Borrowing it
Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/factor-construction/SKILL.mdgit clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/factor-construction)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/factor-construction"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/factor-construction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/factor-construction"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/factor-construction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.02655 |
| Opus 5 | $0.00027 | $0.01327 |
| Sonnet 5 | $0.00011 | $0.00531 |
| Haiku 4.5 | $0.00005 | $0.00265 |
Grade A, and why
factor-construction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factor Construction Rules
These rules prevent look-ahead bias (LAB) when forming cross-sectional factors. LAB means using information beyond the portfolio formation date — it silently inflates factor returns and invalidates results.
LAB Audit Checklist
Run this checklist whenever forming portfolios or constructing factors. Flag any violation.
1. Signal is point-in-time at formation date t
- Accounting signals use data available at t (raw Compustat: ≥4-month lag; JKP: already aligned)
- Market data (price, ME, volume) uses end-of-period t values
- Regression-based signals (beta, loading estimates) use windows ending at or before t
- No
.shift(-1)or forward-looking operation on the signal - Signal does not condition on future outcomes (e.g., future default, future delisting)
2. Universe defined at t
- Stock universe uses only t-available information (listing status, exchange, share type at t)
- Stocks that delist after t are included in the t-formation universe (you didn't know they'd delist)
- Stocks that IPO after t are excluded from the t-formation universe
- No survivorship conditioning (don't require stocks to exist for N future months)
3. Breakpoints computed at t
- If using NYSE breakpoints: NYSE membership determined at t
- Percentile cutoffs computed from the cross-section at t
- No future data in breakpoint calculation
4. Returns from t+1
- Portfolio holds from t+1 (month after formation)
- VW returns use ME at t as weights (not t+1 ME)
- Delisting returns compounded with final trading-day return in t+1
5. Output dating convention
- Portfolio return series re-dated to the earning period (see convention below)
Core Convention: Lead Returns, Not Signals
Preferred approach: Keep signals at their natural date. Lead only the portfolio return series.
Step 1: At date t, sort stocks using signal known at t
Step 2: Compute portfolio return from stock returns at t+1
Step 3: Assign this return to date t+1 (the period it was earned)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 247 lines · 54 tokens per session scan A 285833cb5dec
factor-construction is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 2,655 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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