Does your AI keep getting the task wrong?
The problem isn’t the model. It’s context. I organized +1,000 skills so my AI finds the right ones to get the job done.
A prompt is context. A markdown file is context. An MCP server is context. A CLAUDE.md, a .cursorrules, an AGENTS.md, SOUL.md, TOOLS.md - all context. Different names, same idea. They are all context.
The question isn’t which format. It’s how you organize context as it grows.
| Level | Name | Files | What Changes |
|---|---|---|---|
| 1 | Raw Prompt | 0 | You type everything, every time |
| 2 | Instruction File | 1 | AI follows your rules by default |
| 3 | Skills | 10-50 | AI switches behavior per task |
| 4 | Complex Skills | 50-200 | Skills have references, scripts, assets |
| 5 | Discovery Layer | 200+ | AI self-navigates to what it needs |
Everyone starts here. You type your request or paste some context. Next session, you do it again. The AI has no memory of your conventions, your tools, your preferences.
Normal ChatGPT, Claude, Gemini calls fit into this bucket.
Problem: You repeat yourself every session.
Solution: One instruction file that loads automatically.
Your conventions, tools, project structure - written down once. The AI reads it every session without you typing it.
## Workspace
- Code: ~/Projects/code
- Client sites: ~/Projects/clients
- Ad accounts: Google Ads (ID: 123-456), Meta (ID: 789)
## Tools
- gh: GitHub PRs and issues
- gads: Google Ads CLI
- dcli: Credential manager
## Rules
- Never push without approval
- Run tests before committing
- Use 80 10 10 rule to manage ads.
- DNS changes require screenshot of current state first
One file. Covers code, ads, email, DNS. Loads every session. The AI stops being generic.
Problem: Your AGENTS.md is too long. AI starts ignoring parts of it.
Solution: Separate files per domain. Each one focused on a specific job.
Claude Code/Codex has this built in. You put a markdown file in .claude/skills/ and it becomes a skill the AI can load when needed.
.claude/skills/
├── seo-audit/SKILL.md
├── deploy/SKILL.md
├── manage-ads/SKILL.md
├── dns-ops/SKILL.md
├── outbound-sdr/SKILL.md
└── client-onboarding/SKILL.md
Each file: 200-500 lines. Focused on one job. The AI loads only the one it needs.
You say “run an SEO audit” and the AI finds seo-audit/SKILL.md on its own. Or you type /seo-audit to trigger it directly. Either way, it loads that skill and nothing else. No ad rules. No DNS procedures. Just SEO.
Problem: A single skill file isn’t enough for complex workflows. Your SEO audit skill needs data source documentation, analysis templates, QA checklists, validation scripts. One file can’t hold all of that without becoming a wall of text the AI half-reads.
Solution: Skills become folders. A main file plus references, scripts, and assets. The main file tells the AI what else to read.
skills/seo-audit/
├── SKILL.md
├── references/
│ ├── data-source-ga4.md
│ ├── data-source-gsc.md
│ ├── data-source-ahrefs.md
│ └── improvement-protocol.md
├── templates/
│ ├── 01-analytics-baseline.md
│ ├── 07-keyword-cannibalization.md
│ └── ...19 step templates
└── scripts/
├── data-staleness.sh
└── validate-analysis.sh
The main SKILL.md references the supporting files:
## Before Starting
Read `references/data-source-ga4.md` for GA4 access setup.
Read `references/data-source-gsc.md` for Search Console setup.
## Step Execution
For each step, create sub-agent, read the matching template in `templates/`.
The template defines success criteria. Work backward from those.
## After Each Step
Run `scripts/validate-analysis.sh` to check your output.
The AI reads the main file, follows its references, reads only what’s needed for the current step. 20+ files in the folder, touches maybe 8 per task.
Main file orchestrates. References provide depth. Scripts automate checks. The AI follows the chain.
Problem: You now have 50+ skill folders with 200+ files total. The context becomes huge. The AI starts ignores it some rules again.
Solution: A slim AGENTS.md that points to where things live, plus a script that reads file metadata so the AI discovers what’s available without loading everything.
The AGENTS.md at this level is minimal:
## Docs
- SEO: docs/seo/
- Ads: docs/ads/
- Code: docs/code/
- SDR: docs/outbound/
- DNS: docs/dns-ops/
- Clients: docs/clients/
## Discovery
Run `docs-catalog` inside relevant folder to list all available docs with summaries.
No skills loaded at startup. Just pointers.
The discovery script scans the docs/ folder and reads the metadata header of every markdown file and injects into the context. Each doc starts with a standard header:
---
summary: "Run full SEO audit with GA4, GSC, and Ahrefs data"
read_when:
- Running SEO analysis or audit
- Pulling analytics data
---
The script outputs a compact list:
docs/seo/seo-audit.md
read_when: "Run full SEO audit with GA4, GSC, and Ahrefs data"
docs/seo/keyword-research.md
read_when: "Keyword gap analysis and cannibalization detection"
docs/ads/google-ads-optimize.md
read_when: "Optimize Google Ads campaigns, adjust bids, pause underperformers"
docs/outbound/prospecting.md
read_when: "Find and qualify leads matching ICP criteria"
docs/dns/domain-ops.md
read_when: "Domain purchases, DNS changes, redirect setup"
...
The AI reads this index, matches the current task against summaries, picks relevant docs, reads those fully, follows their references. 1,000+ files in the system. The AI touches maybe 10-15 per task. Everything else is ignored.
This scales infinitely. Each doc folder can have sub-docs with their own references. Each layer narrows the context until the AI reads only what it needs for this specific task.
The navigation chain looks like this:
AGENTS.md → "SEO is at docs/seo/"
→ script reads metadata of 12 SEO docs
→ AI picks docs/seo/seo-audit.md
→ that doc references 4 data source docs + 19 templates
→ AI reads only the 2 data sources it needs right now
When even the discovery layer isn’t enough - when you have thousands of docs across dozens of domains - the solutions split into two paths.
Commands that reference hundreds of docs. A /seo-analyze command that already knows which 30 reference files, 19 templates, and 8 scripts to load. The user types one command. The AI doesn’t discover anything - the command pre-loads the right context.
Specialized agents with pre-loaded references. Instead of one AI that navigates everything, you split into agents that each own a domain. An SEO agent with 200 docs already in its context. An ads agent with its own 150. A code agent with its own 300. Each agent is focused. No discovery needed because each one already knows its domain.
At this scale, you’re not managing context anymore. You’re managing teams of AIs, each with their own organized knowledge base.
For most use cases specialized agents are over-engineered, but it works.
The difference between getting a workflow done and “AI doesn’t work” is context.
You start from the AI knowing nothing to navigating 1000s of files and finds what it needs
No databases. No embeddings. No vector search. Only Markdown files, folder structure, and a script that reads headers.
Need help setting this up for your team? DM me ↗
Do you want more? Learn about the 5 stages of AI orchestrartion ↗