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ai-asset-pricing: Skill for Claude Code

.claude/skills/bond-data/SKILL.md

bond-data is a skill for Claude Code from Alexander-M-Dickerson/ai-asset-pricing. It costs 49 tokens per session (1,506 once invoked), scanned A, original, MIT.

A reference guide for using the Dickerson corporate-bond dataset with PyBondLab, a Python library for bond portfolio analysis.

In plain words
What is it for?
Mapping identifiers, returns, market values, ratings, and equity links; preparing the Parquet data; and choosing the correct PyBondLab input columns.
Why use it?
Bond datasets often use different column names, rating codes, and return definitions. The guide helps match the cleaned data to what PyBondLab expects and avoids data-type and preparation errors.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is Alexander-M-Dickerson/ai-asset-pricing's own configuration. It tells Claude Code how to work on ai-asset-pricing itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-asset-pricing configures →

not rated 59repo 6mo ago A scan 49 tokens original MIT
View source ↗ Alexander-M-Dickerson/ai-asset-pricing
Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/bond-data/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricing

Made for: Claude Code.

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Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,506 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01506
Opus 5.5 $0.00020 $0.00602
Sonnet 5.5 $0.00010 $0.00301
Haiku 4.5 $0.00005 $0.00151

Measured 2mo ago against content hash 161323ecee68, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

bond-data 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.

.claude/skills/bond-data/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Bond Data Reference

The Dickerson cleaned TRACE corporate bond panel (141 columns, 2.83M rows, 1973-01 to 2025-03). Fetch via bonds-wrds-expert, stored in data/ as Parquet.

PyBondLab Column Mapping

Parquet Column PBL Name Description
cusip ID 9-digit bond CUSIP
ret_vw ret Month-end total return (primary)
mcap_e VW Bond market cap at month-end
spc_rat RATING_NUM S&P composite rating (1-22)
permno PERMNO CRSP equity link (for WithinFirmSort)

Three Mapping Approaches

# Option 1: fit() params (StrategyFormation)
result = sf.fit(IDvar='cusip', RETvar='ret_vw', VWvar='mcap_e', RATINGvar='spc_rat')

# Option 2: rename upfront
data = data.rename(columns={'cusip': 'ID', 'ret_vw': 'ret', 'mcap_e': 'VW', 'spc_rat': 'RATING_NUM'})

# Option 3: Batch columns= dict (pbl_name -> user_name)
batch = pbl.BatchStrategyFormation(
    data=data,
    columns={'ID': 'cusip', 'ret': 'ret_vw', 'VW': 'mcap_e', 'RATING_NUM': 'spc_rat'},
    ...
)

Real Data Prep

data['spc_rat'] = data['spc_rat'].astype('float64')  # nullable IntegerArray breaks numba

Alternate Return Columns

Column When to Use
ret_vw Default. Use with MMN-adjusted signals
ret_vw_bgn Use ONLY with noisy/unadjusted signals (_mmn suffix)
ret_vwx Excess return (ret_vw minus duration-matched Treasury)

VW Column: mcap_e vs mcap_s

Column Definition When to Use
mcap_e Market cap at end of month t Default. Contemporary with signal
mcap_s Market cap at end of month t-1 Lagged market cap

Rating Encoding

spc_rat: S&P composite rating (S&P first, Moody's fallback).

Numeric Rating Grade
1 AAA IG
2-4 AA+/AA/AA- IG
5-7 A+/A/A- IG
8-10 BBB+/BBB/BBB- IG
11-13 BB+/BB/BB- HY
14-16 B+/B/B- HY
17-19 CCC+/CCC/CCC- HY
20-22 CC/C/D HY/Default

Read the full file on GitHub · 116 lines

Changes

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.

  1. 2mo ago First seen · 116 lines · 49 tokens per session scan A 161323ecee68

Subscribe to this mod's changes

bond-data is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 1,506 once invoked, about $0.0002 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.

Finance and fintech
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