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Agent skill

Casadi Ipopt NLP

by benchflow-ai in benchflow-ai/skillsbench

Nonlinear optimization with CasADi and IPOPT solver. An agent skill from benchflow-ai/skillsbench.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Casadi Ipopt NLP

skills CLI
$ npx skills add benchflow-ai/skillsbench --skill casadi-ipopt-nlp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install benchflow-ai/skillsbench casadi-ipopt-nlp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/energy-ac-optimal-power-flow/environment/skills/casadi-ipopt-nlp .claude/skills/casadi-ipopt-nlp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
casadi-ipopt-nlp
GitHub stars
1.8k
Token cost
~1.2k tokens
SKILL.md length
229 words
Files
1
Skills in repo
189
Repo updated
First seen
Licence
Apache-2.0

At a glance

Nonlinear optimization with CasADi and IPOPT solver. An agent skill from benchflow-ai/skillsbench.

  • Works in 5 steps: Decision variables → Objective function → Constraints → …
  • Building and solving NLP problems: defining symbolic variables
  • SKILL.md covers Quick start (Linux), Building an NLP, IPOPT options (tuning guide) and Initialization matters, plus 3 more sections
  • Calls apt-get and pip

What it does

Casadi Ipopt NLP is an agent skill from benchflow-ai/skillsbench. Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Natural language processing. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

  • Building and solving NLP problems: defining symbolic variables
  • Adding nonlinear constraints
  • Setting solver options
  • Handling multiple initializations

Example prompts

  • “/casadi-ipopt-nlp”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Decision variables
  2. Objective function
  3. Constraints
  4. Variable bounds
  5. Create and call solver

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • apt-get
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Casadi Ipopt NLP loads about 1.2k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 229 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 229 words, ~1,232 tokens.

Download SKILL.mdSave it as .claude/skills/casadi-ipopt-nlp/SKILL.md (or your agent's skills folder).
name
casadi-ipopt-nlp
description
Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.

CasADi + IPOPT for Nonlinear Programming

CasADi is a symbolic framework for nonlinear optimization. IPOPT is an interior-point solver for large-scale NLP.

Quick start (Linux)

bash
apt-get update -qq && apt-get install -y -qq libgfortran5
pip install numpy==1.26.4 casadi==3.6.7

Building an NLP

1. Decision variables
python
import casadi as ca

n_bus, n_gen = 100, 20
Vm = ca.MX.sym("Vm", n_bus)   # Voltage magnitudes
Va = ca.MX.sym("Va", n_bus)   # Voltage angles (radians)
Pg = ca.MX.sym("Pg", n_gen)   # Real power
Qg = ca.MX.sym("Qg", n_gen)   # Reactive power

# Stack into single vector for solver
x = ca.vertcat(Vm, Va, Pg, Qg)
2. Objective function

Build symbolic expression:

python
# Quadratic cost: sum of c2*P^2 + c1*P + c0
obj = ca.MX(0)
for k in range(n_gen):
    obj += c2[k] * Pg[k]**2 + c1[k] * Pg[k] + c0[k]
3. Constraints

Collect constraints in lists with bounds:

python
g_expr = []  # Constraint expressions
lbg = []     # Lower bounds
ubg = []     # Upper bounds

# Equality constraint: g(x) = 0
g_expr.append(some_expression)
lbg.append(0.0)
ubg.append(0.0)

# Inequality constraint: g(x) <= limit
g_expr.append(another_expression)
lbg.append(-ca.inf)
ubg.append(limit)

# Two-sided: lo <= g(x) <= hi
g_expr.append(bounded_expression)
lbg.append(lo)
ubg.append(hi)

g = ca.vertcat(*g_expr)
4. Variable bounds
python
# Stack bounds matching variable order
lbx = np.concatenate([Vm_min, Va_min, Pg_min, Qg_min]).tolist()
ubx = np.concatenate([Vm_max, Va_max, Pg_max, Qg_max]).tolist()
5. Create and call solver
python
nlp = {"x": x, "f": obj, "g": g}
opts = {
    "ipopt.print_level": 0,
    "ipopt.max_iter": 2000,
    "ipopt.tol": 1e-7,
    "ipopt.acceptable_tol": 1e-5,
    "ipopt.mu_strategy": "adaptive",
    "print_time": False,
}
solver = ca.nlpsol("solver", "ipopt", nlp, opts)

sol = solver(x0=x0, lbx=lbx, ubx=ubx, lbg=lbg, ubg=ubg)
x_opt = np.array(sol["x"]).flatten()
obj_val = float(sol["f"])

IPOPT options (tuning guide)

OptionDefaultRecommendationNotes
tol1e-81e-7Convergence tolerance
acceptable_tol1e-61e-5Fallback if tol not reached
max_iter30002000Increase for hard problems
mu_strategymonotoneadaptiveBetter for nonconvex
print_level50Quiet output

Initialization matters

Nonlinear solvers are sensitive to starting points. Use multiple initializations:

python
initializations = [x0_from_data, x0_flat_start]
best_sol = None

for x0 in initializations:
    try:
        sol = solver(x0=x0, lbx=lbx, ubx=ubx, lbg=lbg, ubg=ubg)
        if best_sol is None or float(sol["f"]) < float(best_sol["f"]):
            best_sol = sol
    except Exception:
        continue

if best_sol is None:
    raise RuntimeError("Solver failed from all initializations")

Good initialization strategies:

  • Data-derived: Use values from input data, clipped to bounds
  • Flat start: Nominal values (e.g., Vm=1.0, Va=0.0)
  • Always enforce known constraints in initial point (e.g., reference angle = 0)

Extracting solutions

python
x_opt = np.array(sol["x"]).flatten()

# Unpack by slicing (must match variable order)
Vm_sol = x_opt[:n_bus]
Va_sol = x_opt[n_bus:2*n_bus]
Pg_sol = x_opt[2*n_bus:2*n_bus+n_gen]
Qg_sol = x_opt[2*n_bus+n_gen:]

Power systems patterns

Per-unit scaling

Work in per-unit internally, convert for output:

python
baseMVA = 100.0
Pg_pu = Pg_MW / baseMVA      # Input conversion
Pg_MW = Pg_pu * baseMVA      # Output conversion

Cost functions often expect MW, not per-unit - check the formulation.

Bus ID mapping

Power system bus numbers may not be contiguous:

python
bus_id_to_idx = {int(bus[i, 0]): i for i in range(n_bus)}
gen_bus_idx = bus_id_to_idx[int(gen_row[0])]
Aggregating per-bus quantities
python
Pg_bus = [ca.MX(0) for _ in range(n_bus)]
for k in range(n_gen):
    bus_idx = gen_bus_idx[k]
    Pg_bus[bus_idx] += Pg[k]

Common failure modes

  • Infeasible: Check bound consistency, constraint signs, unit conversions
  • Slow convergence: Try different initialization, relax tolerances temporarily
  • Wrong tap handling: MATPOWER uses tap=0 to mean 1.0, not zero
  • Angle units: Data often in degrees, solver needs radians
  • Shunt signs: Check convention for Gs (conductance) vs Bs (susceptance)
  • Over-rounding outputs: Keep high precision (≥6 decimals) in results

© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in tasks/energy-ac-optimal-power-flow/environment/skills/casadi-ipopt-nlp of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Casadi Ipopt NLP next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Casadi Ipopt NLP compared with similar skills
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OpenMed Model Card Writermaziyarpanahi/openmed5.5k—~1.8kAutomated safety check: PassApache-2.0
Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel1.3k—~1.1kAutomated safety check: PassCustom licence
Andrej KarpathyK-Dense-AI/mimeo282—~1.9kAutomated safety check: PassMIT
Comparetaishi-i/awesome-japanese-nlp-resources1k—~4.1kAutomated safety check: NotesCC0-1.0

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Categories

Questions about Casadi Ipopt NLP

What does Casadi Ipopt NLP do?

Nonlinear optimization with CasADi and IPOPT solver. An agent skill from benchflow-ai/skillsbench. Casadi Ipopt NLP is an agent skill from benchflow-ai/skillsbench. Nonlinear optimization with CasADi and IPOPT solver.

When should I use Casadi Ipopt NLP?

Casadi Ipopt NLP fits situations like: building and solving NLP problems: defining symbolic variables; adding nonlinear constraints; setting solver options; handling multiple initializations.

How do I install Casadi Ipopt NLP in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill casadi-ipopt-nlp -a claude-code`. Or copy the skill folder (tasks/energy-ac-optimal-power-flow/environment/skills/casadi-ipopt-nlp in benchflow-ai/skillsbench) into .claude/skills/casadi-ipopt-nlp in your project. Claude Code loads it when a task matches its description.

How do I install Casadi Ipopt NLP in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill casadi-ipopt-nlp -a codex`. Or copy the skill folder (tasks/energy-ac-optimal-power-flow/environment/skills/casadi-ipopt-nlp in benchflow-ai/skillsbench) into .agents/skills/casadi-ipopt-nlp in your project. Codex loads it when a task matches its description.

Can I use Casadi Ipopt NLP in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill casadi-ipopt-nlp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/casadi-ipopt-nlp, .gemini/skills/casadi-ipopt-nlp, .github/skills/casadi-ipopt-nlp and .opencode/skills/casadi-ipopt-nlp in your project.

What does Casadi Ipopt NLP need to run?

Going by SKILL.md and its folder, Casadi Ipopt NLP needs the command-line tools its instructions call (apt-get and pip). Our summary lists: Python 3.

Does Casadi Ipopt NLP access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Casadi Ipopt NLP safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Casadi Ipopt NLP use?

Casadi Ipopt NLP is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Casadi Ipopt NLP use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Casadi Ipopt NLP?

Skills that share tags, products or a category with Casadi Ipopt NLP: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars), Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars) and Andrej Karpathy (K-Dense-AI/mimeo, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Casadi Ipopt NLP?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.