conscientiousness
Acerca de
Esta habilidad hace que Claude verifique sistemáticamente su trabajo para asegurar su exhaustividad y precisión antes de finalizar una tarea. Úsala para garantizar una ejecución minuciosa, especialmente después de operaciones complejas o cuando un resultado parece simplemente "suficientemente bueno". Impone diligencia al comprobar que no se hayan tomado atajos y confirmar que todos los compromisos en la respuesta se hayan cumplido completamente.
Instalación rápida
Claude Code
Recomendadonpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/conscientiousnessCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Conscientiousness
Systematic thoroughness and diligence — ensuring completeness, verifying results, following through on every commitment, and finishing tasks to the standard they deserve.
When to Use
- 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
Inputs
- Required: The task or deliverable to verify (available from conversation context)
- Optional: The original user request (for comparison against what was delivered)
- Optional: Any checklist or acceptance criteria provided by the user
- Optional: Prior commitments made during the session (things promised but not yet checked)
Procedure
Step 1: Reconstruct the Full Commitment
Before checking work, re-establish exactly what was committed to.
- Re-read the user's original request carefully — not the interpreted version, the actual words
- List every explicit requirement mentioned
- 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?
- Note any acceptance criteria the user provided
- Compare the commitment list against what was actually delivered
Got: A complete list of commitments — explicit requirements plus implicit promises — with a preliminary match against deliverables.
If fail: If the original request is no longer in context (compressed), reconstruct from what remains and acknowledge any gaps to the user.
Step 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 | |
+---------------------+------------------+------------------+
- 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
- Mark each item: Done (fully complete), Partial (started but incomplete), Missing (not addressed)
- For Partial and Missing items, note what remains
Got: Every commitment has a verified status. No item is left unchecked.
If fail: If verification reveals missed items, address them immediately rather than noting them for later. Conscientiousness means completing now, not intending to complete.
Step 3: Verify Correctness
Completeness is necessary but not sufficient — what was done must also be right.
- 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?
- For code: would this survive a code review? Are there obvious improvements?
- For documentation: is it accurate, clear, and free of errors?
- For multi-step processes: does the output of each step correctly feed the next?
Got: Each deliverable is both complete and correct. Errors are caught before the user sees them.
If fail: If errors are found, fix them immediately. Do not present work with known errors, even if the errors seem minor.
Step 4: Verify Presentation
The final check: is the deliverable presented in a way that serves the user?
- Clarity: Can the user understand what was done without re-reading multiple times?
- Organization: Is the response structured logically? Are related items grouped?
- Conciseness: Is there unnecessary padding or repetition?
- Actionability: Does the user know what to do next?
- Honesty: Are limitations or caveats clearly stated?
Got: A deliverable that is complete, correct, and well-presented.
If fail: If presentation is poor despite correct content, restructure. Good work poorly presented is a conscientiousness failure.
Validation
- 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
Pitfalls
- 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
Related Skills
honesty-humility— conscientiousness verifies completeness; honesty-humility ensures transparent reporting of what was and was not achievedheal— subsystem assessment overlaps with self-verification; conscientiousness focuses on deliverable qualityvishnu-bhaga— preservation of working state complements conscientiousness in maintaining qualityobserve— sustained neutral observation supports the verification processintrinsic— genuine engagement (not compliance) drives thorough execution naturally
Repositorio GitHub
Habilidades relacionadas
llamaguard
OtroLlamaGuard es el modelo de Meta de 7-8B parámetros para moderar las entradas y salidas de LLM en seis categorías de seguridad como violencia y discurso de odio. Ofrece una precisión del 94-95% y puede implementarse usando vLLM, Hugging Face o Amazon SageMaker. Utiliza esta skill para integrar fácilmente filtrado de contenido y barreras de seguridad en tus aplicaciones de IA.
cost-optimization
OtroEsta Skill de Claude ayuda a los desarrolladores a optimizar los costes en la nube mediante el ajuste de tamaño de recursos, estrategias de etiquetado y análisis de gastos. Proporciona un marco para reducir los gastos en la nube e implementar una gobernanza de costes en AWS, Azure y GCP. Úsala cuando necesites analizar los costes de infraestructura, ajustar el tamaño de los recursos o cumplir con restricciones presupuestarias.
quantizing-models-bitsandbytes
OtroEsta habilidad cuantiza LLMs a precisión de 8 o 4 bits utilizando bitsandbytes, logrando una reducción de memoria del 50-75% con pérdida mínima de precisión. Es ideal para ejecutar modelos más grandes en memoria GPU limitada o para acelerar la inferencia, admitiendo formatos como INT8, NF4 y FP4. La habilidad se integra con HuggingFace Transformers y permite entrenamiento QLoRA y optimizadores de 8 bits.
dispatching-parallel-agents
OtroEsta Skill de Claude despliega múltiples agentes para investigar y solucionar 3 o más problemas independientes de forma concurrente. Está diseñada para escenarios que involucran fallos no relacionados que pueden resolverse sin estado compartido o dependencias. Su capacidad principal es la resolución paralela de problemas, asignando un agente por cada dominio problemático independiente para maximizar la eficiencia.
