Context engineering
Skill BuilderCed/agent-skills/skills/meta/context-engineering
31 cross-platform AI agent skills for regulated industries & underserved markets. EU compliance (AI Act, NIS2, DORA, GDPR), French professional (accounting, tax, notary, real estate), security audit, agent evaluation, Africa mobile money, offline-first.
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Manage AI agent context effectively — what to include, what to exclude, compression strategies, and context hierarchy for optimal performance.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
3.7 KB, as published. Nobody here has run it
Context Engineering
Based on ETH Zurich research: overly detailed instructions reduce task success by 3%, increase token cost by 20%, and add 2-4 reasoning steps.
When to Use
- Writing SKILL.md, AGENTS.md, or system prompts
- Debugging poor agent performance
- Optimizing token costs
- Designing multi-agent workflows
- Reducing context window pressure
Context Hierarchy (5 Levels)
Most persistent → most transient:
| Level | Content | Persistence | Example |
|---|---|---|---|
| 1. Rules | Project-wide standards | Always loaded | CLAUDE.md, AGENTS.md |
| 2. Spec | Feature/session scope | Per feature | PRD, architecture docs |
| 3. Source | Per task | Per task | Relevant source files |
| 4. Errors | Per iteration | Per attempt | Test failures, stack traces |
| 5. History | Accumulates | Session | Conversation history |
Principle: Levels 1-2 are curated (high leverage). Levels 3-5 are per-call (keep minimal).
What to Include
Include ONLY what the agent cannot discover independently:
- Non-obvious conventions ("we use snake_case for DB columns")
- Project-specific constraints ("never modify the auth module")
- Architectural decisions not in code ("we chose Drizzle over Prisma because...")
- External dependencies not discoverable ("deploy via internal CI, not GitHub Actions")
What NOT to Include
The agent can discover these itself — including them wastes tokens:
- Tech stack (visible in package.json / requirements.txt)
- File structure (visible via ls / find)
- Key files (visible via search)
- Build commands (visible in scripts / Makefile)
- Standard patterns (the model already knows React, Express, etc.)
Sizing Guidelines
| Context Type | Max Size | Rationale |
|---|---|---|
| AGENTS.md | 500-1000 tokens | ETH Zurich: more = worse |
| SKILL.md (core) | 1000-2500 tokens | Balance detail vs overhead |
| references/ per skill | 500-1000 tokens | Support data, not duplicate |
| System prompt total | < 5K tokens | Beyond this: diminishing returns |
Compression Strategies
- Remove examples when the pattern is clear — one example > three redundant ones
- Use tables over prose — 50% fewer tokens for structured info
- Remove "obvious" instructions — "write clean code" is noise
- Use references for static data — move schemas/checklists to files
- Lazy-load context — only load what's needed for current task
Anti-Patterns
| Anti-Pattern | Problem | Fix |
|---|---|---|
| "Always be thorough" | Forces effort=high, +35% tokens | Remove — model handles this |
| "Think step by step" | Redundant with adaptive thinking | Remove on modern models |
| Repeating the same rule 3x | Token waste, no benefit | State once, clearly |
| Including full API docs | Context overflow | Link to docs, summarize key parts |
| "You are a helpful assistant" | Generic, no value | Use specific task context |
What This Skill Does NOT Do
- Does not manage conversation memory (different problem)
- Does not optimize the model itself (skill ≠ fine-tuning)
- Does not handle multi-agent coordination (orchestration concern)