Back to Skills

conscientiousness

pjt222
Updated 2 days ago
5 views
17
2
17
View on GitHub
Otherai

About

This skill systematically verifies work, checks for completeness, and ensures results match the original request before marking a task as done. It is designed for use when a response feels "good enough," after complex multi-step operations, or to counter patterns of rushing. It enforces thoroughness by having the AI review its own output against commitments, preventing corner-cutting.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/conscientiousness

Copy and paste this command in Claude Code to install this skill

Documentation

Conscientiousness

Systematic thoroughness + diligence → ensure completeness, verify results, follow through every commitment, finish tasks to standard deserved.

Use When

  • Before marking task complete → final verification pass
  • Res feels "good enough" but task deserves better
  • Post complex multi-step op where steps may have drifted
  • User req has multi parts + each needs verification
  • Before submitting code, docs, or any deliverable for user review
  • Self-monitoring detects pattern of cutting corners / rushing

In

  • Required: Task / deliverable to verify (from conv context)
  • Optional: Original user req (compare vs. what delivered)
  • Optional: Any checklist / acceptance criteria from user
  • Optional: Prior commitments during session (things promised but not checked)

Do

Step 1: Reconstruct Full Commitment

Before checking work → re-establish exactly what was committed.

  1. Re-read user's original req carefully → not interpreted version, actual words
  2. List every explicit req mentioned
  3. List every implicit commitment made during 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 from user
  5. Compare commitment list vs. what actually delivered

Full commitment list — explicit reqs + implicit promises — w/ prelim match vs. deliverables.

If err: Original req no longer in context (compressed) → reconstruct from what 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. Each item → valid. w/ evidence, not memory, actual verification:
    • Code changes: re-read file to confirm change exists
    • Test results: re-run or ref actual out
    • Docs: re-read to confirm accuracy
  2. Mark each: Done (full complete), Partial (started, incomplete), Missing (not addressed)
  3. Partial + Missing → note what remains

Every commitment has verified status. No item unchecked.

If err: Verification reveals missed items → address immediately vs. note for later. Conscientiousness = completing now, not intending to complete.

Step 3: Verify Correctness

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

  1. Each completed item → check:
    • Accuracy: Does it do what it should? Values correct?
    • Consistency: Aligns w/ rest of work? No contradictions?
    • Edge cases: Boundary conditions considered?
    • Integration: Works w/ surrounding context?
  2. Code: would this survive code review? Obvious improvements?
  3. Docs: accurate, clear, free of errs?
  4. Multi-step processes: out of each step correctly feeds next?

Each deliverable complete + correct. Errs caught before user sees them.

If err: Errs found → fix immediately. Don't present work w/ known errs, even if minor.

Step 4: Verify Presentation

Final check: deliverable presented in way serving user?

  1. Clarity: User can understand w/o re-reading multi times?
  2. Organization: Res structured logically? Related items grouped?
  3. Conciseness: Unnecessary padding / repetition?
  4. Actionability: User knows what to do next?
  5. Honesty: Limitations / caveats clearly stated?

Deliverable complete, correct, well-presented.

If err: Presentation poor despite correct content → restructure. Good work poorly presented = conscientiousness failure.

Check

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

Traps

  • Verification theater: Going through motions of checking w/o actually re-reading / re-verifying. Check must use evidence, not memory.
  • Partial conscientiousness: Checking main deliverable but ignoring side commitments ("I'll also..."). Every promise counts.
  • Perfectionism masquerading as diligence: Endless polishing delays delivery. Conscientiousness = meeting committed standard, not exceeding indefinitely.
  • Conscientiousness fatigue: Becoming less thorough as session progresses. Last task deserves same diligence as first.
  • Skip for simple tasks: Assuming simple tasks don't need verification. Simple tasks w/ errs more embarrassing than complex w/ errs.

  • honesty-humility — conscientiousness verifies completeness; honesty-humility ensures transparent reporting of what was + wasn't achieved
  • heal — subsystem assessment overlaps w/ 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 exec naturally

GitHub Repository

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

Related Skills

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

cost-optimization

Other

This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.

View skill

quantizing-models-bitsandbytes

Other

This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.

View skill

dispatching-parallel-agents

Other

This Claude Skill dispatches multiple agents to investigate and fix 3+ independent problems concurrently. It is designed for scenarios involving unrelated failures that can be resolved without shared state or dependencies. The core capability is parallel problem-solving, assigning one agent per independent problem domain to maximize efficiency.

View skill