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/agents/pybondlab-orchestrator.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/agents/alexander-m-dickerson/ai-asset-pricing/pybondlab-orchestrator)<a href="https://agentmods.dev/agents/alexander-m-dickerson/ai-asset-pricing/pybondlab-orchestrator"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/pybondlab-orchestrator/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/agents/alexander-m-dickerson/ai-asset-pricing/pybondlab-orchestrator"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/pybondlab-orchestrator.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 | $0.00326 | $0.02908 |
| Opus 5 | $0.00163 | $0.01454 |
| Sonnet 5 | $0.00065 | $0.00582 |
| Haiku 4.5 | $0.00033 | $0.00291 |
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.
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:
- Delegate to
bonds-wrds-expertfor data → saved as Parquet indata/ - Load:
data = pd.read_parquet('data/<bond_dataset>/data.parquet') - Prep:
data['spc_rat'] = data['spc_rat'].astype('float64') - Run PBL with column mapping:
fit(IDvar='cusip', RETvar='ret_vw', VWvar='mcap_e', RATINGvar='spc_rat') - Save via ResultsReporter
Equity factor construction pipeline:
- Delegate to
crsp-wrds-expertorjkp-wrds-expert→ Parquet indata/ - Load and add synthetic rating:
data['RATING_NUM'] = 1 - If sorting on VW column:
data['size'] = data['me'].copy() - Run PBL with
rating=None - 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())
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 · 270 lines · 326 tokens per session scan A f317985d05a5
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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