research-matrix-builder

Build literature matrices from papers, notes, and abstracts to compare methods, data, findings, and research gaps.

Security Scan

The skill's files, runtime instructions, and minimal python helper are consistent with building literature matrices and do not request unrelated credentials or perform hidden network/install actions.

Purpose & Capability

Name/description match the actual behavior: it normalizes records and outputs a CSV matrix. Required binary is only python3 and the bundled resource is a schema used by the workflow — all proportional to the stated purpose.

Instruction Scope

SKILL.md confines operations to normalizing inputs, producing CSV and a narrative. It references only the local script and resource and instructs preview-first behavior. It does not direct reading unrelated system files or exfiltrating data.

Install Mechanism

No install spec — instruction-only with a small local script. No downloads, no brew/npm installs, no remote URLs or extract steps. Helper script is plaintext and auditable.

Credentials

No environment variables, credentials, or config paths are requested. The single runtime dependency (python3) is appropriate for the included script.

Persistence & Privilege

Skill is not force-included (always:false) and does not request elevated or persistent privileges or modify other skills. Autonomous invocation is allowed by platform default but not unusual here.

Assessment

This skill appears coherent and low-risk: the included Python helper simply converts a user-supplied JSON list to a CSV using the provided schema. Before installing, (1) review the small script yourself to confirm it matches expectations (it's short and readable), (2) only provide non-sensitive sample data to test outputs, and (3) remember that platform agents and models may see the data you pass to the skill—avoid sending confidential material unless you trust the execution environment. If you need stricter guarantees, run the script locally yourself with your data rather than invoking it via an automated agent.

Purpose

Build literature matrices from papers, notes, and abstracts to compare methods, data, findings, and research gaps.

Trigger phrases

• 文献矩阵
• build a literature matrix
• 整理论文综述
• research gap table
• 做研究对比表

Ask for these inputs

• paper list or notes
• research question
• matrix dimensions
• citation style if needed

Workflow

1. Normalize each source into the bundled matrix schema.
2. Extract problem, method, data, metric, result, limitation, and gap.
3. Cluster similar methods and contradictory findings.
4. Generate a matrix CSV and a narrative synthesis outline.
5. Keep missing fields explicit and cite where possible.

Output contract

• literature matrix CSV
• thematic clusters
• gap summary
• review outline

Files in this skill

• Script: {baseDir}/scripts/build_matrix.py
• Resource: {baseDir}/resources/matrix_schema.csv

Operating rules

• Be concrete and action-oriented.
• Prefer preview / draft / simulation mode before destructive changes.
• If information is missing, ask only for the minimum needed to proceed.
• Never fabricate metrics, legal certainty, receipts, credentials, or evidence.
• Keep assumptions explicit.

Suggested prompts

• 文献矩阵
• build a literature matrix
• 整理论文综述

Use of script and resources

Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one.

Boundaries

• This skill supports planning, structuring, and first-pass artifacts.
• It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions.

Compatibility notes

• Directory-based AgentSkills/OpenClaw skill.
• Runtime dependency declared through metadata.openclaw.requires.
• Helper script is local and auditable: scripts/build_matrix.py.
• Bundled resource is local and referenced by the instructions: resources/matrix_schema.csv.