claude-101-mastery

name	claude-101-mastery
description	Provides optimal workflows for 24 use cases (writing, analysis, coding, business) powered by the claude-101 MCP server. Calls the matching MCP tool to obtain structured methodology and computed metrics, then produces output grounded in real data.
allowed-tools	["mcp__claude-101__draft_email","mcp__claude-101__draft_blog_post","mcp__claude-101__parse_meeting_notes","mcp__claude-101__format_social_content","mcp__claude-101__scaffold_tech_doc","mcp__claude-101__structure_story","mcp__claude-101__analyze_data","mcp__claude-101__summarize_document","mcp__claude-101__build_comparison_matrix","mcp__claude-101__analyze_survey","mcp__claude-101__analyze_financials","mcp__claude-101__review_legal_document","mcp__claude-101__scaffold_code","mcp__claude-101__analyze_code","mcp__claude-101__process_sql","mcp__claude-101__scaffold_api_doc","mcp__claude-101__generate_test_cases","mcp__claude-101__create_adr","mcp__claude-101__plan_project","mcp__claude-101__prepare_interview","mcp__claude-101__scaffold_proposal","mcp__claude-101__build_support_response","mcp__claude-101__scaffold_prd","mcp__claude-101__evaluate_decision","Read"]

Claude 101 Mastery

This skill provides the optimal workflow for 24 use cases powered by the claude-101 MCP server. Each use case follows the same pattern: call the MCP tool first to get structured data and computation, then use that data to produce a superior final output.

The claude-101 MCP server provides 27 tools (3 meta + 24 use-case tools) that perform real local computation — statistics, code analysis, SQL parsing, financial math, text analysis, and scoring — and return structured JSON.

Core Principle

Always call the MCP tool FIRST before writing the final output. The tool provides two things that enhance the quality of the response:

1. Methodology — structured frameworks, best practices, section templates, and checklists that guide the approach to each use case. Following these frameworks ensures a professional-grade result that covers all standard elements.

2. Computation — precise metrics (scores, rankings, statistics, parsed data) that the user cannot get from natural language generation alone. These numbers ground the response in verifiable data.

Never skip the tool call. Never attempt to compute statistics, parse structured data, or calculate financial metrics manually. The tool handles computation; the final writing and reasoning come from Claude.

Workflow Pattern

For every use case, follow these five steps:

1. Identify the matching MCP tool from the mapping table below
2. Call the tool with the user's input as parameters
3. Read every field in the result — each exists for a reason
4. Produce the final output informed by both the methodology and the computed data
5. Present key metrics (scores, rankings, statistics) explicitly to the user

When the tool returns both a template/scaffold AND computed analysis, use the template as a structural guide (do not copy it verbatim) and weave the computed metrics into the final output. The combination of professional structure and precise data is what makes the output superior.

Use Case to Tool Mapping

Writing and Communication

User Intent	MCP Tool	Key Output Fields
Write or draft email	draft_email	text_analysis (formality, readability, tone), tone_guide, pre_send_checklist
Plan blog post or article	draft_blog_post	outline (section word targets), seo_fields, topic_analysis, readability_target, heading_validation, content_gaps
Organize meeting notes	parse_meeting_notes	attendees, action_items (owner + deadline), decisions, topics_discussed, metrics
Create social media post	format_social_content	within_limit, chunks (if over limit), hashtags, engagement_signals (question, CTA, hook strength)
Write technical documentation	scaffold_tech_doc	template, sections (required vs optional), best_practices, effort_estimate, optional analysis (code structure, completeness)
Structure a story	structure_story	beats (word targets + tension levels), tension_curve, character_arc_template, genre_conventions, optional text_analysis (pacing, dialogue ratio, scene transitions)

Analysis and Research

User Intent	MCP Tool	Key Output Fields
Analyze CSV or JSON data	analyze_data	columns (per-column stats), correlations (Pearson r + strength), outliers
Summarize a document	summarize_document	key_sentences (scored), flesch_score, flesch_grade, keywords, reading_time_minutes
Compare options or competitors	build_comparison_matrix	rankings (weighted scores), winner (option + margin), sensitivity_framework
Analyze survey results	analyze_survey	questions (per-question mean, median, distribution), nps (promoter/passive/detractor), overall_satisfaction
Review financial data	analyze_financials	margins (gross, operating, net), growth_rates, burn_rate, cash_runway, summary
Review legal document	review_legal_document	clauses_found (type + risk level + snippet), missing_clauses, complexity_score

Coding and Technical

User Intent	MCP Tool	Key Output Fields
Generate code scaffold	scaffold_code	code, description_matched (CRUD/API/auth pattern), naming_convention, notes
Review code quality	analyze_code	complexity (grade, cyclomatic, nesting), issues (type + severity + line), metrics (lines, comments)
Process or optimize SQL	process_sql	result (formatted/explained), tables, columns, warnings, performance_hints
Generate API documentation	scaffold_api_doc	document (OpenAPI/Markdown), consistency (score + issues), example_bodies, optional code_analysis (routes, auth)
Generate test cases	generate_test_cases	test_cases (happy/edge/boundary), coverage_analysis, code (test file scaffold)
Make architecture decision	create_adr	trade_off_matrix (per-option ratings from tech knowledge base), adr (status, pros/cons), markdown

Business and Productivity

User Intent	MCP Tool	Key Output Fields
Plan a project	plan_project	phases (hours + tasks), milestones, critical_path, risks, total_hours, resource_allocation
Prepare for interview	prepare_interview	preparation.questions (with STAR templates), difficulty_distribution, per_question_time, optional job_analysis, optional star_validation
Write a proposal	scaffold_proposal	sections (with word targets), persuasion_framework (AIDA), objection_templates, aida_coverage, optional argument_analysis, optional roi_analysis
Handle customer support	build_support_response	issue_classification (category, severity, sentiment), escalation_risk (score + level), resolution_estimate, customer_effort, optional response_quality
Create a PRD	scaffold_prd	user_stories, prioritization (MoSCoW), success_metrics, requirements_analysis (completeness, story quality, dependencies)
Make a decision	evaluate_decision	rankings (weighted scores), winner (option + margin), sensitivity_analysis (weight change needed to flip)

Per-Tool Workflow Details

Before executing any use case, load the relevant reference file for that domain. Each file contains the exact tool call parameters, field-by-field workflow, and output instructions:

• For writing and communication tools (email, blog, meeting notes, social, tech doc, story): read references/writing-workflows.md
• For analysis and research tools (data, summary, comparison, survey, financial, legal): read references/analysis-workflows.md
• For coding and technical tools (codegen, review, SQL, API doc, test gen, ADR): read references/coding-workflows.md
• For business and productivity tools (planning, interview, proposal, support, PRD, decision): read references/business-workflows.md

Each reference file provides the exact workflow for each tool: what to call, which parameters to pass, which result fields to use, and how to produce the final output.

Critical Rules

1. Always call the tool first — do not attempt computation (statistics, scoring, parsing) manually. The tool provides precise results.

2. Use every returned field — each field in the tool result guides a specific aspect of the output. Ignoring fields means missing part of the methodology or computation.

3. Never copy templates verbatim — use scaffold sections and templates as structural guides. Write original, contextual content that fits the user's specific situation.

4. Present computed metrics explicitly — share scores, rankings, percentages, and statistics with the user. These are the unique value the tool provides. For example, state "Formality score: 73.5" or "Vue leads with 8.1 points (margin: 0.2)".

5. Flag issues the tool detects — if the tool finds problems (missing legal clauses, low completeness scores, weak argument strength, high escalation risk), proactively tell the user and explain the implications.

6. Add reasoning and recommendations — the tool provides data; add interpretation, context, and actionable recommendations. Explain what the numbers mean and what the user should do about them.

7. Use optional parameters when available — many tools accept optional input (existing text, code, job description, draft response, investment amounts) that unlocks additional analysis. Ask the user for this input when relevant.

8. Always remind about limitations — for legal review, always note "have a lawyer review the final analysis." For financial analysis, note these are computed from the provided data only.