Build Claude Skills That Know Your Business

Context Window Efficiency: Why Skills Trump Claude.md

Claude's context window acts as working memory, filled by system prompt (fixed, ~10%), Claude.md (loaded every turn, often 1,000+ tokens), skills (only name + description until needed), tools, codebase, and conversation history. Stay under 70% usage to avoid degradation like hallucinations past 80%. Dumping workflows into Claude.md wastes tokens—e.g., 7,000 upfront vs. skills' 200 total, with progressive disclosure loading full instructions only when relevant.

Quote: "Skills, they are effectively how you turn Claude from a chatbot that just guesses into a system that actually knows your business and it knows your workflows."

Traditional Claude.md bloats context on every interaction, leading to generic outputs. Skills enable precise loading: agent decides relevance or uses commands like "use skill". Rule: Skip Claude.md for 95% of cases unless proprietary info (e.g., company methodology) is needed every turn. Early lesson from voice agents for medical clinics: long prompts caused more hallucinations, not fewer.

Three-Step Process to Train Skills Like a New Employee

Identify repeated workflows first—e.g., sponsor research, competitor analysis, analytics reports. Don't write skills from scratch; that's the top mistake yielding generic garbage.

Step 1: Pick Workflow. Choose something manual you've mastered, like sponsor vetting: check website, Twitter, Trustpilot, funding.

Step 2: Interactive Walkthrough (Critical, Often Skipped). Chat step-by-step without pre-writing instructions. Prompt: "I got a sponsor email from Jasper AI. First, tell me about the company." Refine iteratively: "No, check Trustpilot, Twitter, funding via Crunchbase. Reject if 2+ red flags: low followers, poor reviews, irrelevant product." Continue until successful output matching your criteria (e.g., audience relevance for AI/business owners). This builds context like training an employee—show, correct, repeat.

Step 3: Codify into Skill.md. Post-success: "Review this conversation and create skill.md: name, one-line description, step-by-step instructions, rejection criteria, output format." Claude generates YAML-structured file:

name: Sponsor Check
 description: Vets potential sponsors via website, Twitter, Trustpilot, Crunchbase, audience fit.
input: Company name, website, email.
steps:
  - Fetch website summary.
  - Check Twitter followers/engagement.
  - Pull Trustpilot rating.
  - Crunchbase funding.
  - Assess audience relevance (AI/business owners).
rejection_criteria: Reject if 2+ failures (e.g., <10k followers, <4* rating).
output: Pass/Reject verdict with details.

Quote: "Most people, they completely skip step number two, and that's why their skills are just complete garbage. Garbage in, garbage out."

Recursive Improvement: Bulletproof Skills Through Failure

Skills fail initially—celebrate it. Loop: Run skill → Error (e.g., API block on Twitter/X) → Diagnose ("Why error? Wrong tool?") → Fix ("Use web search fallback; update skill") → Rerun. Claude self-heals (e.g., switches from blocked web tool to Firecrawl). After 3-5 iterations, complex skills (e.g., report from 8 sources: Notion, YouTube Analytics, Twitter) run flawlessly. No one-shot perfection; iteration exposes vulnerabilities.

Quote: "When skills fail, you know, don't just complain, you don't throw it away. You ask the agent like, 'Okay, what happened?'"

Live Demo: Sponsor Vetting Skill in Claude Code

Setup: Use Claude Code in Cursor (IDE) or Claude Code Work (web). Install extension, Cmd+Escape to launch. Premium plan required.

1. Hypothetical prompt: "Research Jasper AI/Anthropic sponsors: website → Twitter → Trustpilot."
   • Agents parallelize: Web fetch fails → Self-heals to Firecrawl/web search.
   • Outputs basics; refine: Add Crunchbase funding, followers (>10k?), rating (>4*), relevance.
2. Iteration: Handles X scrape issues via search fallback. Verdicts: Pass both (high funding, relevance).
3. Generate: "Create sponsor-check skill.md from this." → Instant file with full process.

Tweaks: Edit Markdown directly or prompt changes (e.g., Google Sheet output). Digitize SOPs first for best results.

Quote: "95% of you do not need a Claude.md file unless you have proprietary information that the agent genuinely needs to know on every single turn, like a specific company methodology or maybe your credentials. You should just be using skills instead."

Tools shine in resilience: Like Clay.com's 50+ tool fallbacks, Claude tries alternatives automatically.

Setup and Platforms for Skill Building

• Claude Code Work: Web, simplified dashboard.
• Claude Code in IDE (Cursor/VS Code): Terminal integration, file navigation. Extensions auto-handle install/login.

Commands: "skills creator" or descriptive prompts. Explore agents mid-run for debugging.

Key Takeaways

• Replace Claude.md bloat with skills: 200 tokens vs. 7,000, loading on-demand.
• Always interactively train workflows before codifying—skip and get generic fails.
• Use 3 steps: Identify → Walkthrough → Generate skill.md.
• Embrace failures: Run recursive loop (diagnose → fix → update) 3-5x for reliability.
• Start simple: Vet sponsors via website/Crunchbase/Trustpilot/Twitter/relevance.
• Setup: Claude Code in Cursor; premium Anthropic plan.
• Digitize SOPs; prompt refinements iteratively.
• Parallel agents + self-healing (e.g., Firecrawl fallback) boost efficiency.
• Test under 70% context; monitor for bloat in long convos.