Skip to content
TrustedSkillsbeta
Back to all skills

Loop Plan Evaluator

Checked
ibrahim-3d·vlatest·productivity·AGPL-3.0

Analyzes iterative planning loops, identifying inefficiencies and suggesting improvements to optimize task completion.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ibrahim-3d/orchestrator-supaconductor --skill loop-plan-evaluator -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ibrahim-3d

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ibrahim-3d/conductor-orchestrator-superpowers at 76c9b10023.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
76c9b10023
Manifest
skills/loop-plan-evaluator/SKILL.md
Scanned
1 file, 339 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The loop-plan-evaluator skill allows AI agents to evaluate the effectiveness of iterative plans. It can assess whether a plan is achieving its goals, identify areas for improvement within the loop, and suggest modifications to optimize performance. This enables more adaptive and efficient task execution by analyzing results at each iteration.

When to use it

  • Complex workflows: When an agent needs to repeatedly refine actions based on feedback (e.g., iteratively improving a code generation plan).
  • Optimization tasks: For scenarios requiring iterative adjustments to parameters or strategies to maximize a specific outcome (e.g., optimizing ad campaign bids).
  • Debugging and refinement: To pinpoint why a looping process isn't producing the desired results, allowing for targeted corrections.
  • Adaptive learning: When an agent needs to learn from its mistakes in a loop and adjust its approach accordingly.

Key capabilities

  • Plan evaluation within loops
  • Identification of areas for improvement
  • Suggestion of plan modifications
  • Performance optimization through iterative refinement

Example prompts

  • "Evaluate the current iteration of my code generation plan and suggest improvements."
  • "Analyze the results of this ad bidding loop. What adjustments should I make to improve click-through rates?"
  • "I'm running a simulation loop; tell me if it’s converging on the desired outcome, and what parameters need tweaking."

Tips & gotchas

The skill is most effective when provided with clear metrics or criteria for evaluating plan success. Ensure that the agent has access to relevant data at each iteration of the loop to facilitate accurate evaluation.