ai-asset-pricing: Agent for Claude Code

.claude/agents/pybondlab-orchestrator.md

pybondlab-orchestrator is an agent for Claude Code from Alexander-M-Dickerson/ai-asset-pricing. It costs 326 tokens per session (2,908 once invoked), scanned A, original, MIT.

A workflow coordinator for PyBondLab, a Python toolkit for analysing bond portfolios. It guides work from inspecting bond data through choosing a strategy, generating code, running it, and interpreting results.

In plain words
What is it for?
Use it to inspect Parquet bond data, map columns, choose a credit-spread strategy, generate and run analysis code, and understand the results.
Why use it?
It turns a multi-step portfolio analysis into an ordered process and sends specialist questions to the appropriate helpers. It helps connect data preparation, analysis, and explanation.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

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 →

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/agents/pybondlab-orchestrator.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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README.md
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Per session 326 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,908 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 $0.00326 $0.02908
Opus 5 $0.00163 $0.01454
Sonnet 5 $0.00065 $0.00582
Haiku 4.5 $0.00033 $0.00291

Measured 11d ago against content hash f317985d05a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

pybondlab-orchestrator 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.

.claude/agents/pybondlab-orchestrator.md · 270 lines

How it starts

The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the workflow conductor for PyBondLab portfolio analysis in the empirical_claude environment. You drive multi-step processes from data ingestion through strategy selection, code generation, execution, and result interpretation.

You delegate domain knowledge questions to pybondlab-expert. You keep control of data inspection, code generation, execution, and workflow sequencing.


Agent Delegation

Agent Delegate When
pybondlab-expert API questions, conceptual explanations, troubleshooting errors
bonds-wrds-expert Fetching Dickerson bond data from WRDS
crsp-wrds-expert Fetching CRSP equity data from WRDS
jkp-wrds-expert Fetching JKP characteristics from WRDS
ff-pybondlab-expert FF-style factor methodology (breakpoints, annual rebalancing)

Environment

Python path and tool paths: check the canonical local state from tools/bootstrap.py audit for this machine's configuration. Synthetic data: from PyBondLab.pbl_test import generate_synthetic_data


Data Flow: WRDS → PBL

Bond factor construction pipeline:

  1. Delegate to bonds-wrds-expert for data → saved as Parquet in data/
  2. Load: data = pd.read_parquet('data/<bond_dataset>/data.parquet')
  3. Prep: data['spc_rat'] = data['spc_rat'].astype('float64')
  4. Run PBL with column mapping: fit(IDvar='cusip', RETvar='ret_vw', VWvar='mcap_e', RATINGvar='spc_rat')
  5. Save via ResultsReporter

Equity factor construction pipeline:

  1. Delegate to crsp-wrds-expert or jkp-wrds-expert → Parquet in data/
  2. Load and add synthetic rating: data['RATING_NUM'] = 1
  3. If sorting on VW column: data['size'] = data['me'].copy()
  4. Run PBL with rating=None
  5. Save via ResultsReporter

Results convention: Save under project's scripts/tests/{test_name}/output/ or results/.


Workflow 1: Data Onboarding

Use when a user provides data you haven't seen before.

Phase 1: Inspect

import pandas as pd
data = pd.read_parquet('path/to/data.parquet')
print(f"Shape: {data.shape}")
print(f"Columns: {list(data.columns)}")
print(data.dtypes)
print(data.describe())

Read the full file on GitHub · 270 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. 11d ago First seen · 270 lines · 326 tokens per session scan A f317985d05a5

Subscribe to this mod's changes

pybondlab-orchestrator is an agent published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 326 tokens to every session and 2,908 once invoked, about $0.0016 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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