compact

Compact current session memory into structured text for session recovery. Supports custom descriptions and tagging.

Quality

36%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Risky

Do not use without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./.codex/skills/memory-compact/SKILL.md

Quality

Content

39%

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

The skill provides a thorough and well-structured workflow for session memory compaction with clear sequencing and validation. However, it is severely over-engineered and verbose—much of the content (field definition tables, illustrative pseudocode, path resolution rules) could be dramatically condensed or split into reference files. The JavaScript examples are pseudocode with undefined helper functions, reducing actionability despite the otherwise concrete output format.

Suggestions

Reduce content by at least 60%: remove the field definitions table (duplicates the template), collapse the JavaScript pseudocode into brief bullet-point instructions, and trim the path resolution section to essential rules only.

Replace illustrative pseudocode (extractTodosFromConversation, inferPlanFromDiscussion) with concrete instructions describing what Claude should actually do—e.g., 'Scan conversation for TodoWrite tool calls and extract their content verbatim.'

Split reference material (field definitions, path resolution rules, plan detection priority details) into separate bundle files and reference them from the main SKILL.md to improve progressive disclosure.

Remove the 'Core Philosophy' section entirely—these are implicit in the structured output format and quality checklist, and Claude doesn't need motivational framing.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~300+ lines. The field definitions table duplicates information already shown in the structured output format. The JavaScript code examples for session analysis, plan detection, and text generation are illustrative pseudocode that Claude doesn't need spelled out in such detail. The 'Core Philosophy' bullets, extensive path resolution rules, and reference file categories table add significant bloat. Much of this could be condensed to the output template + a few key rules.	1 / 3
Actionability	The structured output format is concrete and copy-paste ready, and the MCP tool call is specific. However, the JavaScript code throughout is pseudocode/illustrative rather than executable (e.g., `extractTodosFromConversation()`, `inferPlanFromDiscussion()` are undefined functions). The plan detection section describes what to do conceptually but relies on fictional helper functions.	2 / 3
Workflow Clarity	The 4-step execution flow (Analyze → Generate → Import → Report) is clearly sequenced with explicit outputs at each stage. The quality checklist serves as a validation checkpoint before saving. The priority order for plan detection is well-defined with clear fallback logic.	3 / 3
Progressive Disclosure	This is a monolithic wall of text with no references to external files despite being complex enough to warrant splitting. The field definitions table, path resolution rules, plan detection logic, and reference file categories could all be separate reference documents. No bundle files are provided, and no external references are made, resulting in everything being inlined in one massive document.	1 / 3
	Total	7 / 12 Passed

Description

32%

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 conveys the core purpose of compacting session memory but lacks explicit trigger guidance ('Use when...'), which is a significant gap for skill selection. The trigger terms are somewhat relevant but miss common user phrasings, and the specificity of actions could be improved with more concrete details about what the skill produces.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to save, compress, or checkpoint the current conversation context for later recovery.'

Include natural trigger terms users might say, such as 'save context', 'compress memory', 'checkpoint session', 'persist conversation', or 'remember this conversation'.

Expand the concrete actions listed—e.g., 'Summarizes conversation history into a compact structured format, tags key topics, and stores recovery metadata for resuming sessions later.'

Dimension	Reasoning	Score
Specificity	Names the domain (session memory) and some actions (compact, session recovery, custom descriptions, tagging), but doesn't list multiple concrete actions in detail—e.g., what 'compact' means specifically or what structured text looks like.	2 / 3
Completeness	Describes what the skill does (compact session memory into structured text) but has no explicit 'Use when...' clause or equivalent trigger guidance, which per the rubric caps completeness at 2, and the 'when' is entirely missing, warranting a 1.	1 / 3
Trigger Term Quality	Includes some relevant terms like 'session memory', 'session recovery', and 'tagging', but misses natural user phrases like 'save context', 'remember conversation', 'persist memory', 'checkpoint', or 'compress memory'.	2 / 3
Distinctiveness Conflict Risk	The concept of 'session memory compaction' is fairly niche, but without explicit trigger conditions it could overlap with other memory management or context-saving skills. The terms 'structured text' and 'tagging' are somewhat generic.	2 / 3
	Total	7 / 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: catlog22/Claude-Code-Workflow
Commit: 5ff5e86

Reviewed: 27 days ago

Table of Contents

Discovery Implementation Validation

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.