strategic-compact

建议在逻辑间隔处手动压缩上下文，以在任务阶段中保留上下文，而非任意的自动压缩。

Quality

38%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./docs/zh-CN/skills/strategic-compact/SKILL.md

Quality

Discovery

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This description is a vague, abstract recommendation about context management rather than a proper skill description. It fails to specify concrete actions, lacks natural trigger terms, provides no 'when to use' guidance, and is indistinguishable from general advice. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.

Suggestions

Rewrite the description to specify concrete actions the skill performs, e.g., 'Summarizes and compresses conversation context at logical breakpoints during multi-step tasks to preserve key information.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the conversation is getting long, when context window limits are approaching, or when switching between task phases.'

Use third-person declarative voice and include specific scenarios or file types/task types where this skill applies to distinguish it from general conversation management.

Dimension	Reasoning	Score
Specificity	The description is vague and abstract. It mentions 'compressing context at logical intervals' but does not list concrete actions or specific capabilities. It reads more like a general recommendation than a skill description.	1 / 3
Completeness	The description only vaguely hints at 'what' (manual context compression) and completely lacks any 'when should Claude use it' guidance. There is no 'Use when...' clause or equivalent explicit trigger.	1 / 3
Trigger Term Quality	The description is in Chinese and uses abstract terms like '压缩上下文' (compress context) and '逻辑间隔' (logical intervals). These are not natural keywords a user would say when seeking help with a task. There are no recognizable trigger terms.	1 / 3
Distinctiveness Conflict Risk	The description is extremely generic — 'context compression' could apply to virtually any long-running conversation or task. It provides no clear niche or distinct triggers to differentiate it from other skills.	1 / 3
	Total	4 / 12 Passed

Implementation

77%

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

This is a well-structured skill with strong actionability and clear workflow guidance. Its main weakness is verbosity — the token optimization patterns section and explanatory content about why auto-compaction is problematic add significant length without proportional value. Ironically, a skill about context efficiency could itself be more concise.

Suggestions

Move the 'Token Optimization Patterns' section (trigger table lazy loading, context composition awareness, deduplication detection, context optimization tools) to a separate reference file to reduce the main skill's token footprint.

Remove or significantly trim the 'Why Strategic Compaction?' section — Claude doesn't need an explanation of why auto-compaction is suboptimal; just state the recommendation directly.

Dimension	Reasoning	Score
Conciseness	The skill contains useful information but is verbose in places. The 'Why Strategic Compaction?' section explains concepts Claude likely already understands (how auto-compaction works). The token optimization patterns section at the end feels tangential and bloats the skill. Several tables, while useful, could be more concise.	2 / 3
Actionability	Provides concrete, copy-paste ready JSON configuration for hooks setup, specific environment variable configuration, a clear decision table for when to compact, and actionable best practices with specific commands like `/compact Focus on implementing auth middleware next`.	3 / 3
Workflow Clarity	The workflow is clearly sequenced: the hook tracks tool calls → threshold detection → suggestion → user decides. The decision table provides explicit guidance for each phase transition. Best practices include clear ordering (write before compact, don't compact mid-implementation) which serves as validation checkpoints.	3 / 3
Progressive Disclosure	The skill has good section organization but includes too much inline content that could be split out (token optimization patterns, context composition awareness, deduplication detection). The 'Related' section references external resources but the main body is monolithic. The token optimization section feels like it belongs in a separate file.	2 / 3
	Total	10 / 12 Passed

Validation

90%

Warnings & errors only

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

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: affaan-m/everything-claude-code
Commit: 5df943e

Reviewed: about 22 hours ago

Table of Contents

Discovery Implementation Validation

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