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context-manager

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.

Quality

49%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./.agent/skills/context-manager/SKILL.md
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 an empty shell: generic trigger sections, abstract instructions with no executable guidance, and buzzword persona statements that add nothing Claude does not already know. Its one structural virtue — an overview pointing to ten sub-skill files and a playbook — is entirely broken since none of those files exist in the bundle.

Suggestions

Create the referenced files (sub-skills/*.md and resources/implementation-playbook.md) or inline real content; a SKILL.md whose every reference dangles provides no progressive disclosure at all.

Delete the persona line and 'Expert Purpose' paragraph, and replace the generic Instructions with 3-5 concrete, task-specific steps (e.g. how to select an embedding store, how to structure agent memory, when to use a knowledge graph vs. vector search).

Replace 'Apply relevant best practices and validate outcomes' with an explicit validation checkpoint tied to the actual work, such as a concrete test or verification command for context-pipeline changes.

DimensionReasoningScore

Conciseness

The persona line ('You are an elite AI context engineering specialist...') duplicates the 'Expert Purpose' paragraph, which is pure buzzword padding ('Combines advanced context engineering techniques with modern vector databases, knowledge graphs, and intelligent retrieval systems') with no actionable content. The Use/Do-not-use sections are equally generic filler, so a large share of even this short body is padding — matching anchor 2 rather than 3, though it never stoops to explaining known concepts (anchor 1).

2 / 5

Actionability

The Instructions are entirely abstract ('Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification.') with no code, commands, decision rules, or specific techniques — only describing rather than instructing, exactly anchor 1. The sole concrete element is a pointer to a playbook file that does not exist in the bundle.

1 / 5

Workflow Clarity

The Instructions sketch a rough implied sequence (clarify goals, apply best practices, validate outcomes, provide verification) but no step is defined or executable and 'validate outcomes' names no concrete checkpoint. It sits above anchor 1 (a rough sequence does exist) and below anchor 3 (there is no real sequence of actual operations to sequence).

2 / 5

Progressive Disclosure

The structure and signaling follow the right pattern — a short overview with a numbered, one-level-deep module list and a labeled pointer to 'resources/implementation-playbook.md' — but scored against the actual bundle, none of the 11 referenced files exist: no sub-skills/ or resources/ directory is present, and the 'Capabilities' heading is empty. Navigation leads nowhere, so the disclosure structure is nonfunctional; below anchor 3, above anchor 1 only because the reference pattern itself is correctly designed.

2 / 5

Total

7

/

20

Passed

Description

61%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 identifies a real niche with several natural domain keywords and an explicit (if vague) trigger clause, but it is weakened by buzzword padding ('Elite', 'mastering', 'intelligent') and abstract verbs instead of concrete, quotable actions. The trigger 'complex AI orchestration' is too broad to serve as a reliable activation signal.

Suggestions

Replace abstract role claims with 2-4 concrete actions, e.g. 'Designs context pipelines that combine vector retrieval, knowledge graphs, and agent memory; selects chunking, embedding, and caching strategies for RAG systems'.

Replace 'Use PROACTIVELY for complex AI orchestration' with concrete trigger conditions, e.g. 'Use when the user mentions RAG, context windows, agent memory, vector database selection, or losing state across long multi-agent workflows'.

Remove filler ('Elite', '2024/2025 best practices') and add missing natural synonyms (RAG, embeddings, retrieval, context window) to improve both specificity and trigger coverage.

DimensionReasoningScore

Specificity

The description names domains and technologies ('dynamic context management, vector databases, knowledge graphs, and intelligent memory systems') but the actions are generic role claims ('mastering', 'Orchestrates context') rather than concrete operations, padded with buzzwords like 'Elite' and '2024/2025 best practices'. It falls below anchor 3, which requires 1-2 concrete actions, but above anchor 1 because specific domains and objects of work are named.

2 / 5

Completeness

Both a what ('Elite AI context engineering specialist mastering... Orchestrates context across multi-agent workflows...') and an explicit when ('Use PROACTIVELY for complex AI orchestration') are present. It matches anchor 4 ('when' could be more explicit or specific) rather than 5, because the trigger 'complex AI orchestration' names no concrete situations, and the 'what' is buzzword-laden.

4 / 5

Trigger Term Quality

Natural domain keywords are present ('context management', 'vector databases', 'knowledge graphs', 'multi-agent workflows', 'AI orchestration') and users would plausibly say these. It is below anchor 5 because common variations like 'RAG', 'embeddings', 'retrieval', or file extensions are missing, but above anchor 3 since several distinct natural terms appear rather than one generic phrase.

4 / 5

Distinctiveness Conflict Risk

'AI context engineering' is a recognizable niche, but the description claims coverage of vector databases, knowledge graphs, memory systems, and multi-agent orchestration individually, so it overlaps with skills dedicated to each of those areas. It fits anchor 3 ('somewhat specific but could still overlap with similar skills') rather than 4, where overlap would be minor.

3 / 5

Total

13

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 10 missing

Warning

Total

13

/

16

Passed

Repository

Dokhacgiakhoa/Agent-Skills-4-Vibe-Coding-CLI

Path

.agent/skills/context-manager/SKILL.md

Reviewed

Table of Contents

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