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Convergence Failure Mitigation

Organization
Power-Agent
convergence-failure-mitigation

Senior power-engineer playbook for power-flow convergence failures. Use whenever a base case or post-change case diverges, oscillates, or fails to solve in PSS/E, PSLF, PowerWorld, pandapower, PyPSA, surge, or OpenDSS. Triggers on "power flow diverged", "case won't solve", "did not converge", "blown-up voltages", or "max iterations reached". Every tool skill stops on a non-convergent base case — this playbook is what to do next, separating data errors from numerical fragility from genuine physical infeasibility.

Overview

PublisherPower-Agent
RepositoryPowerSkills
Skill nameconvergence-failure-mitigation
Stars
77
Forks
23
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Power-Agent on GitHub. Read the source before you install it.

Installation

Install the Convergence Failure Mitigation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Power-Agent/PowerSkills/tree/main/powerskills-engineering/skills/convergence-failure-mitigation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Power-Agent/PowerSkills.git /tmp/PowerSkills
mkdir -p .claude/skills
cp -r /tmp/PowerSkills/powerskills-engineering/skills/convergence-failure-mitigation .claude/skills/convergence-failure-mitigation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convergence Failure Mitigation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Convergence Failure Mitigation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Convergence Failure Mitigation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Convergence failure mitigation

Start from the failed solve and its mismatch report. The goal is to learn why it failed, not just to force a solution.

Preferred action order

  1. Rule out data problems first: isolated or de-energized buses, zero or absurd branch impedances, missing or misplaced swing bus, transformer tap and phase-angle data, load/generation totals that cannot balance.
  2. Check islanding and slack coverage: every energized island needs a slack with feasible MW. Use the tool's island or topology report before touching solver settings.
  3. Simplify the solve, then restore: try flat start vs warm start, lock taps, switched shunts, and phase shifters, relax generator Q-limits — then re-enable controls stepwise once a solution exists.
  4. Seed from an easier point: a DC solve, a previously solved neighbor case, or a reduced-stress version of the case, then scale load or transfer toward the target in steps.
  5. If failure persists only near the target stress level, treat it as a feasibility boundary, not a numerical bug: reduce transfer or load, add reactive support, and flag possible voltage-collapse proximity.

Working rules

  • The largest mismatch bus tells you where to look — start there, not with global solver settings.
  • Change one solver setting at a time and record what finally solved.
  • A case solved with controls locked or Q-limits relaxed is not finished; restore realism before reporting results from it.
  • Distinguish "the math failed" from "the operating point does not exist" — the corrective actions are completely different.

Deliver

  • The failure category: data error, control interaction, numerical fragility, or physical infeasibility.
  • The specific change that made the case solve.
  • Any realism caveats that remain (locked controls, relaxed limits) and what to re-check.

Frequently asked questions

What does the Convergence Failure Mitigation AI skill do?

Senior power-engineer playbook for power-flow convergence failures. Use whenever a base case or post-change case diverges, oscillates, or fails to solve in PSS/E, PSLF, PowerWorld, pandapower, PyPSA, surge, or OpenDSS. Triggers on "power flow diverged", "case won't solve", "did not converge", "blown-up voltages", or "max iterations reached". Every tool skill stops on a non-convergent base case — this playbook is what to do next, separating data errors from numerical fragility from genuine physical infeasibility.

Why use Convergence Failure Mitigation on TypingMind?

Because you install it once and use it with any model. Convergence Failure Mitigation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Convergence Failure Mitigation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Power-Agent/PowerSkills/tree/main/powerskills-engineering/skills/convergence-failure-mitigation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Convergence Failure Mitigation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Convergence Failure Mitigation?

As many as you like. As long as a model supports skills, you can use Convergence Failure Mitigation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Convergence Failure Mitigation AI skill free?

Yes. It is published on GitHub by Power-Agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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