Install
openclaw skills install @yuanzhian-patsnap/company-tech-profileSingle-company technology profile and R&D assessment for a defined technology topic. Use when the user asks for a company technology profile, company technical analysis, R&D direction assessment, technical due diligence briefing, or any single-company-by-topic technical evaluation — even if they only mention a company plus a technology area without explicitly asking for a "profile".
openclaw skills install @yuanzhian-patsnap/company-tech-profileProvided by Patsnap Eureka.
Produce an evidence-driven technology profile for a single company in a defined technology topic. The deliverable is a structured report — conclusion first, evidence second — suitable for technical leaders, analysts, and investment screeners.
competitive-landscapetech-route-comparisonrd-initiation-reviewThis skill works across multiple tool environments. Before retrieval, detect which capabilities are available and select the highest tier that is reachable.
method_decisions.md.Create or update these files in a writable run folder:
request.mdworkplan.mdmethod_decisions.mdquery_log.csvsource_index.csvclaim_ledger.csvreport.mdRecommended subfolders are described in references/workflow.md.
Confirm or infer the following before any retrieval:
Scope routing decision tree:
User input shape?
├─ Company + technology topic → proceed as single-company profile
├─ Company only, no topic → infer topic from company's main business domain;
│ confirm with user if ambiguous
├─ Multiple companies mentioned → redirect to competitive-landscape
├─ Technology topic only, no company → redirect to tech-route-comparison
└─ "Compare company A with B" → redirect to competitive-landscape
If the user only names a company without a topic, do not stall — infer the most likely technology topic from the company's known domain and state the inference explicitly.
Sampling decision tree (Tier 1 with structured retrieval):
Patent evidence shape?
├─ Scope is small and route concentration is obvious
│ └─ Skip sampler; deep-read the strongest 3-5 patents directly
├─ Scope is medium/large and route coverage matters
│ └─ Run the optional representative patent sampling script
├─ Scope is large and highly heterogeneous across time/jurisdiction/CPC
│ └─ Run the optional sampling script; treat result as default evidence pack
└─ Task is drifting toward legal completeness
└─ Do not use sampler; redirect to a legal/FTO-oriented workflow
Default thresholds:
<= 12 patents after filtering: usually skip sampler13-80 patents: sampler recommended if route interpretation is part of the goal> 80 patents: sampler should be the defaultAdjust analysis emphasis by industry type:
Industry type?
├─ Semiconductor / Hardware → emphasize: patent clusters by process/architecture,
│ fab vs fabless signals, IPC H01L/H04 distribution
├─ Biopharma / Medtech → emphasize: pipeline stage, clinical trial signals,
│ target coverage, IPC A61K/C07/A61B distribution
├─ AI / Software → emphasize: papers and benchmarks, open-source presence,
│ model performance, API/platform adoption signals
├─ New Energy / Materials → emphasize: material composition patents, capacity
│ signals, cost trajectory, IPC H01M/C01/H02 distribution
└─ General Manufacturing → balanced patent + product + standard participation
Synthesize all evidence into the output skeleton. Separate every claim into:
1-2 sentences: the company's overall technical position and trajectory in the topic.
2-4 bullet points: core technology routes, key patents or papers, differentiation from the broader field. Use a compact markdown table when comparing 3+ dimensions.
1 paragraph: R&D direction evolution over the time window — acceleration, deceleration, pivots, emerging focus areas. Cite specific year-over-year changes when data supports it.
1 set of bullet points:
Every claim must cite its source type and identifier, e.g., [Patent: CN1234567B],
[Paper: DOI or title], [Web: source name].
All must pass before delivering the final answer:
competitive-landscape.Some hosts may provide extra helpers for representative sampling, quantitative aggregation, or patent-card normalization. These helpers are optional and are not part of the core open-source contract.
If you use host-specific automation:
method_decisions.mdquery_log.csv, source_index.csv,
and claim_ledger.csv