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Tech Solution Research
by
_silhouette
· GitHub ↗
· v0.3.0
· MIT-0
122
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0
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0
Active Installs
1
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Install in OpenClaw
/install tech-solution-research
Description
技术方案调研/框架选型/技术对比/最终报告生成 — multi-source evidence orchestration for technical decision-making
Usage Guidance
This skill is coherent and appears to do what it says: orchestrate multi-source technical research and generate a structured report. Before installing or invoking it, check the following: 1) Confirm which platform-native connectors/CLIs (GitHub/gh, feedgrab, xiaohongshu, agent-browser, moltbook, ClawHub, etc.) your agent has access to and whether those connectors require credentials you consider sensitive. 2) If you expect the skill to use internal documents (internal-assets lane), ensure those accesses are intentional and governed by your access controls. 3) Runtime tests imply executing scripts or commands — review how test artifacts, logs, and credentials will be stored or transmitted. 4) Because the SKILL.md enforces specific 'must use' data sources, verify each referenced skill/connector is trusted; otherwise the agent may fall back to alternative sources and must record that downgrade. If any of the above is unacceptable, restrict or audit the agent's connector permissions before using this skill.
Capability Analysis
Type: OpenClaw Skill
Name: tech-solution-research
Version: 0.3.0
The 'Tech Solution Research' skill bundle is a highly structured framework for technical evaluation and decision-making. It implements a 'Multi-Source Evidence Orchestration' workflow across files like SKILL.md and workflow.md, requiring the agent to cross-reference data from GitHub, official docs, and social platforms using specific tools (e.g., feedgrab, gh CLI). The bundle includes comprehensive guardrails against biased reporting, explicit instructions for conflict resolution, and a detailed scoring rubric (scoring-rubric.md). No indicators of malicious intent, data exfiltration, or harmful prompt injection were found; the instructions are professional and strictly aligned with the stated purpose of technical research.
Capability Assessment
Purpose & Capability
The name/description (technical solution research) match the SKILL.md: it defines multi-source evidence lanes, scoring, runtime tests and a report template. The listed source lanes (official docs, GitHub, platform-native, community, runtime tests, internal-assets, ClawHub) are appropriate for technical evaluation. Nothing requested is out-of-scope for a technical research skill.
Instruction Scope
The instructions strictly prescribe data collection, evidence schema, test/score/templating and 'must use' platform-native skills (feedgrab, xiaohongshu, gh/gh CLI, agent-browser, moltbook-global, ClawHub registry, etc.). That is coherent with the goal, but the skill assumes the agent will call other skills or run runtime tests — which may invoke network calls, CLIs, or execute test scripts. The SKILL.md does not instruct reading unrelated host files or environment variables, but it does include an 'internal-assets' lane that implies access to internal docs/code if available — confirm that such access is intentional and permissioned.
Install Mechanism
No install spec and no code files (instruction-only). This minimizes supply-chain risk: nothing will be downloaded or written by the skill itself.
Credentials
The skill declares no required env vars or credentials, but its runtime rules assume availability of platform-native connectors and CLIs (GitHub/gh, feedgrab, agent-browser, xiaohongshu, moltbook, ClawHub). Those connectors typically need credentials or elevated access. It's normal for a research skill to use such tools, but the skill does not declare or constrain them — verify what credentials the agent already has and whether exposing them to these evidence-collection steps is acceptable.
Persistence & Privilege
always:false and no install lifecycle actions. The skill does not request permanent inclusion or write to other skills' configs. Autonomous invocation is allowed by default (platform normal) but not combined with other red flags here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install tech-solution-research - After installation, invoke the skill by name or use
/tech-solution-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.0
Initial public release with platform-native source routing, source coverage gates, and technical-solution research workflow.
Metadata
Frequently Asked Questions
What is Tech Solution Research?
技术方案调研/框架选型/技术对比/最终报告生成 — multi-source evidence orchestration for technical decision-making. It is an AI Agent Skill for Claude Code / OpenClaw, with 122 downloads so far.
How do I install Tech Solution Research?
Run "/install tech-solution-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Tech Solution Research free?
Yes, Tech Solution Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Tech Solution Research support?
Tech Solution Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Tech Solution Research?
It is built and maintained by _silhouette (@lanyasheng); the current version is v0.3.0.
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