CtrlK
BlogDocsLog inGet started
Tessl Logo

continuous-learning

Claude Codeセッションから再利用可能なパターンを自動的に抽出し、将来の使用のために学習済みスキルとして保存します。

43

Quality

42%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./docs/ja-JP/skills/continuous-learning/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%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 setup half of the skill (config + Stop hook wiring) is concrete and usable, but the operational core — how patterns are detected and turned into saved skills — is never specified, the referenced script and spec document are absent from the bundle, and a dated comparative-research section consumes context without adding executable value. As written, Claude could install the hook but could not perform the skill's actual work.

Suggestions

Replace the named steps with the actual executable extraction workflow (or ship evaluate-session.sh in scripts/ and point to it), including how a detected pattern becomes a written skill under ~/.claude/skills/learned/.

Move the Homunculus comparison notes into a separate reference file (or delete them) and remove the dangling docs/continuous-learning-v2-spec.md link — dated research does not belong in the main body.

Add a validation checkpoint before writing learned skills (e.g. verify the pattern generalizes and is not in ignore_patterns) so the automatic batch write is not unguarded.

DimensionReasoningScore

Conciseness

The trailing "比較ノート (調査: 2025年1月)" section — a dated research comparison against Homunculus with a five-row feature table and v2 speculation — is conceptual background that does not instruct execution, and time-dated material outside a deprecated/old-patterns section further penalizes conciseness. Not a 3 because multiple sections (the comparison notes, the 'Stopフックを使用する理由' rationale, and the pattern table duplicating config keys) could be cut without losing actionable content.

2 / 5

Actionability

The config.json block and the settings.json hook snippet are concrete and copy-pasteable, but the actual extraction mechanism is only named ("パターン検出", "スキル抽出") with no executable process, and the referenced script ~/.claude/skills/continuous-learning/evaluate-session.sh does not exist in the bundle. Not a 4 because the core task — how patterns are detected and saved — has no concrete implementation guidance.

3 / 5

Workflow Clarity

A rough three-step sequence exists (セッション評価 → パターン検出 → スキル抽出), but it is a summary of what the missing script does, not an executable workflow, and there are no validation checkpoints (e.g. confirming a pattern is worth saving before writing to ~/.claude/skills/learned/) for this automatic batch write. Not a 2 because the hook wiring and config thresholds do give some sequence and gating.

3 / 5

Progressive Disclosure

The body has reasonable section headers and the config/hook snippets are appropriately placed, but the ~28-line comparison-notes section is inline content that clearly belongs in a separate reference file, and it points to docs/continuous-learning-v2-spec.md which dangles with no bundle file behind it. Not a 2 because the document is genuinely sectioned and navigable rather than a minimal or buried structure.

3 / 5

Total

11

/

20

Passed

Description

42%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 states a clear, concrete 'what' in third person, but provides zero trigger guidance and no natural user-facing keywords, so it would rarely be surfaced when needed. It is serviceable as a domain label but fails at its main job of telling Claude when to use it.

Suggestions

Add an explicit 'when' clause (e.g. "Use when a session ends and reusable patterns should be captured, or when the user asks to learn from past sessions / save learnings / extract patterns").

Include natural trigger terms and synonyms users would actually say — 'learn from this session', 'remember this fix', 'save this pattern', 'session retrospective' — rather than only technical jargon.

Briefly enumerate the pattern categories detected (error resolutions, workarounds, project conventions) to sharpen specificity and distinguish it from generic memory/learning skills.

DimensionReasoningScore

Specificity

Names the domain (Claude Code sessions, pattern extraction) and two concrete actions — "再利用可能なパターンを自動的に抽出し" and "学習済みスキルとして保存します" — but stops there, matching '1-2 concrete actions, not comprehensive'. It is not a 4 because no further actions or scope details (what patterns, what a learned skill looks like) are given.

3 / 5

Completeness

The 'what' is clear (extract reusable patterns and save them as learned skills), but the 'when' is entirely absent — there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the rubric guidelines. It is not a 2 because the 'what' half is concrete rather than vague.

3 / 5

Trigger Term Quality

The only keywords are technical jargon ("Claude Codeセッション", "再利用可能なパターン", "学習済みスキル") with no natural phrases a user would say to invoke this. Not a 3 because there are no relevant natural variations or synonyms at all — a user would never utter these terms when wanting this skill.

2 / 5

Distinctiveness Conflict Risk

The domain (automatic pattern extraction from Claude Code sessions) is somewhat specific, but it overlaps with memory/learning/skill-management skills and has no distinct triggers to disambiguate. Not a 4 because nothing in the description prevents mis-triggering against other learning or session-review skills.

3 / 5

Total

11

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
affaan-m/ECC
Reviewed

Table of Contents

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.