Results for “fixed-income”

Build a personal budget from income and expenses using the 50/30/20 rule or a zero-based plan.

DeFi Primitives

Builds auditable firm-fixed-price IGCEs using BLS wages, layered fringe, overhead, G&A and profit, GSA CALC+ positioning, and GSA Per Diem travel data.

Build a rolling 30/60/90-day cash forecast from AR/AP aging and fixed outflows, flag floor breaches, and model liquidity levers.

Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.

Analyzes federal contract competitors, incumbents, and obligation trends using live USAspending.gov data via the usaspending MCP, supporting black-hat competitor research and contract-number-first obligation analysis.

金融经济学实证论文计划书生成器。根据用户提供的研究方向，生成包含标题、假说、数据来源、实证策略、 预期结果表格、稳健性检验、异质性分析、机制检验等12个完整模块的研究计划书。 内置中国微观/宏观数据库（皮皮侠1599个数据集、马克数据377个数据集）和WRDS国际数据库索引， 自动匹配可用数据源。融合Edmans (2024) "Learnings From 1000 Rejections"的编辑视角作为质量护栏， 确保选题具有真正的边际贡献而非"just another determinant of Y"。 当用户提到以下任何情境时触发：写研究计划书、research proposal、论文开题、选题+计划、 帮我设计一个实证研究、empirical research design、我想研究X对Y的影响怎么做、 帮我找个能发表的选题、generate proposal、写一个可以投稿的研究方案、 研究设计、identification strategy、DID/RDD/IV研究设计。 即使用户只是描述了一个经济金融现象并想知道"能不能做成论文"，也应考虑使用此技能。

Implements financial records retention requirements across EU directives (5-7 years), SOX Section 802 (7 years), MiFID II (5-7 years), tax records, payment data, and AML obligations under AMLD. Maps financial data categories to statutory retention periods with cross-jurisdictional reconciliation. Activate for financial retention, SOX records, MiFID retention, AML retention, tax record keeping queries.

Firm-fixed-price cost buildup mirroring 1102 IGCE FFP wrap-rate math (BLS OEWS + GSA CALC+ + GSA Per Diem). USE WHEN the user asks to "build an FFP IGCE", "wrap rate buildup", "FFP cost estimate", "layer fringe overhead G&A fee", "how should we price this FFP task order", or "IGCE parity for our bid". Pulls labor categories from pursuit vault or defaults, stacks fringe/overhead/G&A/fee, exports JSON + XLSX to Studio. DO NOT USE FOR T&M/LH pricing (`igce-builder-lh-tm`), cost-reimbursement (`igce-builder-cr`), OT milestones (`ot-prototype-strategist`), or obligation intel (`competitive-intel`).

Fix HOLD bias in RL reward function. Trigger when: (1) model learns to always HOLD, (2) trade rate is too low (<10%), (3) slippage penalty exceeds typical price moves.

Reviews MATLAB fixed-point (fi) code for performance, code generation efficiency, and correctness. Identifies antipatterns and suggests idiomatic improvements. Use when reviewing fi, fimath, numerictype, or quantizenumeric code.

Public finances briefing. Supports UK, US, and Australia. Produces a single compact briefing on borrowing, debt, receipts, spending, and fiscal rules. Optional debt sustainability analysis via the debtkit R package. User-selectable sub-components and multi-format export.

当意识到自身金钱观念存在限制，并希望采取建设性态度改善财务状况时

当需要在众多机会中筛选出投入产出比最高的选项，以最小化风险并最大化潜在回报时

Flamingo: A Visual Language Model for Few-Shot Learning

Monthly P&L, cashflow forecast, runway calculation, and top movers. Investor-ready Gamma deck plus Slack summary, archived to Notion. Runs monthly on a 1st-of-month cron schedule.

波总个人财务中枢 v2.0 — 债务追踪 + 消费分析 + 还款日预测 + 暴力催收。当你收到"还了XX YY元""花呗清了""我的债务""截图""消费"等财务相关指令时使用。

BISON v2.0 — Conviction Holder (Hardened). Top 10 assets by volume. All signals are score contributors — no hard gates. Scanner enters via create_position internally (Wolverine pattern). RatchetStop exits. Thesis exit REMOVED. v2.0: every hard gate converted to score contributor, ensureExecutionAsTaker=false, conviction-scaled margin 25-37%.

Offers fractional AI engineering and consulting services, acting as a part-time AI executive to generate $3K-10K per month.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Cost-benefit analysis. Produces economic NPV and financial NPV side by side, with BCR, optimism bias (with mitigation), Marginal Excess Tax Burden, real-terms rebasing, WELLBY / QALY / VPF wellbeing valuation, sensitivity, switching values, EANC for unequal-life options, validation gate, and a one-line headline verdict (socially worthwhile vs financially self-sustaining). Backed by the greenbook R package (HM Treasury Green Book primitives) when available, with graceful fallback. Supports HMT Green Book, EU Better Regulation, World Bank, ADB, and Victorian HVHR. Reads a longlist markdown file directly via --from.

根据研究者提供的**研究计划书（Research Proposal）**执行基于**外国（美国/欧盟/英国/日本/跨国）制度环境**的公司金融类实证研究全流程。**启动后第一件事：根据计划书的主题、识别策略、贡献边际与样本范围，从外国 CF 顶刊池（AER/QJE/JPE/JF/JFE/RFS/JFQA/JAR/JAE/MS/JCF/JBF 等 25+ 期刊）中推荐 5 本最匹配的目标期刊（[J1]–[J5]），等待研究者明确选定一本；该期刊决定 main.tex 的 bibliographystyle、Section 骨架、Introduction 风格与表注规范**。然后用 Python 完成数据清洗、描述性统计、基准回归、内生性检验（IV/2SLS、DML）、平行趋势、异质性、机制、稳健性检验与图表绘制。LaTeX 表格和图像严格遵循 template/ 示例格式，研究逻辑与排版严格遵循 rule/ 下的《通用实证研究逻辑与规范总结》与《回归表写作规范总结》。数据集与政策集从 asset/ 中按计划书中的关键词检索（WRDS / NBER/Fed releases / FRED / 全球宏观库）。**当计划书预期的实证结果无法实现时（系数不显著、平行趋势不通过、IV 弱工具、机制不成立等），skill 自动切换备选方案直至完成研究项目**。最终交付物：Python 代码 + LaTeX 表格 + 图像（.pdf/.png）。触发条件：研究者提交研究计划书（含 X→Y 假设、识别策略、样本、政策冲击等）。

Build a scalable outbound B2B sales machine with specialized SDR, AE, and CSM roles, using Cold Calling 2.0, referral emails, ANUM qualification, and pipeline math to generate predictable revenue.

Given a stock ticker, cost basis, and position purpose, classifies the holding scenario and returns concrete hedge recommendations (Long Put, Collar, Tier-down) with live option strikes and premiums.

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs. Use when the user says "create the architecture", "create technical architecture", "architecture spine", or "create a solution design".

Delivers a daily DeFi regime verdict (RISK-ON/NEUTRAL/RISK-OFF) from five named inputs, top TVL movers with causal reasoning, and a sustainable-vs-incentive yield split to distinguish real product-market fit from emissions-pumped APY.

Build 3-scenario financial models (Base/Bull/Bear) for startups with templates by business model, unit economics, cohort analysis, and runway calculations.

Reviews and repairs risky or unclear code with minimal validated changes, running a fixed-point loop until the diff is clean across two consecutive review cycles.

当设定个人或工作目标，希望目标本身能带来持续的内在动力和满足感时

This skill should be used when the user wants to "dca into" a token, "buy X every day", set up a "recurring buy", "dollar cost average" into an asset, "schedule a buy", or "auto-buy on a dip". Buys a fixed amount into a token on a schedule, optionally only when a condition holds (for example only when ETH is below a price threshold). The host agent's scheduler wakes the skill on a cadence; each wake is one self-contained run.

Detects unusual options activity and classifies smart money signals to help follow institutional positioning, including volume/OI ratio spikes, block trades, sweep orders, and net premium flow.

AI-powered financial analysis suite. Generates executive CFO briefings from QuickBooks exports (P&L, Balance Sheet, General Ledger, Cash Flow, etc.) with anomaly detection, burn rate, runway analysis, and scenario modeling. Also estimates codebase development costs with organizational overhead and AI ROI analysis. Triggers on: 'CFO briefing', 'financial analysis', 'cost briefing', 'expense review', 'runway analysis', 'burn rate', 'cost estimate', 'how much would this cost to build', 'development cost', 'Claude ROI'.

Tie out a fund NAV package end to end — assets, liabilities, capital, and per-share/per-unit price — proving the accounting equation, the price, and the day-over-day movement before the NAV is released.

Turn "we should run some ads" into a paid-ads plan you can hand to a buyer. Audiences, the offer per audience, the creative angles, the budget split, the metric that calls it a win, and a kill rule so you stop feeding losers. Built for B2B marketing teams, customizable to your channels and your funnel. Trigger on "build a paid ads plan", "how should we split budget", "what should the ads say", "what's our target CPL", "when do we kill a campaign", or any paid-media planning question.