CtrlK
BlogDocsLog inGet started
Tessl Logo

context-manager

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

55

1.00x
Quality

31%

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/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

17%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 is a verbose persona-and-capability resume: pages of abstract bullet lists with no executable code, commands, or concrete patterns, a 10-step workflow that lacks any validation checkpoints, and a single reference to a file that is not present in the bundle. It tells Claude what an ideal context engineer 'is' rather than what to do.

Suggestions

Replace the abstract Capabilities/Behavioral Traits/Knowledge Base inventories with concrete, executable guidance — code snippets, commands, or specific patterns (e.g., a chunking strategy, an embedding-index setup, a retrieval-recall check) Claude can apply directly.

Add explicit validation checkpoints and feedback loops to the Response Approach steps (e.g., 'verify retrieval recall@k before scaling', 're-validate the embedding index after re-indexing') instead of abstract 'monitor and measure' phrasing.

Create the referenced 'resources/implementation-playbook.md' (currently missing) or remove the broken reference, and move the bulk capability inventories into that reference file so SKILL.md stays a lean overview.

DimensionReasoningScore

Conciseness

The ~175-line body is padded with large capability inventories, a 'Behavioral Traits' list, a 'Knowledge Base' list, and a persona restatement ('You are an elite AI context engineering specialist...') that add no executable knowledge Claude does not already have, matching 'noticeably verbose; several unnecessary padded sections.'

2 / 5

Actionability

Every section is abstract — 'Apply relevant best practices and validate outcomes', 'Dynamic context assembly and intelligent information retrieval' — with no code, commands, or concrete patterns, and the only referenced file does not exist, matching 'entirely vague or abstract; no concrete code or commands; only describes rather than instructs.'

1 / 5

Workflow Clarity

The 10-step 'Response Approach' gives a rough high-level sequence (analyze, design, implement, optimize...) but the steps are abstract and contain no validation checkpoints or feedback loops, matching 'rough sequence present but many gaps; steps poorly defined; validation absent.'

2 / 5

Progressive Disclosure

Section headers provide some structure, but the single external reference ('resources/implementation-playbook.md') points to a file that does not exist, and the large Capabilities/Knowledge Base inventories are inlined rather than split into reference files, matching 'minimal structure; content that clearly belongs in separate files is inlined.'

2 / 5

Total

7

/

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 buzzword-heavy persona statement that names the domain and adjacent technologies but states no concrete actions and omits any explicit 'when to use' guidance. It is readable and on-topic yet too abstract to trigger reliably or distinguish sharply from neighboring AI-infrastructure skills.

Suggestions

Replace the abstract verb 'mastering' with concrete actions the skill performs (e.g., 'designs', 'implements', 'tunes') so the 'what' is specific rather than a persona claim.

Add an explicit 'Use when...' clause naming natural trigger phrases a user would actually say, such as RAG setup, vector database indexing, context-window optimization, or knowledge-graph modeling.

Include common synonyms and concrete terms (RAG, embeddings, semantic search, retrieval, Pinecone/Weaviate/Qdrant) to improve natural keyword coverage and reduce overlap ambiguity.

DimensionReasoningScore

Specificity

The description names the domain and several sub-areas ("dynamic context management, vector databases, knowledge graphs, and intelligent memory systems") but uses only the generic verb "mastering" and lists noun phrases rather than concrete actions, matching the 'names the domain but actions are minimal or generic' anchor.

2 / 5

Completeness

It gives a vague 'what' (an elite specialist mastering a set of domains) but contains no 'Use when...' clause or equivalent trigger guidance, so per the rubric completeness is capped at 3 with 'when' missing.

3 / 5

Trigger Term Quality

It includes some relevant technical terms a user might say ("vector databases", "knowledge graphs", "memory systems") but misses common natural synonyms and phrases like RAG, embeddings, semantic search, and retrieval, fitting 'some relevant keywords but missing common variations or synonyms.'

3 / 5

Distinctiveness Conflict Risk

The context-engineering niche is somewhat specific, but buzzy phrasing like "context management" and "intelligent memory systems" leaves real overlap risk with adjacent RAG, vector-database, and AI-infrastructure skills, matching 'somewhat specific but could still overlap.'

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.

Validation15 / 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
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.