← Back to Skills Marketplace
48h-Expert-Methodology
by
Lawliet-ai
· GitHub ↗
· v1.0.1
· MIT-0
280
Downloads
0
Stars
2
Active Installs
2
Versions
Install in OpenClaw
/install 48h-expert
Description
A meta-learning method compressing deep expertise into 48 hours by extracting core mental models, expert debates, and critical assessment questions for mastery.
README (SKILL.md)
Skill: 48h-Expert-Protocol (Cognitive-Compressor V2.1)
1. Core Assertion
System SHALL NOT output unstructured prose. All cognitive extractions MUST be serialized according to the local schema.json to ensure cross-skill interoperability.
2. Operational Phases
Phase 0: High-Authority Source Retrieval
- Mandate: Execute targeted retrieval of "Foundational Textbooks," "Peer-Reviewed Research," and "Academic Syllabi."
- Filtering: Prioritize
.edu,.gov, and high-impact industry white papers.
Phase 1: Primitive Logic Extraction
- Assertion: Deconstruct the domain into 5 Core Mental Models.
- Logic: Each model MUST facilitate the derivation of 80% of secondary field logic.
Phase 2: Dialectical Conflict Mapping
- Requirement: Isolate 3 Fundamental Schisms among top-tier experts.
- Format: Present polarized arguments with zero-bias evidentiary grounding.
Phase 3: Diagnostic Socratic Audit
- Action: Generate 10 Deep-Level Probes to detect knowledge illusions.
Phase 4: Data Serialization & Handoff (Critical)
- Action: Map all outputs from Phase 0-3 into the structured
schema.jsonformat. - Integrity Check: The resulting JSON MUST pass structural validation.
- Persistence: Write the final JSON to
~/.openclaw/swarm_tmp/expert_output.json.
3. Hard Constraints
- C1 (Chaining): Every output node MUST be referenceable by subsequent audit skills.
- C2 (Schema Compliance): Any deviation from
schema.jsonSHALL trigger a mandatory re-formatting cycle. - C3 (Deterministic Output): No conversational filler before or after the JSON payload.
Usage Guidance
This skill appears to do what it says: fetch authoritative sources, extract core mental models, and write a validated JSON file. Before installing, consider: 1) Confirm you are comfortable with the agent performing web retrievals (it may crawl or fetch many pages); ensure your environment/network policy allows this. 2) The skill will write output to ~/.openclaw/swarm_tmp/expert_output.json but did not declare that config path — decide whether creating that file is acceptable and where you want artifacts stored. 3) The skill gives no guidance on handling paywalled or copyrighted sources, and it does not mention respecting robots.txt or rate limits; if scraping is a concern, restrict or sandbox the agent. 4) Because the skill forbids unstructured prose and insists on deterministic JSON, you may lose helpful narrative explanations; verify this behavior matches your needs. 5) If you want stronger assurance, run the skill in a sandboxed environment first, inspect the generated JSON and any fetched URLs, and confirm no unexpected network endpoints are contacted.
Capability Analysis
Type: OpenClaw Skill
Name: 48h-expert
Version: 1.0.1
The skill bundle is a structured framework designed to guide an AI agent through academic research and knowledge synthesis. The instructions in SKILL.md focus on data retrieval, cognitive modeling, and serialization into a specific JSON format defined in schema.json. The only system interaction is the persistence of output to a local temporary directory (~/.openclaw/swarm_tmp/expert_output.json), which is consistent with the stated goal of cross-skill interoperability and does not exhibit signs of malicious intent, data exfiltration, or unauthorized system access.
Capability Assessment
Purpose & Capability
The name/description (compress expertise into 48 hours) matches the runtime instructions: retrieve authoritative sources, extract mental models, map conflicts, generate diagnostic probes, and serialize into schema.json. There are no unrelated env vars, binaries, or credentials requested.
Instruction Scope
SKILL.md explicitly directs web retrieval of textbooks, peer-reviewed research, and syllabi and requires producing precisely structured JSON. This is in-scope for the task, but the instructions do not constrain how retrieval is performed (e.g., no guidance about respecting robots.txt, rate limits, or handling paywalled content), and they forbid any unstructured prose output which may limit human-readable justification. The skill also mandates prioritizing .edu/.gov sources but doesn't describe citation, licensing, or attribution policies.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest installation risk. Nothing is downloaded or written by an installer step.
Credentials
No environment variables, credentials, or special binaries are requested (proportional). However, the runtime instructions require writing output to a specific path in the user's home directory (~/.openclaw/swarm_tmp/expert_output.json) even though no required config paths were declared — a minor inconsistency that should be documented and approved by the user.
Persistence & Privilege
The skill is not always-enabled and can be invoked normally. It does request persistence by writing a JSON file to the user's home directory; this is reasonable for the stated purpose but is a persistent artifact created without the skill declaring config-path requirements. It does not request system-wide or other-skills modifications.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install 48h-expert - After installation, invoke the skill by name or use
/48h-expert - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Introduced a strictly structured output format; all results must be serialized to the new `schema.json`.
- Added a new phase: Data serialization and validation, with outputs written to `~/.openclaw/swarm_tmp/expert_output.json`.
- Expanded source requirements to include government and industry white papers.
- Enforced several hard constraints: referenceability, strict schema validation, and deterministic output with no extraneous text.
- Replaced unstructured analysis with schema-driven, interoperable data for more robust downstream integration.
v1.0.0
This is a deep research plugin based on the strategy of "Meta-Learning." It forces AI to abandon superficial "summaries" and "explanations," instead focusing on 5 core thinking models, 3 fundamental points of divergence, and 10 expert-level stress test questions in the field. It is suitable for professionals who need to quickly enter new industries or new tracks.
Metadata
Frequently Asked Questions
What is 48h-Expert-Methodology?
A meta-learning method compressing deep expertise into 48 hours by extracting core mental models, expert debates, and critical assessment questions for mastery. It is an AI Agent Skill for Claude Code / OpenClaw, with 280 downloads so far.
How do I install 48h-Expert-Methodology?
Run "/install 48h-expert" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 48h-Expert-Methodology free?
Yes, 48h-Expert-Methodology is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 48h-Expert-Methodology support?
48h-Expert-Methodology is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 48h-Expert-Methodology?
It is built and maintained by Lawliet-ai (@lawliet-ai); the current version is v1.0.1.
More Skills