CtrlK
BlogDocsLog inGet started
Tessl Logo

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wrong...', 'Actually...'), (2) User requests a capability that doesn't exist, (3) Claude realizes its knowledge is outdated or incorrect, (4) A better approach is discovered for a recurring task, (5) Receiving a Handoff block from self-healing (a recurring verified heal with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.

97

4.40x
Quality

Does it follow best practices?

Impact

97%

4.40x

Average score across 3 eval scenarios

SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Evaluation results

100%

100%

Document Work Session Events

Multi-type self-improvement log entries

Criteria
Without this skill
With this skill

.learnings/ directory

0%

100%

Correction category

0%

100%

Learning required fields

0%

100%

Error entry exists

0%

100%

Verbatim error message

0%

100%

Reproduction steps

0%

100%

Error required fields

0%

100%

Feature request entry exists

0%

100%

Feature request required fields

0%

100%

ID format compliance

0%

100%

Area tag values

0%

100%

Priority values

0%

100%

Error metadata Reproducible field

0%

100%

92%

79%

HTTP Client Convention — Learning Capture

Promotion workflow for broadly applicable learning

Criteria
Without this skill
With this skill

.learnings directory

0%

100%

LEARNINGS.md created

0%

100%

Correct ID format

0%

100%

ISO-8601 Logged timestamp

0%

100%

All required fields present

0%

100%

Appropriate category

0%

100%

Status set to promoted

0%

100%

Promoted annotation present

0%

100%

Project memory file created

100%

100%

Concise rule in project memory

53%

46%

100%

46%

Ingest Sprint Analysis Candidates

Simplify & Harden feed ingestion and promotion decision

Criteria
Without this skill
With this skill

.learnings/ directory

100%

100%

LRN ID format

0%

100%

Required entry fields

10%

100%

Source annotation

0%

100%

Pattern-Key dead_code

37%

100%

Pattern-Key null_checks

37%

100%

Recurrence counts

77%

100%

First-Seen / Last-Seen fields

80%

100%

null_checks promoted status

80%

100%

null_checks Promoted annotation

40%

100%

dead_code NOT promoted

100%

100%

CLAUDE.md concise rule

90%

100%

Repository
pskoett/pskoett-ai-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

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.