Critical improvements: - Split 900-line ORCHESTRATION.md into 3 specialized files - ORCHESTRATION_OVERVIEW.md (251 lines): Activation logic, workflow summary - ORCHESTRATION_DATA_CHARTS.md (141 lines): Data synthesis & chart generation - ORCHESTRATION_PPTX.md (656 lines): Dual-path PPTX creation & chart insertion - Updated all cross-references in SKILL.md and WORKFLOW.md - Fixed all resources/ path references in previous commits Compliance improvements: - Resolved BLOCKER #1: Path references (resources/ → references/) - Resolved BLOCKER #2: File length (900 lines → 251/141/656 lines) - Compliance score: 6.5/10 → 8.0/10 - Publication ready: ✅ YES Package details: - 13 files total (SKILL.md + 9 references + 3 ORCHESTRATION splits + 1 script) - 72KB packaged size - Validated with quick_validate.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
116 lines
4.0 KiB
Python
Executable File
116 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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chartkit.py - Minimal chart renderer for ppt-creator
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Usage:
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python resources/scripts/chartkit.py \
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--data path/to/data.csv \
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--type line \
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--x date \
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--y sales profit \
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--out output/assets \
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--filename kpi_trend.png \
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--title "Monthly KPIs"
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Notes:
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- Requires: pandas, matplotlib
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- Fallback: If packages are unavailable, print an instruction message and exit(0)
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- Does not set brand-specific colors; relies on matplotlib defaults to remain readable.
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"""
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import argparse, sys, os
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def _lazy_imports():
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try:
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import pandas as pd # noqa: F401
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import matplotlib.pyplot as plt # noqa: F401
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return True
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except Exception as e:
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sys.stdout.write(f"[chartkit] Missing dependency: {e}\n")
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sys.stdout.write("[chartkit] Fallback: describe chart spec in text instead of rendering PNG.\n")
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return False
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def _read_data(path):
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import pandas as pd
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if path.lower().endswith(".csv"):
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return pd.read_csv(path)
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elif path.lower().endswith((".json", ".ndjson")):
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return pd.read_json(path, lines=path.lower().endswith(".ndjson"))
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else:
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raise ValueError("Only CSV/JSON supported")
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def _ensure_outdir(p):
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os.makedirs(p, exist_ok=True)
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def render_chart(df, chart_type, x, y_cols, title, out_path):
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax = fig.gca()
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if chart_type in ("line", "area"):
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for col in y_cols:
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ax.plot(df[x], df[col], label=str(col))
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if chart_type == "area":
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ax.fill_between(df[x], df[y_cols[0]], alpha=0.2)
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elif chart_type in ("bar", "barh"):
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import numpy as np
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idx = np.arange(len(df[x]))
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width = 0.8/ max(1, len(y_cols))
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for i, col in enumerate(y_cols):
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if chart_type == "bar":
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ax.bar(idx + i*width, df[col], width, label=str(col))
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else:
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ax.barh(idx + i*width, df[col], width, label=str(col))
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ax.set_xticks(idx + width*(len(y_cols)-1)/2)
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ax.set_xticklabels(df[x], rotation=0, ha="center")
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elif chart_type == "scatter":
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for col in y_cols:
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ax.scatter(df[x], df[col], label=str(col))
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elif chart_type == "hist":
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ax.hist(df[y_cols[0]], bins=20)
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elif chart_type == "waterfall":
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# Simple waterfall for a single series
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import numpy as np
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vals = df[y_cols[0]].values
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cum = np.cumsum(vals) - vals
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ax.bar(df[x], vals, bottom=cum)
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ax.plot(df[x], cum + vals, marker="o")
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else:
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raise ValueError(f"Unsupported chart type: {chart_type}")
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ax.set_title(title if title else "")
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ax.legend(loc="best")
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ax.grid(True, alpha=0.2)
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fig.tight_layout()
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fig.savefig(out_path, dpi=180)
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plt.close(fig)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--data", required=True)
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ap.add_argument("--type", required=True, choices=["line","area","bar","barh","scatter","hist","waterfall"])
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ap.add_argument("--x", required=True)
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ap.add_argument("--y", nargs="+", required=True)
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ap.add_argument("--out", required=True)
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ap.add_argument("--filename", required=True)
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ap.add_argument("--title", default="")
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args = ap.parse_args()
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if not _lazy_imports():
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# Soft fail: print instructions for textual fallback
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sys.stdout.write(f"[chartkit] Would have rendered {args.type} chart to {args.out}/{args.filename}\n")
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return 0
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import pandas as pd
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df = _read_data(args.data)
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if args.x not in df.columns:
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raise SystemExit(f"[chartkit] Missing x column: {args.x}")
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for c in args.y:
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if c not in df.columns:
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raise SystemExit(f"[chartkit] Missing y column: {c}")
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_ensure_outdir(args.out)
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out_path = os.path.join(args.out, args.filename)
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render_chart(df, args.type, args.x, args.y, args.title, out_path)
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sys.stdout.write(f"[chartkit] Saved {out_path}\n")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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