context-engineering
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques, compression strategies, memory architectures, multi-agent patterns, evaluation, tool design, and project development.
Install with Tessl CLI
npx tessl i github:siviter-xyz/dot-agent --skill context-engineering87
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that follows best practices. It uses third person voice, provides specific concrete actions, includes an explicit 'Use when...' clause with multiple trigger scenarios, and covers a distinct specialized domain. The description effectively communicates both capabilities and usage triggers while maintaining clear differentiation from other potential skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'designing agent architectures', 'debugging context failures', 'optimizing token usage', 'implementing memory systems', 'building multi-agent coordination', 'evaluating agent performance', 'developing LLM-powered pipelines'. Also enumerates specific topics covered. | 3 / 3 |
Completeness | Clearly answers both what ('Master context engineering for AI agent systems') and when ('Use when designing agent architectures, debugging context failures...') with explicit 'Use when' clause containing multiple trigger scenarios. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'agent', 'context', 'token usage', 'memory systems', 'multi-agent', 'LLM', 'pipelines', 'optimization', 'compression'. Good coverage of terms someone working on AI agents would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on 'context engineering for AI agent systems' - a specialized domain. The combination of agent-specific terminology (context failures, token usage, multi-agent coordination) creates distinct triggers unlikely to conflict with general coding or document skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill content excels at conciseness and progressive disclosure, providing a high-signal overview that respects token budget while clearly pointing to detailed references. The main weakness is actionability - while the principles and metrics are valuable, the skill would benefit from concrete code examples or executable commands that demonstrate the concepts in practice.
Suggestions
Add a concrete code example showing context compaction or token measurement in practice
Include a specific workflow example with validation steps for implementing the Four-Bucket Strategy
Provide an executable example of probe-based evaluation mentioned in the Guidelines
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean and efficient. Every section delivers high-signal information without explaining concepts Claude already knows. Bullet points and metrics are precise with no padding or unnecessary context. | 3 / 3 |
Actionability | Provides concrete guidelines and specific metrics (70% threshold, 50-70% reduction targets), but lacks executable code examples or copy-paste ready commands. The Four-Bucket Strategy and Guidelines are actionable concepts but not concrete implementations. | 2 / 3 |
Workflow Clarity | The Guidelines section provides a clear sequence of considerations, and the Four-Bucket Strategy offers a mental framework. However, there are no explicit validation checkpoints or feedback loops for the multi-step processes involved in context engineering. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview and well-signaled one-level-deep references to detailed guidance files. The References section clearly points to specific topics with descriptive labels, making navigation easy. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
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