AgentsAvançado

Academic Deep Research

porkesslerio·kesslerio· v1.0.0 · atualizado em 2026-04-10

Score

Transparent, rigorous research with full methodology—not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

academic-researchliterature-reviewdeep-researchevidence-hierarchyapa-citationresearch-agentrigorous-research

Linguagens

multi-language

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0Forks

0Usos

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Documento do Skill

SKILL.mdacademic-deep-research/workflow

Initial Engagement: — Clarify research goals with user questions.

Research Plan: — Define themes, steps, and deliverables; get user approval.

Cycle 1 Research: — Conduct initial research using web_search and web_fetch.

Cycle 1 Analysis: — Analyze findings, address contradictions, and cite sources.

Cycle 2 Research: — Perform a second research cycle to validate and expand on findings.

Cycle 2 Analysis: — Analyze new data, refine conclusions, and update citations.

Final Report: — Generate a narrative report with APA 7th citations.

Delivery: — Present the final report to the user.

Telemetria de Agentes

Execuções

total

Taxa de Sucesso

últimos 30d

Latência Média

0.0s

p50

Alucinação

0.0%

detecção

Tokens Entrada

avg 0/exec

Tokens Saída

avg 0/exec

Uso por Plataforma

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Árvore do Skill

Academic Deep Research

academic-deep-research

Fases Cognitivas6

1.SENSE

2.CONTEXTUALIZE

3.HYPOTHESIZE

4.EVALUATE

5.RECOMMEND

6.REFLECT

Triggers8

conduct a literature reviewperform deep academic researchinvestigate this topic with academic rigordo an exhaustive analysis of the literatureresearch this topic using APA 7th editionprovide a research report with citationsresearch this topic with multiple sources and evidence hierarchyperform competitive intelligence research

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Score Breakdown

⭐Avaliação Humana0%

🤖Sucesso de Agentes0%

🕐Atualidade100%

🔗Saúde de Dependências100%

🕸️Centralidade no Grafo0%

🛡️Segurança50%

CompositeScore = α·Humano + β·Agente + γ·Recência + δ·Deps + ε·Centralidade + ζ·Segurança

Instalação

$ synaptic mcp download academic-deep-research

$ synaptic skills detail academic-deep-research

$ synaptic skills live academic-deep-research

Links

GitHub Repository