Build Claude Skills Right: Avoid Context Bloat, Train via Workflow

Context Windows Limit Agent Performance—Skills Fix Bloat

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; over 80% causes hallucinations, confusion, worse outputs. Common mistake: cramming workflows into Claude.md burns 7,000 tokens per message before querying. Skills use progressive disclosure—53 tokens for name/description, full instructions load only on invocation. Result: 200 tokens total vs. thousands, precise tool use.

"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... You should just be using skills instead."

Trade-off: Skills require upfront training but save tokens long-term, enabling complex workflows without degradation. Early lesson from voice agents for medical clinics: long prompts increased hallucinations, not intelligence.

Train Skills Like a New Employee: 3-Step Process

Identify repeatable workflows first—sponsor research, competitor analysis, analytics reports, outreach. Don't write instructions from scratch; that's why outputs stay generic.

Step 1: Pick workflow. Choose something you've done manually repeatedly, so you know success criteria.

Step 2: Walk agent through interactively (critical, skipped by most). Simulate training: forward sponsor email, say "Check website, Twitter, Trustpilot." Correct iteratively: "No, check Crunchbase funding, Twitter followers; reject if 2+ criteria fail (low funding/followers, bad reviews, irrelevant to AI/business audience)." Back-and-forth builds context-specific understanding. Garbage in, garbage out—pre-walkthrough skills fail because agent lacks your nuances.

Step 3: Codify from success. After perfect run: "Review conversation, create skill.md with name, 1-line description, step-by-step instructions, rejection criteria." Use /skills create command or prompt. Agent maps exact successful process, not guesses.

"Most people completely skip step number two, and that's why their skills are just complete garbage."

Prerequisites: Claude Code (terminal or Work), premium plan ($20+). In Cursor/VS Code: install extension, Cmd+Escape to launch. Assumes basic terminal comfort, AI agent familiarity.

Recursive Loop Makes Skills Bulletproof

Skills fail initially—good. Diagnose: "What happened? Wrong API? Missed step?" Agent self-heals or you fix: "Update skill to handle this." 3-5 iterations expose vulnerabilities. Example: 8-source analytics report now flawless after loops.

No one-shot complex skills. Loop: fail → analyze → update → test. Agents auto-alternative tools (e.g., Firecrawl → web search on permission walls).

"Every time it fails, you have an opportunity to make it much, much better... after maybe about three to five iterations... bulletproof."

Quality criteria: Consistent success on new inputs, handles errors autonomously, matches your exact criteria (e.g., audience relevance).

Hypothetical: Jasper AI/Anthropic emails. Initial prompt: Basic checks yield solid but generic verdict (credible, verify domains). Missing: Your criteria.

Refine: Add Crunchbase funding, Twitter followers (>10k?), Trustpilot (>4 stars), AI/business relevance. Auto-reject on 2+ fails. Agent parallelizes: fetches sites, searches X/Trustpilot/Crunchbase. Handles errors (X access issues → web search). Outputs: Funding details, followers (Jasper 50k+, Anthropic massive), ratings (4.5+), relevance (high), verdict: PASS.

Create skill: "sponsor-check.md"—name: Sponsor Check, desc: "Research sponsors via funding/Twitter/Trustpilot/relevance, auto-reject bad fits." Steps: 1. Fetch sites/Crunchbase. 2. Check followers/reviews. 3. Assess audience fit. 4. Verdict.

Test on new companies: Invoke "Use sponsor-check on new email." Reproducible, token-efficient.

Before: Generic research, no rejection logic. After: Tailored, autonomous.

Setup in Cursor/Claude Code Work

Cursor: New folder/project → Extensions → Claude Code → Install/login → Cmd+Escape. Handles terminal under hood. Claude Code Work: Download, premium required, simplified UI (90-95% capability).

Tools auto-detected: Web fetch/search, Firecrawl (for scrapes). Permissions prompt for safety.

5 Skills Every Business Needs

Sponsor research (as demoed).
Competitor YouTube analysis.
Analytics report generation.
Outreach crafting.
Content repurposing (scripts → 6 platforms).

Start with your repeats; share in communities for refinement.

"If you are using Claude code and you're not building skills, you are missing the single most powerful feature that Anthropic has shipped this year."

Key Takeaways

Ditch Claude.md for skills: Saves 95% tokens, loads precisely.
Step 2 mandatory: Interactive walkthrough before codifying—trains nuances.
Recursive loop: Fail → diagnose → update (3-5x) for reliability.
Invoke skills explicitly or let agent choose; use /skills create post-success.
Test on fresh data; define reject criteria upfront (e.g., 2+ fails).
Setup: Cursor + Claude Code extension for DX; premium plan.
Essential: Sponsor check, competitor analysis, reports, outreach, repurposing.
Under 70% context: Monitor via token counts.
Train like employee: Correct in-context, build to success.
Self-healing: Agents swap tools on errors (Firecrawl → search).