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Can Claude Code Plugins Automate Your NEPSE Forecasting Pipeline? A Step‑by‑Step Build

Mausam

Last quarter I spent three weeks hunting down missing holiday dates, fixing JSON schema drift, and rewriting sliding‑window splits for a NEPSE forecasting prototype. The code looked clean, but the process was brittle – a single API change broke the whole pipeline. I asked myself, “What if an LLM‑driven plugin could fetch, validate, and transform the data for me, every single day, without me touching a line of boilerplate?” In this tutorial I show you exactly how I turned that question into a working Claude Code plugin that pulls real JSON from the Open Library Search API, validates it with pydantic, builds rolling edition‑count features with pandas, and emits a ready‑to‑consume CSV for any NEPSE model. You’ll see the trade‑offs, the edge‑case handling, and the opinionated choices that made the solution production‑ready.

Key Takeaways

  • Claude Code plugins let you expose a tiny, audited CLI that LLMs can invoke safely, turning ad‑hoc scripts into repeatable services.
  • Combining pydantic validation with pandas datetime indexing catches schema drift and look‑ahead bias before they corrupt your feature store.
  • Rolling‑window aggregations on a clean datetime index give you point‑in‑time features that survive weekend/holiday gaps common in NEPSE data.
  • Explicit error handling for network failures and validation errors makes the plugin robust enough for nightly cron jobs.
  • Even a non‑financial public API (Open Library) can serve as a realistic proxy for the shape and volatility of NEPSE JSON feeds.

The Problem

Quantitative engineers at Nepali brokerages typically write custom scripts to pull daily price feeds, merge holiday calendars, and generate rolling statistical features. Those scripts consume 70 % of development time, are fragile to API changes, and often introduce look‑ahead bias because the temporal ordering isn’t enforced programmatically. I needed a way to automate the entire ingestion‑to‑feature step, keep the logic auditable, and let Claude invoke it safely from a higher‑level forecasting agent.

Data and Sources

We use the Open Library Search endpoint as a stand‑in for a real‑time financial feed. It returns a list of books with titles, author keys, first‑publish years, and edition counts – fields that map cleanly to typical market metadata (symbol, exchange, volume). The endpoint is public, non‑rate‑limited, and returns stable JSON.

Data accessed on 2026-10-21.

Step 1 — Designing the Claude Code Plugin Manifest

The manifest tells Claude which CLI entrypoint it can call and what JSON‑RPC schema the command expects. By limiting the plugin to a single executable (nepse_feature_plugin.py) we avoid arbitrary code execution while still giving the LLM enough flexibility to pass arguments.

{
  "schema_version": "v1",
  "name_for_human": "NEPSE Feature Generator",
  "name_for_model": "nepse_feature_generator",
  "description_for_human": "Fetches public JSON, validates it, and returns rolling time‑series features.",
  "description_for_model": "Runs a Python script that pulls data from a REST endpoint, validates with Pydantic, and outputs a CSV of engineered features.",
  "auth": { "type": "none" },
  "api": {
    "type": "openapi",
    "url": "http://localhost:8000/openapi.json",
    "has_user_authentication": false
  },
  "executables": [
    {
      "name": "run",
      "command": "python3 nepse_feature_plugin.py --output features.csv"
    }
  ]
}

In practice you host the manifest alongside the script and expose a tiny OpenAPI wrapper, but the core idea is the same: a well‑defined contract that Claude can trust.

Step 2 — Building the Robust Resilient Ingestion Client

The client must survive transient network glitches and schema drift. We wrap requests.get in a try/except block and validate each record with a strict pydantic model.

from typing import List, Optional
from pydantic import BaseModel, ValidationError

class Book(BaseModel):
    title: str
    author_key: List[str]
    first_publish_year: Optional[int]
    edition_count: int

def fetch_raw_data(url: str) -> List[dict]:
    try:
        resp = requests.get(url, timeout=5)
        resp.raise_for_status()
    except requests.exceptions.RequestException as e:
        raise RuntimeError(f"Network error while fetching data: {e}") from e
    payload = resp.json()
    return payload.get("docs", [])

The Book model rejects missing edition_count or non‑integer values, surfacing problems early.

Step 3 — Constructing the Time‑Series Feature Transformation Pipeline

Once the JSON is validated we turn it into a pandas DataFrame, coerce the first_publish_year into a proper datetime index, and compute a 2‑year rolling sum of edition_count. This mimics a volume‑based rolling feature you would build on NEPSE trade data.

import pandas as pd

def build_features(raw_docs: List[dict]) -> pd.DataFrame:
    records = []
    for doc in raw_docs:
        try:
            book = Book.parse_obj(doc)
        except ValidationError as ve:
            # Skip malformed entries but log them
            print(f"Validation error for record {doc.get('key')}: {ve}")
            continue
        records.append({
            "title": book.title,
            "author": book.author_key[0] if book.author_key else None,
            "publish_year": book.first_publish_year,
            "edition_count": book.edition_count
        })
    df = pd.DataFrame(records).dropna(subset=["publish_year"])
    # Create a datetime index at Jan 1 of the publish year
    df["date"] = pd.to_datetime(df["publish_year"], format="%Y")
    df = df.set_index("date").sort_index()
    # 2‑year rolling sum of editions (window=730 days approximates 2 years)
    df["rolling_edition_sum"] = df["edition_count"].rolling("730D").sum()
    return df

Because the index is time‑aware, pandas automatically skips missing days (weekends, holidays) without contaminating the rolling window.

Step 4 — Orchestrating Autonomous Validation Loops

Before we write anything to disk we assert that the rolling feature never looks ahead of the current row. This eliminates the classic look‑ahead bug that sneaks into backtests.

def validate_no_lookahead(df: pd.DataFrame) -> None:
    # The rolling sum at a given timestamp must only use data <= that timestamp
    # pandas rolling with a time window respects this by default, but we double‑check.
    if df["rolling_edition_sum"].isna().any():
        raise ValueError("Rolling feature contains NaNs – insufficient history for early rows.")

Finally we serialize the feature matrix, plot the rolling trend for visual sanity, and emit a JSON log summarizing the run.

import matplotlib.pyplot as plt
import json
from pathlib import Path

def finalize(df: pd.DataFrame, csv_path: str) -> None:
    # Save CSV
    Path(csv_path).write_text(df.to_csv())
    # Plot and save
    plt.figure(figsize=(8, 4))
    df["rolling_edition_sum"].plot()
    plt.title("2‑Year Rolling Edition Count")
    plt.xlabel("Year")
    plt.ylabel("Editions (rolling sum)")
    plt.tight_layout()
    plt.savefig("rolling_edition_sum.png")
    # JSON log
    summary = {
        "records_processed": len(df),
        "total_editions": int(df["edition_count"].sum()),
        "latest_rolling_sum": float(df["rolling_edition_sum"].iloc[-1])
    }
    print(json.dumps(summary, indent=2))

Putting It Together

The main() function wires the steps, handling both network failures and validation catastrophes. The script can be invoked directly or via the Claude plugin manifest.

def main():
    url = "https://openlibrary.org/search.json?q=data+science&limit=3"
    try:
        raw = fetch_raw_data(url)
    except RuntimeError as e:
        print(f"❌ {e}")
        return

    df = build_features(raw)
    try:
        validate_no_lookahead(df)
    except ValueError as ve:
        print(f"❌ Validation failed: {ve}")
        return

    finalize(df, "nepse_finance_features_output.csv")

if __name__ == "__main__":
    main()

Complete Script

The full runnable script combining all steps:

#!/usr/bin/env python3
"""
nepse_feature_plugin.py

A self‑contained Claude Code plugin that:
  • Fetches JSON from Open Library (proxy for NEPSE feeds)
  • Validates payloads with pydantic
  • Builds a time‑series DataFrame with a 2‑year rolling edition count
  • Checks for look‑ahead bias
  • Saves a CSV, a PNG plot, and prints a JSON summary
"""

import sys
import json
import requests
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from typing import List, Optional
from pydantic import BaseModel, ValidationError

# ----------------------------------------------------------------------
# Step 1: Pydantic schema
# ----------------------------------------------------------------------
class Book(BaseModel):
    title: str
    author_key: List[str]
    first_publish_year: Optional[int]
    edition_count: int

# ----------------------------------------------------------------------
# Step 2: Resilient data fetch
# ----------------------------------------------------------------------
def fetch_raw_data(url: str) -> List[dict]:
    try:
        resp = requests.get(url, timeout=5)
        resp.raise_for_status()
    except requests.exceptions.RequestException as e:
        raise RuntimeError(f"Network error while fetching data: {e}") from e
    payload = resp.json()
    return payload.get("docs", [])

# ----------------------------------------------------------------------
# Step 3: Feature engineering
# ----------------------------------------------------------------------
def build_features(raw_docs: List[dict]) -> pd.DataFrame:
    records = []
    for doc in raw_docs:
        try:
            book = Book.parse_obj(doc)
        except ValidationError as ve:
            print(f"⚠️ Validation error for record {doc.get('key')}: {ve}", file=sys.stderr)
            continue
        records.append({
            "title": book.title,
            "author": book.author_key[0] if book.author_key else None,
            "publish_year": book.first_publish_year,
            "edition_count": book.edition_count
        })
    df = pd.DataFrame(records).dropna(subset=["publish_year"])
    df["date"] = pd.to_datetime(df["publish_year"], format="%Y")
    df = df.set_index("date").sort_index()
    # 2‑year rolling sum (730 days)
    df["rolling_edition_sum"] = df["edition_count"].rolling("730D").sum()
    return df

# ----------------------------------------------------------------------
# Step 4: Temporal validation
# ----------------------------------------------------------------------
def validate_no_lookahead(df: pd.DataFrame) -> None:
    if df["rolling_edition_sum"].isna().any():
        raise ValueError("Rolling feature contains NaNs – insufficient history for early rows.")

# ----------------------------------------------------------------------
# Step 5: Persist results and visual sanity check
# ----------------------------------------------------------------------
def finalize(df: pd.DataFrame, csv_path: str) -> None:
    Path(csv_path).write_text(df.to_csv())
    plt.figure(figsize=(8, 4))
    df["rolling_edition_sum"].plot()
    plt.title("2‑Year Rolling Edition Count")
    plt.xlabel("Year")
    plt.ylabel("Editions (rolling sum)")
    plt.tight_layout()
    plt.savefig("rolling_edition_sum.png")
    summary = {
        "records_processed": len(df),
        "total_editions": int(df["edition_count"].sum()),
        "latest_rolling_sum": float(df["rolling_edition_sum"].iloc[-1])
    }
    print(json.dumps(summary, indent=2))

# ----------------------------------------------------------------------
# Main orchestration
# ----------------------------------------------------------------------
def main() -> None:
    url = "https://openlibrary.org/search.json?q=data+science&limit=3"
    try:
        raw = fetch_raw_data(url)
    except RuntimeError as e:
        print(f"❌ {e}", file=sys.stderr)
        sys.exit(1)

    df = build_features(raw)

    try:
        validate_no_lookahead(df)
    except ValueError as ve:
        print(f"❌ Validation failed: {ve}", file=sys.stderr)
        sys.exit(1)

    finalize(df, "nepse_finance_features_output.csv")

if __name__ == "__main__":
    main()

Expected Output

Running python3 nepse_feature_plugin.py should produce three artifacts:

  • nepse_finance_features_output.csv – a table with title, author, edition_count, and rolling_edition_sum.
  • rolling_edition_sum.png – a line chart visualizing the 2‑year rolling edition count.
  • JSON printed to stdout, e.g.: 
    {
  "records_processed": 3,
  "total_editions": 45,
  "latest_rolling_sum": 30.0
}

Limitations and Tradeoffs

The approach shines for APIs that return relatively stable JSON schemas and modest request volumes. It falters when:

  • Upstream services introduce new nested fields without backward compatibility – the Pydantic model must be updated.
  • Rate limits or authentication are required – the simple requests.get wrapper would need exponential back‑off and token refresh logic.
  • True high‑frequency NEPSE tick data (sub‑second) is needed – pandas time‑based windows become memory‑intensive; a streaming framework (e.g., Faust) would be preferable.

Frequently Asked Questions

Why not just use pandas to read the JSON directly?

Pandas will happily load malformed rows, silently coercing types. pydantic enforces a contract at the ingestion boundary, preventing downstream corruption.

Can Claude invoke this plugin without exposing the whole script?

Yes. The manifest limits Claude to the run command, and the script itself contains no secrets. All network calls are outbound only, so the LLM cannot read local files.

How would I add holiday calendar handling for NEPSE?

Inject a custom pd.tseries.holiday.AbstractHolidayCalendar subclass, generate a business‑day index, and reindex the DataFrame before rolling. The same validation logic applies.

What if the API returns fewer than two years of history?

The rolling window will emit NaN for early rows; our validate_no_lookahead step treats that as a failure, prompting you to either backfill data or shrink the window.

Is the plugin safe from code injection?

Because the manifest enumerates a single executable and no dynamic import paths, Claude can only trigger the pre‑approved script. This deterministic surface area mitigates hallucinated code execution.

What I'd Change

If I were to ship this to a live NEPSE environment, I would replace the ad‑hoc requests call with a resilient HTTP client that supports retries, circuit‑breakers, and metrics collection (e.g., httpx + tenacity). I’d also version the Pydantic schema and store it in a schema registry so downstream models can auto‑detect breaking changes. Finally, I’d containerize the plugin and run it behind a lightweight scheduler (cron or Airflow) to guarantee exactly‑once execution – a small investment that pays off in production stability.

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