MCP HubMCP Hub
SKILL·8087C2

conscientiousness

pjt222
업데이트됨 1 month ago
9 조회
26
3
26
GitHub에서 보기
기타ai

정보

이 스킬은 클로드가 자신의 작업을 체계적으로 검증하여, 과제를 완료하기 전에 철저성을 보장하고 부실한 처리 방지합니다. 이는 응답이 불완전하게 느껴질 때, 복잡한 다단계 작업 후, 또는 자체 모니터링에서 급하게 처리된 부분이 감지되었을 때 사용하도록 설계되었습니다. 핵심 기능은 완성도 확인, 결과 검증, 그리고 최종 산출물이 원래 요청 사항과 일치함을 보장하는 것입니다.

빠른 설치

Claude Code

추천
기본
npx skills add pjt222/agent-almanac -a claude-code
플러그인 명령대체
/plugin add https://github.com/pjt222/agent-almanac
Git 클론대체
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/conscientiousness

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

誠実性

Systematic thoroughness and diligence — ensuring completeness, verifying results, following through on every commitment, and finishing tasks to the standard they deserve.

使用タイミング

  • Before marking a task as complete — as a final verification pass
  • When a response feels "good enough" but the task deserves better
  • After a complex multi-step operation where individual steps may have drifted
  • When the user's request has multiple parts and each part needs verification
  • Before submitting code, documentation, or any deliverable for user review
  • When self-monitoring detects a pattern of cutting corners or rushing

入力

  • 必須: The task or deliverable to verify (available from conversation context)
  • 任意: The original user request (for comparison against what was delivered)
  • 任意: Any checklist or acceptance criteria provided by the user
  • 任意: Prior commitments made during the session (things promised but not yet checked)

手順

ステップ1: Reconstruct the Full Commitment

Before checking work, re-establish exactly what was committed to.

  1. Re-read the user's original request carefully — not the interpreted version, the actual words
  2. List every explicit requirement mentioned
  3. List every implicit commitment made during the session:
    • "I'll also update the tests" — was this done?
    • "Let me fix that too" — was this completed?
    • "I'll check for edge cases" — were they checked?
  4. Note any acceptance criteria the user provided
  5. Compare the commitment list against what was actually delivered

期待結果: A complete list of commitments — explicit requirements plus implicit promises — with a preliminary match against deliverables.

失敗時: If the original request is no longer in context (compressed), reconstruct from what remains and acknowledge any gaps to the user.

ステップ2: Verify Completeness

Check that every committed item was addressed.

Completeness Matrix:
+---------------------+------------------+------------------+
| Commitment          | Status           | Evidence         |
+---------------------+------------------+------------------+
| [Requirement 1]     | Done / Partial / | [How verified]   |
|                     | Missing          |                  |
+---------------------+------------------+------------------+
| [Requirement 2]     | Done / Partial / | [How verified]   |
|                     | Missing          |                  |
+---------------------+------------------+------------------+
| [Promise 1]         | Done / Partial / | [How verified]   |
|                     | Missing          |                  |
+---------------------+------------------+------------------+
  1. For each item, verify with evidence — not memory, actual verification:
    • Code changes: re-read the file to confirm the change exists
    • Test results: re-run or reference the actual output
    • Documentation: re-read to confirm accuracy
  2. Mark each item: Done (fully complete), Partial (started but incomplete), Missing (not addressed)
  3. For Partial and Missing items, note what remains

期待結果: Every commitment has a verified status. No item is left unchecked.

失敗時: If verification reveals missed items, address them immediately rather than noting them for later. Conscientiousness means completing now, not intending to complete.

ステップ3: Verify Correctness

Completeness is necessary but not sufficient — what was done must also be right.

  1. For each completed item, check:
    • Accuracy: Does it do what it should? Are values correct?
    • Consistency: Does it align with the rest of the work? No contradictions?
    • Edge cases: Were boundary conditions considered?
    • Integration: Does it work with the surrounding context?
  2. For code: would this survive a code review? Are there obvious improvements?
  3. For documentation: is it accurate, clear, and free of errors?
  4. For multi-step processes: does the output of each step correctly feed the next?

期待結果: Each deliverable is both complete and correct. Errors are caught before the user sees them.

失敗時: If errors are found, fix them immediately. Do not present work with known errors, even if the errors seem minor.

ステップ4: Verify Presentation

The final check: is the deliverable presented in a way that serves the user?

  1. Clarity: Can the user understand what was done without re-reading multiple times?
  2. Organization: Is the response structured logically? Are related items grouped?
  3. Conciseness: Is there unnecessary padding or repetition?
  4. Actionability: Does the user know what to do next?
  5. Honesty: Are limitations or caveats clearly stated?

期待結果: A deliverable that is complete, correct, and well-presented.

失敗時: If presentation is poor despite correct content, restructure. Good work poorly presented is a conscientiousness failure.

バリデーション

  • The original request was re-read (not recalled from memory)
  • Every explicit requirement was verified with evidence
  • Every implicit promise was tracked and verified
  • Correctness was checked beyond mere completeness
  • Edge cases were considered where relevant
  • The deliverable is clearly presented and actionable

よくある落とし穴

  • Verification theater: Going through the motions of checking without actually re-reading or re-verifying. The check must use evidence, not memory
  • Partial conscientiousness: Checking the main deliverable but ignoring side commitments ("I'll also..."). Every promise counts
  • Perfectionism masquerading as diligence: Endless polishing that delays delivery. Conscientiousness is about meeting the committed standard, not exceeding it indefinitely
  • Conscientiousness fatigue: Becoming less thorough as the session progresses. The last task deserves the same diligence as the first
  • Skipping for simple tasks: Assuming simple tasks don't need verification. Simple tasks with errors are more embarrassing than complex tasks with errors

関連スキル

  • honesty-humility — conscientiousness verifies completeness; honesty-humility ensures transparent reporting of what was and was not achieved
  • heal — subsystem assessment overlaps with self-verification; conscientiousness focuses on deliverable quality
  • vishnu-bhaga — preservation of working state complements conscientiousness in maintaining quality
  • observe — sustained neutral observation supports the verification process
  • intrinsic — genuine engagement (not compliance) drives thorough execution naturally

GitHub 저장소

pjt222/agent-almanac
경로: i18n/ja/skills/conscientiousness
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams
FAQ

Frequently asked questions

What is the conscientiousness skill?

conscientiousness is a Claude Skill by pjt222. Skills package instructions and resources that Claude loads on demand, so Claude can perform conscientiousness-related tasks without extra prompting.

How do I install conscientiousness?

Use the install commands on this page: add conscientiousness to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.

What category does conscientiousness belong to?

conscientiousness is in the Other category, tagged ai.

Is conscientiousness free to use?

Yes. conscientiousness is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.

연관 스킬

llamaguard
기타

LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.

스킬 보기
cost-optimization
기타

이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.

스킬 보기
sports-betting-analyzer
기타

이 Claude Skill은 스프레드, 오버/언더, 프로프 베트를 포함한 스포츠 베팅 시장을 분석합니다. 역사적 추이와 상황별 통계를 검토하여 가치 베트를 발견하고, 교육적 목적으로 실행 가능한 권장 사항이 담긴 구조화된 마크다운 결과를 제공합니다. 개발자는 이 기능을 스포츠 베팅 분석 도구에 활용할 수 있으며, 단순히 엔터테인먼트/교육 목적으로만 설계되었음을 유의해야 합니다.

스킬 보기
quantizing-models-bitsandbytes
기타

이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.

스킬 보기