How do I install the Intent Sensitive Selection skill?

Run npx skillmds@latest add wangjian169/intent-sensitive-selection in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Intent Sensitive Selection safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Intent Sensitive Selection?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Intent Sensitive Selection free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Intent Sensitive Selection?

wangjian169 (@wangjian169) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Intent Sensitive Selection by wangjian169

Name: Intent Sensitive Selection
Rating: 4.666666666666667 (3 reviews)
Author: wangjian169

Intent Sensitive Selection

Select a global subset of SWMM subcatchment parameters (same param indices for all subcatchments) using intent-driven sensitivity analysis across one or more event/observation pairs. Use when 11 parameters per subcatchment are too many to calibrate and you need a reduced --param_indices set for GA, Bayes-MH, or ES-ILU.

wangjian169 Updated Sep 22, 2026

File contents

Intent-Driven Global Parameter Selection

Select which parameter indices to calibrate from the 11 SWMM subcatchment parameters. The selected indices are global: every subcatchment uses the same selected set.

Inputs

Parameter	Type	Required	Notes
`base_inp`	string	yes	Base SWMM INP path
`obs_csv_paths`	list[string]	yes	Observation CSV paths (obs1, obs2, ...)
`event_paths`	list[string]	yes	Rain event txt paths (event1, event2, ...)
`subcatchments`	list[string]	yes	Subcatchment IDs to calibrate
`intent`	string	yes	`peak`, `volume`, `hydrograph_shape`, `infiltration`, `balanced`
`top_k`	int	yes	Number of final selected indices
`infiltration_model`	string	no	`horton` (default) or `green-ampt`
`method`	string	no	`morris` (currently implemented)
`r`	int	no	Number of Morris trajectories (default 20)
`delta`	float	no	Morris perturbation step in normalized space (default 0.2)
`seed`	int	no	Random seed

Multi-Event Pairing Rule

obs_csv_paths and event_paths must have the same length. They are paired by position:

pair 1: obs_csv_paths[0] with event_paths[0]
pair 2: obs_csv_paths[1] with event_paths[1]
...

Intent Candidate Pools (11-parameter setup)

Parameter index map: 0 width, 1 slope, 2 imperv, 3 n_imperv, 4 n_perv, 5 dstor_imperv, 6 dstor_perv, 7 zero_imperv, 8 maxrate, 9 minrate, 10 decay

peak: [0, 1, 2, 3, 5, 7, 8]
volume: [2, 4, 6, 8, 9, 10]
hydrograph_shape: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
infiltration + horton: [8, 9, 10]
infiltration + green-ampt: fallback to [8, 9, 10] in this 11-parameter script
balanced: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Usage

python -m skills.intent_sensitive_selection.Scripts.select_global_params \
  --base_inp <model.inp> \
  --obs_csv_paths <event1.csv> <event2.csv> \
  --event_paths <event1.txt> <event2.txt> \
  --subcatchments 1 2 3 4 5 6 7 8 \
  --intent peak \
  --top_k 3 \
  --r 20 \
  --delta 0.2 \
  --seed 7

Outputs

Printed once to stdout:

selected_param_indices (global indices for all subcatchments)
selected_param_names
param_indices_cli (directly reusable in calibrate scripts)
candidate_param_indices
ranking (idx, name, mu_star, sigma, rank)
diagnostics (n_events, n_evaluations, method, seed, runtime)

Time Handling Policy

Do not manually edit INP [OPTIONS] dates for event matching. This skill prepares event-specific INP files from each event_path and corresponding obs_csv_paths timestamp range.

---name: intent-sensitive-selectiondescription: Select a global subset of SWMM subcatchment parameters (same param indices for all subcatchments) using intent-driven sensitivity analysis across one or more event/observation pairs. Use when 11 parameters per subcatchment are too many to calibrate and you need a reduced --param_indices set for GA, Bayes-MH, or ES-ILU.---## Intent-Driven Global Parameter SelectionSelect which parameter indices to calibrate from the 11 SWMM subcatchment parameters.The selected indices are global: every subcatchment uses the same selected set.### Inputs| Parameter | Type | Required | Notes ||---|---|---|---|| `base_inp` | string | yes | Base SWMM INP path || `obs_csv_paths` | list[string] | yes | Observation CSV paths (obs1, obs2, ...) || `event_paths` | list[string] | yes | Rain event txt paths (event1, event2, ...) || `subcatchments` | list[string] | yes | Subcatchment IDs to calibrate || `intent` | string | yes | `peak`, `volume`, `hydrograph_shape`, `infiltration`, `balanced` || `top_k` | int | yes | Number of final selected indices || `infiltration_model` | string | no | `horton` (default) or `green-ampt` || `method` | string | no | `morris` (currently implemented) || `r` | int | no | Number of Morris trajectories (default 20) || `delta` | float | no | Morris perturbation step in normalized space (default 0.2) || `seed` | int | no | Random seed |### Multi-Event Pairing Rule`obs_csv_paths` and `event_paths` must have the same length.They are paired by position:- pair 1: `obs_csv_paths[0]` with `event_paths[0]`- pair 2: `obs_csv_paths[1]` with `event_paths[1]`- ...### Intent Candidate Pools (11-parameter setup)Parameter index map:`0 width, 1 slope, 2 imperv, 3 n_imperv, 4 n_perv, 5 dstor_imperv, 6 dstor_perv, 7 zero_imperv, 8 maxrate, 9 minrate, 10 decay`- `peak`: `[0, 1, 2, 3, 5, 7, 8]`- `volume`: `[2, 4, 6, 8, 9, 10]`- `hydrograph_shape`: `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]`- `infiltration` + `horton`: `[8, 9, 10]`- `infiltration` + `green-ampt`: fallback to `[8, 9, 10]` in this 11-parameter script- `balanced`: `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]`### Usage```bashpython -m skills.intent_sensitive_selection.Scripts.select_global_params \ --base_inp <model.inp> \ --obs_csv_paths <event1.csv> <event2.csv> \ --event_paths <event1.txt> <event2.txt> \ --subcatchments 1 2 3 4 5 6 7 8 \ --intent peak \ --top_k 3 \ --r 20 \ --delta 0.2 \ --seed 7```### OutputsPrinted once to stdout:- `selected_param_indices` (global indices for all subcatchments)- `selected_param_names`- `param_indices_cli` (directly reusable in calibrate scripts)- `candidate_param_indices`- `ranking` (`idx`, `name`, `mu_star`, `sigma`, `rank`)- `diagnostics` (`n_events`, `n_evaluations`, `method`, `seed`, runtime)### Time Handling PolicyDo not manually edit INP [OPTIONS] dates for event matching.This skill prepares event-specific INP files from each `event_path` and corresponding `obs_csv_paths` timestamp range.

Frequently asked questions