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context-manager

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

Quality

45%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/context-manager/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

21%Weight 40%Scale 1-5

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

The body reads as a resume for a persona rather than a skill: it catalogs capabilities, traits, and knowledge areas Claude already possesses, while providing zero executable guidance. Only the final untrusted-input/security paragraph and the limitations section add real operational value.

Suggestions

Cut the Capabilities, Behavioral Traits, Knowledge Base, and Example Interactions sections; replace them with concrete guidance such as a context-architecture decision checklist, chunking/retrieval parameter recommendations, or actual vector DB setup code.

Make the Response Approach steps concrete and add explicit validation checkpoints (e.g. 'evaluate retrieval quality on a fixed task set for relevance, leakage, latency, and cost before declaring success').

Move any retained detailed material (e.g. per-domain deep dives) into one-level-deep reference files so the SKILL.md stays a lean overview.

DimensionReasoningScore

Conciseness

Roughly 140 lines are generic capability, behavioral-trait, and knowledge-base bullet lists ('Long-term memory architecture and persistent storage', 'Systems thinking approach to context architecture') that Claude already knows and that add no operational value, matching 'noticeably verbose; several padded sections'.

2 / 5

Actionability

Instructions like 'Clarify goals, constraints, and required inputs' and 'Analyze context requirements and identify optimal management strategy' are entirely abstract; there is no code, command, schema, or concrete procedure anywhere in the body, matching 'entirely vague or abstract; only describes rather than instructs'.

1 / 5

Workflow Clarity

The 'Response Approach' section offers a rough 10-step sequence but the steps are high-level and vague, with no validation checkpoints or feedback loops (e.g. 'Monitor and measure context quality' has no concrete check), matching 'Rough sequence present but many gaps; validation absent'.

2 / 5

Progressive Disclosure

The body is well-headered but ~140 lines of capability catalog that clearly belongs in reference files are fully inlined, and no bundle files or references exist at all; this sits between 'minimal structure with inlined separable content' (headers do exist) and 'some structure'.

3 / 5

Total

8

/

20

Passed

Description

45%Weight 40%Scale 1-5

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 is a persona-flavored domain statement that names relevant technology areas but never states concrete actions or when to use the skill. It will be hard for a user (or Claude) to trigger it confidently against related RAG/memory/database skills.

Suggestions

Replace the persona claim with concrete actions, e.g. 'Designs and implements dynamic context management systems: vector database selection, RAG pipelines, memory architectures, and multi-agent context handoff.'

Add an explicit 'Use when...' clause listing natural trigger phrases such as 'context window optimization', 'RAG pipeline design', 'vector database setup', or 'knowledge graph construction'.

Add common synonyms users would say (RAG, retrieval, embeddings, Pinecone/Weaviate/Qdrant) to improve trigger term coverage.

DimensionReasoningScore

Specificity

Phrases like 'Elite AI context engineering specialist mastering' are persona claims rather than actions; the domains are named (dynamic context management, vector databases, knowledge graphs) but no concrete actions are described, matching 'Names the domain but actions are minimal or generic'.

2 / 5

Completeness

There is no 'Use when...' clause or equivalent trigger guidance, capping completeness at 3; the 'what' is a role description ('specialist mastering...') rather than a clear statement of what the skill does, sitting between the vague-what and clear-what anchors.

3 / 5

Trigger Term Quality

'Vector databases', 'knowledge graphs', and 'memory systems' are keywords domain users would say, but common variations and synonyms are missing (RAG, retrieval, embeddings, Pinecone, context window).

3 / 5

Distinctiveness Conflict Risk

The scope spans RAG, memory systems, vector databases, knowledge graphs, and multi-agent orchestration, creating overlap risk with several distinct skills in those domains, matching 'Somewhat specific but could still overlap with similar skills'.

3 / 5

Total

11

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

sickn33/agentic-awesome-skills

Path

skills/context-manager/SKILL.md

Reviewed

Table of Contents

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