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context-optimization

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity.

49

Quality

54%

Does it follow best practices?

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SecuritybySnyk

Passed

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Fix and improve this skill with Tessl

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

The canonical home for this skill is context-optimization in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

50%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 content is well-organized into themed sections with useful decision guidance and illustrative snippets, but it over-explains known concepts, offers pseudocode rather than executable steps, and lacks validation checkpoints and real external references. It lands at a competent-but-unexceptional middle band across all dimensions.

Suggestions

Trim explanations of concepts Claude already knows (e.g., what a KV-cache is) and keep only the skill-specific guidance.

Replace pseudocode helpers with concrete, executable examples or explicitly justify the abstraction.

Add explicit validate-and-retry checkpoints for compaction/masking steps, since discarding context is a destructive operation.

Either create the referenced "Optimization Techniques Reference" file with a real link, or remove the dangling reference to avoid a buried, non-existent pointer.

DimensionReasoningScore

Conciseness

The body is mostly efficient and well-structured but includes unnecessary explanations of concepts Claude already knows, e.g., "The KV-cache stores Key and Value tensors computed during inference" and restated insight lines.

3 / 5

Actionability

Three code snippets are present but they rely on undefined helpers (compact_context, store_observation, extract_key) and read as illustrative pseudocode rather than copy-paste executable guidance.

3 / 5

Workflow Clarity

A decision framework and guidelines list give rough sequencing, but there are no explicit validation checkpoints for operations that discard context (compaction/masking), which caps workflow clarity at 3.

3 / 5

Progressive Disclosure

Section headers provide reasonable structure, but all content is inlined in one ~170-line file and the only named reference ("Optimization Techniques Reference") is not a real linked file, leaving organization and signaling only partial.

3 / 5

Total

12

/

20

Passed

Description

58%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 clearly communicates what the skill does and its four core strategies, but it lacks any explicit trigger guidance for when Claude should activate it, which caps its completeness. It is reasonably specific and distinct but leans technical rather than user-phrased.

Suggestions

Add an explicit 'Use when...' clause listing concrete activation triggers (e.g., context utilization high, long agent runs, cost/latency from long contexts).

Include natural user phrasings as trigger terms (e.g., 'running out of context', 'context limit', 'token budget') rather than only technical jargon.

Tighten the 'goal' sentence which restates the opening, to reduce redundancy.

DimensionReasoningScore

Specificity

Names the domain and four concrete strategies ("strategic compression, masking, caching, and partitioning") giving several specific actions, though they remain abstract categories rather than fully concrete operations.

4 / 5

Completeness

It clearly states what the skill does (extends effective capacity via four strategies) but provides no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Contains relevant keywords ("context windows", "context capacity", "compression", "caching") but leans technical and omits natural user phrasings like "running out of context" or "context limit".

3 / 5

Distinctiveness Conflict Risk

"Context optimization" is a fairly distinct niche tied to specific levers, with only minor overlap risk against closely related context-engineering skills.

4 / 5

Total

14

/

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

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