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context-manager

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

1.00x
Quality

37%

Does it follow best practices?

Impact

99%

1.00x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/context-manager/SKILL.md

The canonical home for this skill is context-manager in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

10%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.

This is a persona/role prompt masquerading as a skill: an extensive abstract catalog of capabilities, traits, and knowledge with no executable guidance, no concrete workflows, and a broken reference to a nonexistent playbook file. A Claude loading this skill would gain essentially no actionable information beyond a vague role framing.

Suggestions

Replace the capability/trait/knowledge bullet catalogs with concrete, task-oriented guidance: actual code patterns for RAG chunking, vector DB index setup commands, memory schema examples, and decision criteria for choosing between approaches.

Fix the broken progressive disclosure: either create resources/implementation-playbook.md with the substantive material and move the capability catalogs into it, or delete the dead reference.

Convert the generic 10-step 'Response Approach' into a real workflow with validation checkpoints (e.g. verify retrieval quality metrics, test context assembly outputs) instead of phase-name placeholders.

Cut the 'Behavioral Traits' and 'Knowledge Base' sections entirely — they restate knowledge Claude already has and add no skill-specific value.

DimensionReasoningScore

Conciseness

The ~175-line body is almost entirely padded abstraction — ten capability sections of generic bullet lists ('Dynamic context assembly and intelligent information retrieval', 'Advanced vector database implementation'), 'Behavioral Traits' ('Systems thinking approach', 'Quality-first mindset'), and a 'Knowledge Base' section that restate what Claude already knows at a purely abstract level. This matches anchor 1 ('severely verbose... heavily padded'); there is no section that earns its tokens with information Claude lacks.

1 / 5

Actionability

There is no code, no command, no concrete procedure, and no decision criterion anywhere. The Instructions section is entirely generic ('Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes.'), and the only pointer to substance — 'open resources/implementation-playbook.md' — references a file that does not exist in the bundle. This is anchor 1 ('entirely vague or abstract; only describes rather than instructs').

1 / 5

Workflow Clarity

The 'Response Approach' section does provide a rough 10-step sequence ('Analyze context requirements... Design context architecture... Implement dynamic systems...'), so steps are not entirely missing (not anchor 1). But every step is a generic phase name with no commands, no inputs/outputs, and no validation checkpoints — anchor 2 ('rough sequence present but many gaps; steps poorly defined; validation absent').

2 / 5

Progressive Disclosure

The bundle contains no references/, scripts/, or assets/ directories, and the body's single reference — 'resources/implementation-playbook.md' — points to a nonexistent file (broken navigation). Meanwhile ~150 lines of capability catalog that clearly belong in separate reference files are fully inlined in SKILL.md. This matches anchor 2 ('minimal structure; content that clearly belongs in separate files is inlined'); section headers exist, so it is not the structureless anchor 1, but organization is poor and the one reference is dead.

2 / 5

Total

6

/

20

Passed

Description

36%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 buzzword-heavy persona statement rather than a functional skill description: it names domains but no actions, includes no 'Use when' trigger clause, and uses inflation language ('Elite', 'intelligent') instead of concrete capability claims. It would rarely be selected correctly at trigger time because it never states what it does or when to use it.

Suggestions

Rewrite as third-person action statements, e.g. 'Designs and implements dynamic context management systems: RAG pipelines, vector database setups (Pinecone, Qdrant, Weaviate), knowledge graphs, and long-term agent memory architectures.'

Append an explicit trigger clause: 'Use when building or optimizing RAG, retrieval, embeddings search, knowledge graphs, or AI agent memory systems.'

Remove inflation words ('Elite', 'intelligent', 'specialist mastering') that add no signal, and add the natural synonyms users actually say (RAG, retrieval, semantic search, embeddings).

DimensionReasoningScore

Specificity

The description is a persona title ("Elite AI context engineering specialist mastering...") that names the domain and sub-domains ("dynamic context management, vector databases, knowledge graphs, and intelligent memory systems") but contains zero concrete action verbs — it never states what the skill actually does. It sits between anchor 1 (pure abstraction) and anchor 3 (1-2 concrete actions): more domain-naming than 'Helps with documents', but no actions at all, so it does not reach 3.

2 / 5

Completeness

There is no 'Use when...' clause or equivalent trigger guidance, so completeness is capped at 3 per the judging guidelines; the 'what' itself is also vague — it asserts mastery of domains rather than describing functions (design? implement? review?). This lands on anchor 2 ('has a vague what and no when'): more scope-naming than anchor 1, but the function is never stated, so it does not merit 3's 'clear what'.

2 / 5

Trigger Term Quality

Terms like "vector databases", "knowledge graphs", and "context management" are relevant but lean technical; common natural phrases users would actually say — RAG, retrieval, embeddings, semantic search, Pinecone/Qdrant/Weaviate, memory for AI agents — are missing. This matches anchor 3 ('some relevant keywords but missing common variations or synonyms') rather than 4, whose examples show fuller natural-term coverage.

3 / 5

Distinctiveness Conflict Risk

The niche (AI context engineering) is somewhat specific, but the description overlaps heavily with any RAG, vector-database, or multi-agent skill a user might also have, and with no trigger guidance there is nothing to disambiguate. This matches anchor 3 ('somewhat specific but could still overlap with similar skills'); it is not 4 because the overlap risk with the very common RAG/retrieval skill category is more than minor.

3 / 5

Total

10

/

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills-claude/skills/context-manager/SKILL.md

Reviewed

Table of Contents

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