface design step, default for implementation), and invariants verified after every step (e.g., "all existing tests pass", "no provider imports in core"). ## Key Features - **Cold-start execution** — Every step includes a self-contained context brief. No prior context needed. - **Adversarial review gate** — Every plan is reviewed by a strongest-model sub-agent against a checklist covering completeness, dependency correctness, and anti-pattern detection. - **Branch/PR/CI workflow** — Built into every step. Degrades gracefully to direct mode when git/gh is absent. - **Parallel step detection** — Dependency graph identifies steps with no shared files or output dependencies. - **Plan mutation protocol** — Steps can be split, inserted, skipped, reordered, or abandoned with formal protocols and audit trail. - **Zero runtime risk** — Pure Markdown skill. The entire repository contains only `.md` files — no hooks, no shell scripts, no executable code, no `package.json`, no build step. Nothing runs on install or invocation beyond Claude Code's native Markdown skill loader. ## Installation This skill ships with Everything Claude Code. No separate installation is needed when ECC is installed. ### Full ECC install If you are working from the ECC repository checkout, verify the skill is present with: ```bash test -f skills/blueprint/SKILL.md ``` To update later, review the ECC diff before updating: ```bash cd /path/to/everything-claude-code git fetch origin main git log --oneline HEAD..origin/main # review new commits before updating git checkout <reviewed-full-sha> # pin to a specific reviewed commit ``` ### Vendored standalone install If you are vendoring only this skill outside the full ECC install, copy the reviewed file from the ECC repository into `~/.claude/skills/blueprint/SKILL.md`. Vendored copies do not have a git remote, so update them by re-copying the file from a reviewed ECC commit rather than running `git pull`. ## Requirements - Claude Code (for `/blueprint` slash command) - Git + GitHub CLI (optional — enables full branch/PR/CI workflow; Blueprint detects absence and auto-switches to direct mode) ## Source Inspired by antbotlab/blueprint — upstream project and reference design.
["1. **Define the Scope:** Replace [PROJECT/INITIATIVE] with your specific goal (e.g., 'migrating to cloud infrastructure' or 'launching a new product line'). Specify [INDUSTRY/TEAM SIZE] (e.g., 'e-commerce startup with 10 employees').","2. **Customize Resources:** Adjust the [REQUIRED RESOURCES] section to match your budget, tools, and team structure. Use real-world pricing for tools (e.g., check AWS or SaaS provider websites).","3. **Validate the Timeline:** Break down the [TIMELINE] into actionable phases. Use Gantt charts (e.g., in ClickUp or Asana) to visualize dependencies. Add buffer time for dependencies (e.g., vendor approvals).","4. **Refine Risks and Metrics:** Tailor the [RISK ASSESSMENT] to your project’s unique challenges (e.g., regulatory hurdles, technical debt). Align [SUCCESS METRICS] with existing KPIs (e.g., OKRs or departmental goals).","5. **Iterate with Stakeholders:** Share the blueprint with team leads or executives for feedback. Use tools like Miro or Notion to collaborate on edits in real time. Finalize the blueprint only after consensus on priorities and constraints."]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/affaan-m/ECC/tree/main/skills/blueprintCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate a step-by-step implementation blueprint for [PROJECT/INITIATIVE]. Include the following sections: 1) [OBJECTIVE] with key deliverables, 2) [REQUIRED RESOURCES] (tools, budget, team roles), 3) [TIMELINE] with milestones, 4) [RISK ASSESSMENT] and mitigation strategies, and 5) [SUCCESS METRICS]. Tailor this for [INDUSTRY/TEAM SIZE] and ensure it aligns with [COMPANY GOALS].
### **AI-Powered Customer Support Automation Blueprint for TechStart Inc.** **Objective:** Reduce average response time to customer inquiries by 60% and increase first-contact resolution rate to 85% within 6 months by implementing an AI-driven support system. **Key Deliverables:** - Deploy a hybrid AI-human chatbot (using Dialogflow + Zendesk integration) for Tier 1 support. - Train the AI model on 50,000 historical support tickets and FAQs. - Implement a real-time sentiment analysis dashboard to flag escalations. - Roll out a knowledge base update system for continuous AI learning. **Required Resources:** - **Tools:** Dialogflow CX ($2,000/month), Zendesk Suite ($1,500/month), AWS SageMaker for model training ($3,000 one-time), sentiment analysis API (MonkeyLearn, $500/month). - **Team:** 1 AI/ML Engineer (full-time), 1 Customer Support Lead (0.5 FTE), 2 Support Agents (0.25 FTE each), 1 Data Analyst (part-time). - **Budget:** $25,000 total (including $5,000 contingency). **Timeline:** - **Week 1-2:** Requirements gathering and data collection (historical tickets, FAQs, product docs). - **Week 3-6:** AI model training and testing (accuracy target: 90% on validation set). - **Week 7-8:** Chatbot integration with Zendesk and live pilot with 10% of customer base. - **Week 9-12:** Full rollout with A/B testing (AI vs. human-only support). - **Month 4-6:** Performance monitoring, model retraining, and team training. **Risk Assessment:** - **Risk 1:** AI misclassifying complex inquiries → Mitigation: Implement clear escalation paths to human agents with a 2-minute SLA for transfers. - **Risk 2:** Customer resistance to AI → Mitigation: Transparent communication about AI capabilities and opt-out options for sensitive issues. - **Risk 3:** Data privacy concerns → Mitigation: Anonymize training data and comply with GDPR/CCPA. **Success Metrics:** - **Primary:** Average response time (target: <2 minutes), first-contact resolution rate (target: 85%). - **Secondary:** Customer satisfaction score (CSAT) >= 4.5/5, agent productivity increase (tickets handled per hour). - **Tertiary:** Cost savings from reduced agent hours (target: $12,000/month). **Next Steps:** 1. Secure stakeholder approval and allocate budget. 2. Assign team roles and set up collaboration tools (Slack, Jira). 3. Begin data collection and model training. 4. Schedule weekly check-ins to track progress against milestones. *Note: This blueprint assumes a mid-sized tech company with 50,000 monthly support tickets. Adjust resources and timelines for smaller/larger teams.*
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