본문으로 건너뛰기
cccskills
무료GitHub에 공개

weak-area-tracker

Use when logging, scoring, and triaging a learner's persistent weak areas to drive intervention selection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(3)

  • SKILL.md3.4 KB
  • examples.md1.8 KB
  • README.md765 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Purpose

Maintain a prioritized, evidence-based log of topics where the learner consistently struggles. Drives which skills to activate next and prevents ignoring recurring problems in favor of always-new content.

Activation

  • check-understanding, challenge-generator, or misconception-detector flags a repeated error. Returning learner with prior weak areas. Planning a study/revision session. Learner reports a covered topic is still unclear.
  • Skip if: first encounter with a topic (not a weak area yet). Error is a one-time slip. Learner declines tracking.
  • Routing: feed into lesson-plan and revision-mode. Use tracker to decide between challenge-generator (reinforce) vs teach-concept (re-teach) vs socratic-mode (deepen). Mark confirmed improvements and remove from active tracking.

Inputs

  • Error event (topic, type, session date, recurrence count), prior weak-area log, learner's current goals.

Severity Scoring

Score each weak area 1–5: score = recurrence × type_weight + staleness_bonus

  • Type weights: Surface=1, Structural=2, Deep=3
  • Staleness: +1 if not addressed in 3+ sessions

Intervention selection: Score 1–2 → challenge-generator. Score 3–4 → teach-concept re-teach. Score 5 → socratic-mode then misconception-detector.

Workflow

  1. Ingest — New error: add to log (recurrence=1) or increment existing entry + update last_seen.
  2. Score — Compute severity from recurrence, type, staleness, and self-reported confidence.
  3. Triage — Rank by score. Surface top 2 for current session. Max 2 weak areas per session.
  4. Select Intervention — Match score to appropriate skill (see above).
  5. Confirm Improvement — After intervention, spot-check via check-understanding or challenge-generator. 2 consecutive clean passes → "resolving." 3 consecutive → "resolved" and archived. Regression after resolution → re-open.
  6. Maintain — Cap active list at 5. Archive resolved items. Warn if topic persists 5+ sessions without resolution.

Rules

  • DO: ground weak areas in observed evidence, not assumptions.
  • DO: update scores after every session where the topic appears.
  • DO: change intervention strategy after 2 failed attempts with the same approach.
  • DO: require 2+ consecutive clean passes for resolution.
  • DO: frame output as "what we'll work on" not "what you're bad at."
  • DON'T: accumulate unbounded — cap at 5 active items.
  • DON'T: mark resolved after one good session — require consecutive passes.
  • DON'T: repeat the same intervention 3+ times without changing approach.

Output

Responses should contain: event details (topic + error type + source skill), action taken (added/incremented), severity score, active weak areas table (topic, type, recurrence, score, recommended intervention), session recommendation (top 1–2 focus areas), and resolution updates if applicable. Format naturally.

Checklist

  • Active list capped at 5 items.
  • Severity scores computed using recurrence + type + staleness.
  • Intervention matches severity score.
  • Resolution requires 2+ consecutive clean passes.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

Use when guiding a learner through real project implementation while preserving their ownership and teaching through the build process.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

Use when verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

Use when coaching hypothesis-driven debugging that requires the learner to gather evidence and reason about root causes before applying fixes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

deep-dive

무료

Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

yugash007/edu-agent-skills102026년 5월 19일 업데이트

yugash007의 스킬 모두 보기

이 스킬의 문제 신고