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memory-organizer

智能对话分析与记忆整理助手。获取并压缩指定 Agent 的对话记录,用于在用户授权/触发条件满足时,将信息分类写入 USER.md / AGENT.md / MEMORY.md(含索引与 memory/ 日志)/ IDENTITY.md / SOUL.md。

58

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./app/skills/memory-organizer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

The body is highly actionable with concrete commands, paths, and templates, and has a clear sequenced workflow, but it is held back by redundant explanatory padding, the absence of an explicit validate/verify feedback loop for destructive memory writes, and inline template content that could be split into reference files.

Suggestions

Collapse the redundant trigger-condition statements (the callout, 核心原则, and 何时触发 sections) into a single concise block to tighten token usage.

Add an explicit validation feedback loop after the 写入更新 step — e.g., re-read the target file to confirm the entry landed and dedup holds — since memory writes are batch/destructive operations.

Move the per-file write templates (USER/AGENT/MEMORY/IDENTITY/SOUL) into a dedicated reference file (e.g., references/templates.md) and link to it one level deep, keeping SKILL.md a lean overview.

DimensionReasoningScore

Conciseness

Mostly efficient and avoids explaining concepts Claude already knows, but carries redundancy and verbose instruction padding (e.g., the 核心原则 emoji bullets re-stating constraints, repeated trigger conditions across the callout and the 何时触发 section, and verbose template caveats like '不要把今天做的整理工作写成今天发生的事情'); it could be tightened, so it is at 'mostly efficient but includes some unnecessary explanation' rather than a lean 3.

2 / 3

Actionability

Provides fully executable, copy-paste-ready guidance: concrete CLI commands with documented flags and time formats, real relative paths (scripts/fetch_conversations.py, {SAGE_ROOT}/agents/{agent_id}/), a message-compression table, and explicit markdown write templates; this matches the 'fully executable code/commands; copy-paste ready' anchor and exceeds the incomplete-guidance bar of 2.

3 / 3

Workflow Clarity

The 执行流程 section gives a clear ordered 5-step sequence with a 去重校验 checkpoint and a 安全 confirmation gate for .env/credentials, but the destructive/batch memory-file writes lack an explicit validate→fix→retry feedback loop (no 're-read to confirm the write' step); per the rubric, missing validation feedback in batch/destructive operations caps workflow clarity at 2 rather than 3.

2 / 3

Progressive Disclosure

Structure is clean with one well-signaled one-level-deep bundle reference (scripts/fetch_conversations.py, confirmed present, with a dedicated 文件路径 section) and no nested indirection, but all the memory/templates live inline in SKILL.md rather than being split into separate reference files — 'content that could be separate is inline' — which places it at 2 rather than the cleanly-split 3.

2 / 3

Total

9

/

12

Passed

Description

67%

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 specific and distinctive, naming concrete actions and a clear five-file memory taxonomy, but it omits an explicit 'Use when...' trigger clause and natural user-facing trigger phrasing, capping completeness and trigger-term quality at 2.

Suggestions

Add an explicit 'Use when...' clause with natural trigger phrases users would actually say (e.g., 'Use when the user asks to 整理记忆 / 更新记忆文件 / 把这个记住, or wants to compress a period of conversation history').

Surface common user utterances (整理记忆、更新记忆文件、记住这个、压缩对话) directly in the description rather than only in the body, to improve trigger-term coverage.

Keep the concrete five-file taxonomy but trim the parenthetical detail to keep the description a tight, scannable overview.

DimensionReasoningScore

Specificity

Names multiple concrete actions and targets — '获取并压缩指定 Agent 的对话记录' and '将信息分类写入 USER.md / AGENT.md / MEMORY.md ... / IDENTITY.md / SOUL.md' — matching the 'lists multiple specific concrete actions' anchor; below it has no score 4, and 2 is too weak since both actions and five named destinations are given.

3 / 3

Completeness

Clearly states the 'what' (fetch/compress conversations, classify into five named files) but the 'when' is only implied via '用于在用户授权/触发条件满足时' with no explicit 'Use when...' clause; per the rubric guideline a missing explicit trigger caps completeness at 2, and it is not a 1 since the what is unambiguous.

2 / 3

Trigger Term Quality

Contains some relevant terms (对话分析, 记忆整理, 压缩对话记录) but leans descriptive/technical rather than natural user phrasing; the genuine trigger utterances ('整理记忆', '把这个记住') appear only in the body, not the description, so it sits at 'some relevant keywords but missing common variations' rather than full coverage.

2 / 3

Distinctiveness Conflict Risk

Occupies a clear niche — conversation analysis plus memory organization across a specific five-file taxonomy (USER/AGENT/MEMORY/IDENTITY/SOUL) — making it unlikely to trigger for unrelated skills; 2 would apply only if it overlapped with similar skills, which the named-file scheme avoids.

3 / 3

Total

10

/

12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
ZHangZHengEric/Sage
Reviewed

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