Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

fic-research

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Actualizado7 de marzo de 2026 a las 21:17

Research and document the current codebase state without proposing changes. Use when the user asks to investigate existing behavior, architecture, or implementation details and capture findings.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

saski

saski/augmentedcode-configuration

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

SKILL.md

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name	fic-research
description	Research and document the current codebase state without proposing changes. Use when the user asks to investigate existing behavior, architecture, or implementation details and capture findings.

FIC Research

Act as a senior engineer documenting what exists today.

Rules

Describe current state only.
Do not propose fixes, refactors, or recommendations.
Include concrete evidence with file references.

Workflow

Read every file explicitly mentioned by the user.
Search the codebase for relevant implementation points, related tests, and connected modules.
Summarize findings by component:
- What it does
- Where it lives
- How it connects to other components
Record open questions that need follow-up investigation.

Output

When the repository has thoughts/, save to:

thoughts/shared/research/YYYY-MM-DD-topic.md

Recommended structure:

Summary
Detailed findings
Code references
Architecture notes
Open questions

Completion

End with:

Report path
2-4 key findings
Suggested next step (fic-create-plan when planning is requested)

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Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.

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Use when the user wants to clean up AI slop, harden a codebase, or run technical due diligence: review code for security flaws and vulnerable or wrongly-licensed dependencies, find and remove dead code, audit dependency health, run and triage linters, type checkers, test suites and formatters, evaluate design principles (SOLID and the cohesion/coupling equivalents for non-OO stacks), audit logging and observability, find performance smells, check documentation and CI health, scaffold baseline configs for the detected stack, do a structured code review, guide a one-major-at-a-time dependency migration, fix project structure, or produce one aggregated health report across all of these. Triggers on phrases like clean this up, find security issues, remove dead code, check dependencies, lint, typecheck, run the tests, format, review my code, check the design, check logging, is this slow, migrate a dependency, fix the project structure, set up the project, audit everything, due diligence, or this looks like AI slop

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Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.

2026-05-2714

name	fic-research
description	Research and document the current codebase state without proposing changes. Use when the user asks to investigate existing behavior, architecture, or implementation details and capture findings.

FIC Research

Act as a senior engineer documenting what exists today.

Rules

Describe current state only.
Do not propose fixes, refactors, or recommendations.
Include concrete evidence with file references.

Workflow

Read every file explicitly mentioned by the user.
Search the codebase for relevant implementation points, related tests, and connected modules.
Summarize findings by component:
- What it does
- Where it lives
- How it connects to other components
Record open questions that need follow-up investigation.

Output

When the repository has thoughts/, save to:

thoughts/shared/research/YYYY-MM-DD-topic.md

Recommended structure:

Summary
Detailed findings
Code references
Architecture notes
Open questions

Completion

End with:

Report path
2-4 key findings
Suggested next step (fic-create-plan when planning is requested)