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context-manager

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.

24

Quality

14%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./plugins/antigravity-awesome-skills-claude/skills/context-manager/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

7%Scale 1-3

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

This skill is essentially a persona/role description masquerading as a skill file. It exhaustively catalogs domains of expertise (vector databases, knowledge graphs, RAG, multi-agent systems) without providing any concrete, actionable guidance for any of them. The content would need to be fundamentally restructured from abstract capability lists into specific, executable instructions with real code examples, tool commands, and validation steps.

Suggestions

Replace the extensive capability bullet lists with 2-3 concrete, executable workflows (e.g., a step-by-step RAG implementation with actual code for chunking, embedding, and retrieval).

Add real code examples for at least the most common tasks: vector database setup, knowledge graph queries, context window optimization calculations, and RAG pipeline implementation.

Remove the 'Behavioral Traits', 'Knowledge Base', and 'Example Interactions' sections entirely—they describe a persona rather than teach a skill, and Claude doesn't need to be told to have 'systems thinking' or 'quality-first mindset'.

Move the detailed capability taxonomy into a reference file and keep SKILL.md focused on a concise quick-start with the top 3-5 most common operations and clear validation checkpoints.

DimensionReasoningScore

Conciseness

Extremely verbose with extensive lists of capabilities, behavioral traits, and knowledge areas that Claude already knows. The content reads like a persona description/resume rather than actionable instructions. Most of the 150+ lines are taxonomic bullet lists that add no operational value.

1 / 3

Actionability

No concrete code, commands, schemas, or executable examples anywhere. The entire skill is abstract descriptions ('Dynamic context assembly and intelligent information retrieval') and vague directives ('Apply relevant best practices and validate outcomes'). The 'Example Interactions' section lists prompts but provides no actual solutions or implementation patterns.

1 / 3

Workflow Clarity

The 'Response Approach' section lists 10 high-level steps but they are generic platitudes ('Analyze context requirements', 'Optimize performance') with no validation checkpoints, no concrete tools, no error recovery, and no specificity about what to actually do at each step. No workflow for any specific task is defined.

1 / 3

Progressive Disclosure

There is one reference to 'resources/implementation-playbook.md' for detailed examples, which is a reasonable progressive disclosure pattern. However, no bundle files are provided to verify this reference exists, and the massive inline content (capabilities lists, behavioral traits, knowledge base) should have been split into separate reference files rather than bloating the main skill.

2 / 3

Total

5

/

12

Passed

Description

22%Scale 1-3

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 description reads like a résumé headline rather than a functional skill description. It is heavy on buzzwords ('elite', 'mastering', 'intelligent') and light on concrete actions and trigger guidance. It fails to tell Claude what specific tasks this skill performs or when it should be selected.

Suggestions

Replace vague buzzwords with concrete actions, e.g., 'Builds and queries vector databases, constructs knowledge graphs, manages context windows for LLM applications.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about RAG pipelines, embedding storage, context window optimization, or knowledge graph construction.'

Remove self-promotional language like 'Elite' and 'mastering' — use third-person functional descriptions of capabilities instead.

DimensionReasoningScore

Specificity

The description uses vague, buzzword-heavy language like 'elite AI context engineering specialist' and 'mastering dynamic context management' without listing any concrete actions. No specific operations (e.g., 'build knowledge graphs', 'query vector databases') are described.

1 / 3

Completeness

The description vaguely addresses 'what' through buzzwords but provides no 'when' guidance whatsoever — there is no 'Use when...' clause or any explicit trigger guidance for Claude to know when to select this skill.

1 / 3

Trigger Term Quality

It includes some relevant technical keywords like 'vector databases', 'knowledge graphs', and 'memory systems' that users might mention, but these are buried in fluff and missing common natural variations or user-facing terms.

2 / 3

Distinctiveness Conflict Risk

Terms like 'vector databases' and 'knowledge graphs' provide some distinctiveness, but 'context management' and 'memory systems' are broad enough to overlap with many AI-related skills.

2 / 3

Total

6

/

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.

Validation — 10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
popey/claude-code-skills
Reviewed

Table of Contents

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