返回技能列表

conscientiousness

pjt222
更新于 Yesterday
2 次查看
17
2
17
在 GitHub 上查看
其他ai

关于

This skill enables Claude to systematically verify its work, ensuring thoroughness and preventing corner-cutting before finalizing tasks. It's designed for use when completing complex operations, when outputs feel incomplete, or when self-monitoring detects rushed patterns. The skill focuses on completeness verification and follow-through to guarantee deliverables match promises.

快速安装

Claude Code

推荐
主要方式
npx skills add pjt222/agent-almanac -a claude-code
插件命令备选方式
/plugin add https://github.com/pjt222/agent-almanac
Git 克隆备选方式
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/conscientiousness

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

Conscientiousness

Systematic thorough, diligent — ensure complete, verify results, follow through every commitment, finish task to standard deserved.

When Use

  • Before mark task complete — final verification pass
  • Response feel "good enough" but task deserve better
  • After complex multi-step op — steps may have drifted
  • User request has many parts — each part need verify
  • Before submit code, docs, any deliverable for user review
  • Self-monitor detect pattern of cutting corners, rushing

Inputs

  • Required: Task or deliverable to verify (from conversation context)
  • Optional: Original user request (compare vs what delivered)
  • Optional: Checklist or acceptance criteria from user
  • Optional: Prior commitments during session (promises not yet checked)

Steps

Step 1: Reconstruct Full Commitment

Before check work, re-establish what was committed.

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

Got: Complete list of commitments — explicit requirements plus implicit promises — preliminary match vs deliverables.

If fail: Original request no longer in context (compressed)? Reconstruct from remains, acknowledge gaps to user.

Step 2: Verify Completeness

Check every committed item 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 file to confirm change exists
    • Test results: re-run or reference actual output
    • Docs: re-read to confirm accuracy
  2. Mark each item: Done (fully complete), Partial (started but incomplete), Missing (not addressed)
  3. For Partial, Missing items: note what remains

Got: Every commitment has verified status. No item left unchecked.

If fail: Verification reveals missed items? Address immediately — not note for later. Conscientiousness means complete now, not intend to complete.

Step 3: Verify Correctness

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

  1. For each completed item, check:
    • Accuracy: Does what should? Values correct?
    • Consistency: Aligns with rest of work? No contradictions?
    • Edge cases: Boundary conditions considered?
    • Integration: Works with surrounding context?
  2. For code: survive code review? Obvious improvements?
  3. For docs: accurate, clear, free of errors?
  4. For multi-step: output of each step correctly feeds next?

Got: Each deliverable complete and correct. Errors caught before user sees.

If fail: Errors found? Fix immediately. Do not present work with known errors, even if errors seem minor.

Step 4: Verify Presentation

Final check: deliverable presented in way that serves user?

  1. Clarity: User understand what done without re-reading many times?
  2. Organization: Response structured logical? Related items grouped?
  3. Conciseness: Unnecessary padding or repetition?
  4. Actionability: User know what to do next?
  5. Honesty: Limitations, caveats clearly stated?

Got: Deliverable complete, correct, well-presented.

If fail: Presentation poor despite correct content? Restructure. Good work poorly presented is conscientiousness failure.

Checks

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

Pitfalls

  • Verification theater: Going through motions of checking without actual re-read or re-verify. Check must use evidence, not memory
  • Partial conscientiousness: Check main deliverable but ignore side commitments ("I'll also..."). Every promise counts
  • Perfectionism masquerading as diligence: Endless polishing delays delivery. Conscientiousness means meet committed standard, not exceed indefinitely
  • Conscientiousness fatigue: Become less thorough as session progresses. Last task deserves same diligence as first
  • Skip for simple tasks: Assume simple tasks don't need verification. Simple tasks with errors more embarrassing than complex tasks with errors

See Also

  • honesty-humility — conscientiousness verifies completeness; honesty-humility ensures transparent reporting of what achieved, what not
  • 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 verification process
  • intrinsic — genuine engagement (not compliance) drives thorough execution naturally

GitHub 仓库

pjt222/agent-almanac
路径: i18n/caveman/skills/conscientiousness
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

相关推荐技能

llamaguard

其他

LlamaGuard是Meta推出的7-8B参数内容审核模型,专门用于过滤LLM的输入和输出内容。它能检测六大安全风险类别(暴力/仇恨、性内容、武器、违禁品、自残、犯罪计划),准确率达94-95%。开发者可通过HuggingFace、vLLM或Sagemaker快速部署,并能与NeMo Guardrails集成实现自动化安全防护。

查看技能

cost-optimization

其他

这个Claude Skill帮助开发者优化云成本,通过资源调整、标记策略和预留实例来降低AWS、Azure和GCP的开支。它适用于减少云支出、分析基础设施成本或实施成本治理策略的场景。关键功能包括提供成本可视化、资源规模调整指导和定价模型优化建议。

查看技能

quantizing-models-bitsandbytes

其他

这个Skill使用bitsandbytes库量化大语言模型,能在GPU内存有限时通过8位或4位量化减少50-75%内存占用,同时保持精度损失最小。它支持INT8、NF4、FP4等多种量化格式,可与HuggingFace Transformers无缝集成,适用于需要部署更大模型或加速推理的场景。还提供QLoRA训练和8位优化器支持,让开发者能轻松实现高效模型压缩。

查看技能

dispatching-parallel-agents

其他

该Skill用于并行处理3个以上无依赖关系的独立故障,可为每个问题域分派专属Claude代理同时执行调查修复。它通过并发处理多个独立问题显著提升故障排查效率,特别适用于测试文件、子系统等无共享状态的场景。

查看技能