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self-improvement

Captures learnings, errors, corrections, and feature requests to enable continuous improvement. Use when: (1) User corrects Claude ('No, that's wrong...', 'Actually...'), (2) User requests a capability that doesn't exist, (3) Claude realizes its knowledge is outdated or incorrect, (4) A better approach is discovered for a recurring task, (5) Receiving a Handoff block from self-healing (a recurring verified heal with Recurrence-Count at least 3) to distill into a memory file or new skill. For ACTIVE runtime failures where the agent needs to apply and verify a fix mid-task, use `self-healing` instead (it files HEAL- entries with proof; self-improvement promotes accumulated patterns). Also review learnings before major tasks. For CI-only/headless learning capture, use self-improvement-ci.

77

1.01x
Quality

89%

Does it follow best practices?

Impact

98%

1.01x

1 of 3 eval scenarios. Add 2 more for a full score.

SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is self-improvement in pskoett/pskoett-ai-skills

SKILL.md
Quality
Evals
Security

Quality

Content

75%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.

A dense, highly actionable body for a logging-based improvement loop, with excellent copy-paste templates, executable grep/script commands, and well-gated one-level-deep references. The main weaknesses are token spend on material Claude already knows (priority/area semantics, trigger lists duplicating the frontmatter) and the absence of verification checkpoints in the promotion and resolution workflows.

Suggestions

Trim the Priority Guidelines and Area Tags tables to bare value enumerations (or move them to a reference) — explanations like "critical | Blocks core functionality, data loss risk, security issue" and "frontend | UI, components, client-side code" restate what Claude already knows.

Compress Detection Triggers and Multi-Agent Support (which restate the frontmatter's trigger list) and move Copilot Chat Integration to a reference to cut body tokens without losing operational content.

Link references/examples.md from the body (e.g., from the Logging Format section) so it is discoverable, and add a verify step to the promotion workflow (confirm the promoted rule exists in the target file), mirroring the extraction workflow's existing "Verify" step.

DimensionReasoningScore

Conciseness

Several sections restate what Claude already knows or what the frontmatter already specifies: the Priority Guidelines table ("critical | Blocks core functionality, data loss risk, security issue"), the Area Tags table ("frontend | UI, components, client-side code"), the Detection Triggers section duplicating the description's trigger list, and the Copilot Chat Integration prompts. Not 2 because the core operational material (entry templates, ID format, grep commands, promotion rule) is dense and earns its tokens; not 4 because the padding is more than minor trimmable instances.

3 / 5

Actionability

Fully executable throughout: complete copy-paste entry templates for all three file types, ID format with concrete examples ("LRN-20250115-001", "ERR-20250115-A3F"), runnable commands ("grep -n \"Pattern-Key: <pattern_key>\" .learnings/LEARNINGS.md", "./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run"), and concrete promotion examples showing verbose learning → concise CLAUDE.md rule. Covers the common cases (log, dedupe, resolve, promote, extract) with no gaps.

5 / 5

Workflow Clarity

Multi-step workflows are clearly sequenced with an explicit promotion threshold ("Recurrence-Count >= 3", 2 distinct tasks, 30-day window), search-first dedupe, a dry-run step, a "Verify: Read skill in fresh session to ensure it's self-contained" checkpoint, and a Skill Quality Gates checklist. Not 5 because the promotion and resolution workflows lack the explicit verification checkpoints that extraction has (e.g., no step confirming the promoted rule actually landed in the target file). Not 3 because sequences are complete and checkpoints exist in the risky workflows.

4 / 5

Progressive Disclosure

References are one level deep, clearly signaled as markdown links, and gated ("Read these only when installing or configuring the skill"); installation.md, hooks-setup.md, amp-setup.md, and context-maintenance.md all exist in references/, and scripts/extract-skill.sh exists. Not 5 because references/examples.md is never linked from the body, leaving part of the bundle undiscoverable from SKILL.md, and the 485-line body keeps some material (full entry templates) inline that could live in a reference file. Not 3 because structure and navigation are otherwise good with clearly signaled references.

4 / 5

Total

16

/

20

Passed

Description

100%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.

An exemplary description: concrete third-person action statement, five explicit numbered use-when triggers including quoted natural user phrases, and explicit disambiguation from the two adjacent skills (self-healing, self-improvement-ci). It clearly matches the strongest anchors on all four dimensions with no padding.

DimensionReasoningScore

Specificity

Lists multiple concrete actions covering the domain's full cycle: "Captures learnings, errors, corrections, and feature requests", "distill into a memory file or new skill", "promotes accumulated patterns", and "review learnings before major tasks". Not 4 because coverage spans capture, promotion, and review phases with no obvious minor gaps left unstated.

5 / 5

Completeness

Explicitly answers "what" ("Captures learnings, errors, corrections, and feature requests to enable continuous improvement") and "when" with five numbered trigger conditions plus review timing ("Also review learnings before major tasks"). Not 4 because the "when" clause is fully explicit with concrete trigger phrases rather than merely present-but-improvable.

5 / 5

Trigger Term Quality

Includes literal natural phrases users say when this skill applies ("'No, that's wrong...'", "'Actually...'") plus condition-style triggers ("requests a capability that doesn't exist", "knowledge is outdated or incorrect", "A better approach is discovered for a recurring task"). Not 4 because both conversational quotes and their condition synonyms are present; no natural variant is obviously missing (file extensions are N/A for a conversation-triggered skill).

5 / 5

Distinctiveness Conflict Risk

Actively routes overlap-prone neighbors elsewhere: "For ACTIVE runtime failures where the agent needs to apply and verify a fix mid-task, use `self-healing` instead" and "For CI-only/headless learning capture, use self-improvement-ci", carving a clear niche. Not 4 because adjacent skills are explicitly disambiguated rather than differing only in wording.

5 / 5

Total

20

/

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 suspicious

Warning

Total

15

/

16

Passed

Repository

pskoett/pskoett-ai-skills

Path

agent-plugin/skills/self-improvement/SKILL.md

Reviewed

Table of Contents

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