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/panel-data-rules/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/panel-data-rules)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/panel-data-rules"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/panel-data-rules/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/panel-data-rules"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/panel-data-rules.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 | $0.00068 | $0.02251 |
| Opus 5.5 | $0.00027 | $0.00900 |
| Sonnet 5.5 | $0.00014 | $0.00450 |
| Haiku 4.5 | $0.00007 | $0.00225 |
Grade A, and why
panel-data-rules 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 2mo 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Financial Panel Data Rules
Apply these rules whenever writing code that manipulates CRSP, Compustat, OptionMetrics, or any financial panel data. These are hard-won lessons — violating them causes silent data errors.
Rule 1: Safe Lagging/Leading (MANDATORY)
Every groupby().shift() and .diff() MUST be followed by a date gap check. Data is rarely contiguous — missing quarters, fiscal year changes, and delistings create gaps. A bare shift(1) will silently pair non-adjacent observations.
After any shift(k), auto-insert this validation:
df["_date_shifted"] = df.groupby(id_col)[date_col].shift(k)
day_gap = (df[date_col] - df["_date_shifted"]).dt.days
df.loc[(day_gap > max_gap) | (day_gap < min_gap), shifted_col] = np.nan
df.drop(columns=["_date_shifted"], inplace=True)
Thresholds by data source (validated against real WRDS data):
| Source | shift(1) max gap | shift(1) min gap | Notes |
|---|---|---|---|
CRSP msf/msf_v2 (month-end normalized) |
31 days | 28 days | 2.26M obs: max gap = 31 days exactly (v2: mthcaldt) |
CRSP msf/msf_v2 (raw trading dates) |
33 days | 28 days | Raw dates vary by last trading day |
Compustat fundq |
100 days | 80 days | 99.89% in [90,92]; >100 = missing quarter |
Compustat funda |
380 days | 350 days | <350 = FYE change (correctly flagged) |
CRSP dsf/dsf_v2 |
5 days | 0 days | Weekends/holidays (v2: dlycaldt) |
For shift(k): max_gap = k * single_max, min_gap = k * single_min.
Infer frequency from data source (table name, column names). Columns ending in q (ibq, atq, seqq) → quarterly. If source unknown, ask the user.
CRSP month-end normalization (do this FIRST, before any gap checks or merges):
df["date"] = df["date"].dt.to_period("M").dt.to_timestamp("M")
This converts raw trading dates to calendar month-end. Required for: (1) gap checks to use the 31-day threshold, (2) matching CRSP to Compustat/JKP data, (3) consistent date joins across datasets.
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.
- 2mo ago First seen · 150 lines · 68 tokens per session scan A 6488bb39eac1
panel-data-rules is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 2,251 once invoked, about $0.0003 per session on Opus 5.5. 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.
panel-data-rules compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| panel-data-rulesAlexander-M-Dickerson/ai-asset-pricing | Skill | 59 | 6mo ago | MIT | A | 68 | Claude Code |
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