context-manager

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

Quality

35%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./skills/context-manager/SKILL.md

Quality

Content

20%

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

The body is a verbose persona sketch dominated by capability and trait lists rather than executable guidance, with a broken reference and only an abstract, validation-free workflow.

Suggestions

Replace the capability/trait/knowledge-base lists with a concise overview plus concrete, executable examples or specific commands for the core tasks.

Fix or remove the broken reference to resources/implementation-playbook.md, and split detailed material into real one-level-deep reference files.

Tighten the 'Response Approach' into a concrete sequenced workflow with explicit validation checkpoints for risky operations like batch indexing or multi-agent state handoff.

Dimension	Reasoning	Score
Conciseness	The ~180-line body is padded with capability lists, behavioral traits, and a knowledge base that restate concepts Claude already knows (e.g. 'Long-term memory architecture', 'Episodic memory'), matching the verbose anchor 1.	1 / 3
Actionability	Guidance is abstract ('Clarify goals… Apply relevant best practices and validate outcomes') with no executable code or commands, and the single detailed reference points to a non-existent file.	1 / 3
Workflow Clarity	The 'Response Approach' provides a numbered 10-step sequence, but the steps are high-level (Analyze, Design, Implement) with no validation checkpoints or concrete commands, fitting anchor 2 rather than 3.	2 / 3
Progressive Disclosure	Sections are organized and one reference is signaled, but the referenced resources/implementation-playbook.md does not exist and the body is a monolithic wall of text that should be externalized.	2 / 3
	Total	6 / 12 Passed

Description

50%

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 communicates a clear domain but is built on persona buzzwords ('Elite', 'mastering') rather than concrete actions and lacks any explicit trigger guidance for when to invoke the skill.

Suggestions

Rewrite in third person with concrete verbs (e.g. 'Designs and implements dynamic context-management systems, vector indexes, and memory architectures') instead of persona language like 'Elite … specialist mastering'.

Add an explicit trigger clause, e.g. 'Use when building or optimizing context management, RAG retrieval, knowledge graphs, or AI memory systems.'

Drop marketing terms ('Elite', 'intelligent') and replace with specific, user-natural keywords to improve trigger quality and distinctiveness.

Dimension	Reasoning	Score
Specificity	Names the domain and sub-domains ('dynamic context management, vector databases, knowledge graphs, and intelligent memory systems') but uses persona framing ('Elite AI context engineering specialist mastering…') with no concrete actions, so it falls short of anchor 3.	2 / 3
Completeness	It clearly states what the skill covers but includes no 'Use when…' trigger clause; per the guideline a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	Includes some relevant keywords ('vector databases', 'knowledge graphs', 'memory systems') but they are wrapped in buzzword-heavy jargon ('Elite AI context engineering specialist mastering…') and miss common natural variations.	2 / 3
Distinctiveness Conflict Risk	The context-engineering niche is somewhat specific but overlaps with adjacent RAG, vector-database, and memory skills, so it is not clearly distinct.	2 / 3
	Total	8 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: sickn33/antigravity-awesome-skills
Path: skills/context-manager/SKILL.md
Commit: a3068b7

Reviewed: 1 day ago

Table of Contents

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