generate-calculations
GitHub将已验证的参数JSON转换为包含确定性公式的Python模块，为下游场景运行和蒙特卡洛模拟提供可导入的纯函数。
Trigger Scenarios
Install
npx skills add PlanExeOrg/PlanExe --skill generate-calculations -g -y
SKILL.md
Frontmatter
{
"name": "generate-calculations",
"description": "Use when the user wants to turn a validated extract-parameters-from-full JSON into a Python module of deterministic functions implementing the formula_hint expressions for downstream scenario runs and Monte Carlo"
}
Generate Deterministic Calculations from Extracted Parameters
Overview
Wraps the calculation-generator system prompt at system-prompt.txt (next to this file) and applies it to a parameter JSON produced by extract-parameters-from-full (validated by validate-parameters). Output is a single Python module of small, pure functions — one per formula_hint declared in recommended_first_calculations and derived_questions.
Stage 5 of the pipeline described in planexe_simulator/README.md.
When to Use
- User asks to "generate calculations", "emit Python", "materialise the formulas", or "build the deterministic functions" given a validated parameter JSON
- Pipeline step between
validate-parameters(passes clean) /generate-boundsandrun-scenarios - User wants importable Python functions ready for scenario tables
Not for: regenerating the parameter JSON (use extract-parameters-from-full), validating it (use validate-parameters), producing low/base/high ranges (use generate-bounds), or running scenarios (use run-scenarios).
Workflow
- Get the input JSON path. If the user did not provide one, ask. Do not guess.
- Read
system-prompt.txt(sibling of this SKILL.md). Its function-shape, division-guard, and module-structure rules are authoritative. - Read the parameter JSON. Assume it has already passed
validate-parameters; if it visibly hasn't, tell the user and offer to validate first. - Produce the Python module per the system prompt.
- Output destination. Default: write to a file. Suggested default path:
<input-basename>.calculations.pynext to the input. Print the file path back, plus a one-line summary (function count, any# skippedlines, any TODO stubs forP(...)notation).
What gets a function
| Input list | Action |
|---|---|
recommended_first_calculations |
one function each |
derived_questions |
one function each |
key_values |
not converted — these are caller-supplied inputs |
missing_values_to_estimate |
not converted — supplied via bounds at scenario time |
Skip an entry whose formula_hint is null, empty, or unparseable. Replace with a # skipped: <id> -- <reason> comment.
Function shape (re-stated for emphasis — see system prompt for full detail)
def x(a: float, b: float) -> float:
return a * b
- Function name = LHS of
formula_hintif present, else the entry'sid - Args = each
depends_onid in declared order, all typedfloat - Return type
float - Body: at most three lines (optional guard
if, optional intermediate, return)
Division guards: every variable denominator must short-circuit to float("inf") when ≤ 0. Numeric-literal denominators (e.g. value / 100) need no guard.
Function-style notation translations
| Source | Translation |
|---|---|
max(...), min(...), abs(...), sum(...) |
Python builtins |
exp(...), log(...), sqrt(...), ln(...) |
math.exp, math.log, math.sqrt (add import math) |
mean(...), avg(...) |
_mean(*args) helper at top of module |
P(...), p(...) |
raise NotImplementedError(...) stub with TODO comment carrying the original formula |
Module structure
"""
Generated PlanExe deterministic calculations.
Plan: <plan_name>
Plan type: <plan_type>
One function per formula_hint entry...
"""
from __future__ import annotations
import math # only if needed
# _mean helper, only if needed
# functions, in order: recommended_first_calculations, then derived_questions
No top-level executable code, no __main__ block, no file I/O, no classes, no decorators, no per-function docstrings, no in-body comments (except the P(...) TODO).
Common Mistakes
| Mistake | Fix |
|---|---|
Wrapping the Python output in ```python fences |
Raw Python only |
Adding a if __name__ == "__main__": demo block |
This stage emits a library, not a runnable script |
| Generating a class hierarchy | One function per formula; no classes unless required by the formulas themselves (they aren't) |
| Inventing or omitting arguments to "make the formula work" | Args must match depends_on exactly, in order |
| Forgetting the divide-by-zero guard | Every variable denominator gets a guard; numeric literals don't |
Emitting def people_contacted(...) -> float: """People contacted""" |
No per-function docstrings — the signature is self-documenting |
Translating P(x >= y) as a literal Python comparison |
P(...) is probability notation; emit a NotImplementedError stub instead |
Including functions for key_values or missing_values_to_estimate ids |
Those are inputs, not calculations |
Reference
- System prompt (authoritative):
system-prompt.txt - Pipeline overview and code-generation rules:
../../README.md, Stage 5 - Companion skills:
../extract-parameters-from-full/SKILL.md,../validate-parameters/SKILL.md,../generate-bounds/SKILL.md - Example input for testing:
/tmp/extract-params-heatwave-v10.json(passes validate-parameters withvalid: true)
Version History
- 846f612 Current 2026-08-20 11:18
