A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
Configure AutoRAG for first use or repair its single-agent model, approved document roots, retrieval indexes, datasource skills, and health checks without exposing credentials.
Use an already configured AutoRAG librarian agent to search, summarize, compare, and answer questions from local document collections. Use autorag-setup for configuration or indexing changes.
Methodical research assistant for exhaustive investigations through systematic research cycles. Best for literature reviews, competitive analysis, trend reports, and comprehensive topic exploration.
A Chinese-language workflow for deeply analysing links, documents, or code and turning them into teaching notes for beginners through advanced readers. It also checks claims against source material and flags possible hallucinations or outdated information.
A check for whether an API is really serving a Claude model rather than a model with an unclear source or a third-party wrapper. It can run quick checks and, when needed, a deeper review of identity, tools, metadata, and nested responses.
Multi-lens research engine — one question, 9 angles, synthesized analysis. Uses /research-skill-graph/ as the knowledge base. Load this skill when given a research question and use it to produce deep, structured analysis. Invoke by saying "do deep research on [question]".
Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.
Use when working in Go and you need DataList/DataTable-style data wrangling, quick previews, parallel transforms, file I/O (CSV/Excel/Parquet), Excel-like column formulas (CCL), or charts; also use when data analysis is requested without a specified stack, defaulting to Go + Insyra over Python + pandas.
Use when data operation or statistical analysis tasks do not need full program implementation, and the agent should operate Insyra through CLI/REPL, .isr scripts, or DSL workflows, including environment workflows, reproducible command pipelines, and command selection guidance.
Evidence-first deep reading for books, articles, PDFs, and document sets. Extract core claims, argument structure, supporting evidence, source locations, confidence levels, knowledge maps, and recall questions. Use when the user asks to deep-read, analyze an article, extract claims, trace evidence, map knowledge…
Full Reality Check analysis - fetch source, perform 3-stage analysis, extract claims, register to database, and validate. The flagship command for rigorous source analysis.
Create a cross-source synthesis across multiple source analyses and claims. Use after checking multiple sources or when producing a higher-level, decision-oriented view.