llmquant-credit
Router skill for LLMQuant credit workflows. Use when the user needs issuer credit review, spread regime analysis, high-yield stress monitoring, default risk, debt maturity, or covenant context.
npx skills add LLMQuant/skills --skill llmquant-credit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLMQuant Credit This category routes credit research workflows for issuer risk, spread regimes, and high-yield stress. ## Routing Rules 1. Identify issuer, ticker, bond, index, sector, maturity bucket, credit rating, and horizon. 2. Select the closest workflow below. 3. Open only that workflow and any referenced local resources. 4. Use LLMQuant Data for filings, debt schedule, fundamentals, rates, spreads, ratings, equity prices, CDS, and macro context. 5. Report filing dates, market timestamps, rating dates, observation windows, stale notices, and missing inputs. ## Workflow Index | User intent | Workflow | |---|---| | Review an issuer's balance-sheet, cash-flow, maturity, and covenant credit risk. | [`workflows/issuer-credit-risk-review.md`](workflows/issuer-credit-risk-review.md) | | Diagnose credit-spread regime, risk appetite, and sector pressure. | [`workflows/credit-spread-regime.md`](workflows/credit-spread-regime.md) | | Monitor high-yield stress, refinancing risk, fallen angels, and default pressure. | [`workflows/high-yield-stress-monitor.md`](workflows/high-yield-stress-monitor.md) | ## LLMQuant Data Contract Prefer LLMQuant Data when available. The workflows may need
- Routing Rules
- Workflow Index
- LLMQuant Data Contract
What does the llmquant-credit skill do?
Router skill for LLMQuant credit workflows. Use when the user needs issuer credit review, spread regime analysis, high-yield stress monitoring, default risk, debt maturity, or covenant context.
How do I install it?
Run `npx skills add LLMQuant/skills --skill llmquant-credit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LLMQuant/skills, a repository with 183 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.