Train Claude Skills Conversationally for Precise Agents

Context Window Mechanics: Why Skills Beat 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 (name + description only until needed), tools, codebase, and growing conversation. Stay under 70% usage to avoid degradation—hallucinations, confusion, forgetting start at 80%.

Stuffing workflows into claude.md wastes tokens: a 1,000-line file burns ~7,000 tokens per interaction before your query. Skills use progressive disclosure—~50 tokens for name/description, full instructions load only when invoked (e.g., via /use command). Result: 200 tokens total for dozens of skills vs. thousands.

"95% of you do not need a claw.md file unless you have proprietary information that the agent genuinely needs to know in every single turn... You should just be using skills instead."

This shift happened after early failures like medical voice agents hallucinating from prompt overload. Skills deliver concise, on-demand knowledge of your business/workflows without constant bloat.

3-Step Process: Train Like a New Employee

Identify repeatable workflows first (e.g., sponsor research, competitor analysis, report generation). You've done it manually enough to know success markers.

Step 1: Spot the workflow. Pick something rote: researching sponsors (email → company check → rejection criteria), YouTube competitor analysis, analytics reports.

Step 2: Walkthrough training (most skipped, causes generic output). Chat iteratively like onboarding:

• Paste input (e.g., sponsor email).
• Guide: "Check website, Twitter, Trustpilot. If 2+ red flags, reject."
• Correct misses: "Also check Crunchbase funding, Twitter followers, audience fit for AI/business owners."
• Iterate until perfect output. Agent learns your criteria in context—no manual writing.

Common mistake: Writing skills from scratch yields garbage (guessing, no success example). Train first for precision.

Step 3: Extract skill. Post-success: "Review conversation, create skill.md: name, 1-line description, step-by-step instructions, rejection criteria, output format."

Use Claude's /skills creator or prompt explicitly. Outputs YAML-frontmatter Markdown:

name: Sponsor Check
description: Research potential sponsors: funding, social proof, audience fit, verdict.
---
1. Input: Company name/sites.
2. Research: Website → Twitter followers → Trustpilot rating → Crunchbase funding.
3. Criteria: Reject if 2+ fail (low followers, poor reviews, no funding, audience mismatch).
4. Output: Structured verdict (Pass/Reject).

Quality criteria: Mirrors exact successful run, includes your SOPs/business context (digitize them in Google Docs first). Tweak post-creation: "Change output to Google Sheets columns."

Recursive Improvement: Bulletproof via Failure Loops

Skills fail initially—celebrate it. Exposes gaps (wrong API, missed step). Loop:

1. Run skill, note failure.
2. Query: "What happened? Why error?"
3. Fix: Instruct update ("Add X API fallback, handle Y error").
4. Agent self-heals or you guide; /update skill persists.

3-5 iterations yield flawless execution. Example: Speaker's report generator pulls from 8 sources (Notion, YouTube Analytics, Twitter) after loops—no one-shot possible for complexity.

"When skills fail... don't just complain... ask the agent 'Okay, what happened?' ... after 3 to five iterations... your skill will inevitably just become bulletproof."

Agents adapt like Clay.com's 50+ tool fallbacks (one fails → next). Self-annealing: Tries Firecrawl → web search → etc.

Live Demo: Sponsor Checker in Claude Code + Cursor

Setup (prerequisites: Claude Pro/Max, Claude Code extension):

• Cursor/VSCode: Install Claude Code extension, Cmd+Esc to launch.
• Or Claude Co-work (dumbed-down UI).

Demo workflow (hypothetical Jasper/Anthropic emails):

1. Prompt: "Research Jasper/Anthropic: website → Twitter → Trustpilot."
   • Parallel agents launch, hit tool permissions → self-switches to web search/Firecrawl.
   • Output: Credibility summary.
2. Refine: "Add funding (Crunchbase), followers, reviews, audience fit (AI/business owners). Reject on 2+ fails."
   • Bypasses X scrape issues via search; delivers Pass verdicts.
3. Extract: "Create skill.md from this."
   • Generates file instantly; view/edit in IDE.

Test: /use sponsor-check + new input → reuses without reteaching. IDE bonus: File navigation vs. terminal.

"The number one mistake... is writing skills from scratch without ever doing the workflow with the agent first and they're surprised when the output is pretty generic."

Fits broader workflow: Digitize SOPs → train/extract → iterate → deploy across businesses (speaker uses 30 for sponsors, scripts, repurposing).

Assumed level: Familiar with Claude Pro, basic terminal/IDE. No prior skills needed—start simple.

Key Takeaways

• Replace claude.md with skills for 95% cases: Saves 7k+ tokens/turn, loads on-demand.
• Train via 5-10 chat iterations before extraction—mimics employee onboarding for your exact criteria.
• Use Claude Code in Cursor for IDE file management; Pro plan required.
• Loop failures: Diagnose → fix → update; 3-5x for production-ready.
• Digitize SOPs first; include business context (audience, rejection rules) for relevance.
• Test rigorously: Parallel agents, tool fallbacks make it robust.
• Extract with /skills creator or prompt; YAML MD format for easy tweaks.

"You are not writing the skill file just yet... walk the agent through the workflow step by step... only after... tell the agent, 'review everything... create a skill file.'"