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context-engineering

Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.

45

Quality

47%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Fix and improve this skill with Tessl

tessl review fix ./plugins/customaize-agent/skills/context-engineering/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

27%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill contains substantial domain knowledge about context engineering but fails dramatically on conciseness and progressive disclosure. It reads more like a textbook chapter than an actionable skill file, explaining many concepts Claude already understands at length. The multi-agent workflow sections provide the most value with concrete templates, but they're buried in an overwhelming amount of theoretical content that should be drastically condensed or split into referenced files.

Suggestions

Split into a concise SKILL.md overview (under 100 lines) with references to separate files: FUNDAMENTALS.md, DEGRADATION_PATTERNS.md, MULTI_AGENT_WORKFLOWS.md, and OPTIMIZATION.md

Remove explanations of concepts Claude already knows (what attention mechanisms are, what PDFs are, what system prompts do, how tool outputs work) and focus only on novel heuristics and decision frameworks

Convert the theoretical degradation pattern descriptions into concise lookup tables (pattern → symptoms → mitigation) rather than multi-paragraph explanations

Make workflow templates more executable by providing concrete Claude Code command examples or actual script snippets rather than pseudocode patterns with placeholder variables

DimensionReasoningScore

Conciseness

This skill is extremely verbose at ~4500+ lines, extensively explaining concepts Claude already understands (attention mechanisms, what PDFs are, how tool outputs work, what system prompts are). It includes lengthy theoretical explanations of context degradation patterns, attention budgets, and position encoding that Claude inherently knows. Massive amounts of content could be condensed to a fraction of the size.

1 / 3

Actionability

The skill provides some concrete guidance with structured prompt templates and workflow patterns (hallucination detection, lost-in-middle detection, error propagation analysis), but much of it is pseudocode or template-style examples rather than truly executable code. The multi-agent workflow sections contain actionable templates, but the fundamentals and degradation sections are largely descriptive rather than instructive.

2 / 3

Workflow Clarity

The multi-agent workflow sections have clear step-by-step sequences with numbered steps and decision thresholds, which is good. However, validation checkpoints are inconsistent—some workflows have clear decision thresholds (e.g., poisoning risk < 0.1) while others lack concrete verification steps. The fundamentals and degradation pattern sections have no workflow structure at all, just descriptive prose.

2 / 3

Progressive Disclosure

This is a monolithic wall of text with no references to external files despite being thousands of lines long. The content covers at least 4 major topics (fundamentals, degradation patterns, multi-agent workflows, optimization techniques) that should be split into separate files with a concise overview in the main SKILL.md. No bundle files are provided, and no external references are made.

1 / 3

Total

6

/

12

Passed

Description

67%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description has a solid structure with an explicit 'Use when...' clause that clearly delineates trigger conditions, which is its strongest aspect. However, the 'what' portion is more conceptual ('Understand the components, mechanics, and constraints') than action-oriented, and the trigger terms could be expanded to cover more natural user language variations. The skill occupies a somewhat niche domain but could benefit from sharper differentiation.

Suggestions

Replace the abstract 'Understand the components, mechanics, and constraints of context' with concrete actions like 'Analyzes context window usage, manages token budgets, structures system prompts, and optimizes information density in agent prompts.'

Add more natural trigger terms users might say, such as 'context window', 'token limit', 'prompt engineering', 'system prompt', or 'context length' to improve discoverability.

DimensionReasoningScore

Specificity

The description names the domain ('context in agent systems') and some actions ('writing, editing, or optimizing commands, skills, or sub-agents prompts'), but the 'what' portion ('Understand the components, mechanics, and constraints of context') is abstract rather than listing concrete actions like 'analyze token usage, manage context windows, trim conversation history.'

2 / 3

Completeness

The description explicitly answers both 'what' (understand components, mechanics, and constraints of context in agent systems) and 'when' ('Use when writing, editing, or optimizing commands, skills, or sub-agents prompts'), with a clear 'Use when...' clause providing explicit triggers.

3 / 3

Trigger Term Quality

Includes some relevant terms like 'commands', 'skills', 'sub-agents', 'prompts', and 'context', but misses common natural variations users might say such as 'context window', 'token limit', 'system prompt', 'prompt engineering', or 'context management'. The term 'context in agent systems' is somewhat technical.

2 / 3

Distinctiveness Conflict Risk

The focus on 'context in agent systems' provides some specificity, but terms like 'writing, editing, or optimizing commands, skills, or sub-agents prompts' could overlap with general prompt-writing or skill-authoring skills. The niche is somewhat defined but not sharply distinct.

2 / 3

Total

9

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1262 lines); consider splitting into references/ and linking

Warning

Total

10

/

11

Passed

Repository
NeoLabHQ/context-engineering-kit
Reviewed

Table of Contents

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