How do I install the Recursion Iteration Transformer skill?

Run npx skillmds@latest add 45ck/recursion-iteration-transformer 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.

What does the Recursion Iteration Transformer skill do?

recursion-iteration-transformer It is listed under AI & ML on SkillMD.

Is Recursion Iteration Transformer safe to use?

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

Which AI agents work with Recursion Iteration Transformer?

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 Recursion Iteration Transformer free to use?

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

Who published Recursion Iteration Transformer?

45ck (@45ck) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Recursion Iteration Transformer by 45ck

Name: Recursion Iteration Transformer
Author: 45ck

File contents

recursion-iteration-transformer

Purpose

Convert between recursive and iterative formulations while preserving correctness, complexity, and readability.

Trigger this skill when

You need a structure or algorithm recommendation grounded in the real workload.
A current implementation choice feels arbitrary, costly, or hard to justify.
You want to reason about correctness or performance before writing more code.

Expected inputs

current formulation
base/termination conditions
state carried across calls/loops
space constraints
readability goals

Deliverables

transformed formulation
state mapping
correctness cautions
space/runtime notes
preferred final form

Operating procedure

Identify the data or algorithmic concern this skill is meant to assess.
Separate facts about operations, sizes, ordering, and mutation from assumptions.
Review the current or proposed approach against the workload, invariants, complexity, and representation constraints.
Surface concrete risks, tradeoffs, and improvement options.
Prefer the least complex structure or algorithm that meets the stated needs.
Hand off to the next most relevant skill if the issue is broader than this skill alone can resolve.

Quality gates

Findings are tied to the stated workload or concrete algorithm steps.
Recommendations explain why the choice fits the context.
Tradeoffs are stated when multiple options are viable.
Output distinguishes asymptotic concerns from practical implementation risks.

Handoff targets

algorithm-trace-checker
invariant-finder
complexity-red-flag-detector

Output style

Be explicit about uncertainty.
Prefer short, evidence-based findings over generic claims of efficiency.
Call out hidden assumptions such as sorted input, bounded size, or low mutation.
End with recommended next actions.

Failure modes to avoid

Do not recommend advanced structures without a workload that justifies them.
Do not rely on Big-O alone when locality or constant factors dominate.
Do not conflate proof of correctness with a single successful example.
Do not ignore edge cases, degenerate inputs, or mutation semantics.

Minimum output skeleton

## Summary
## Findings
## Risks
## Recommendations
## Tradeoffs
## Recommended next skill

---name: recursion-iteration-transformerdescription: recursion-iteration-transformer---# recursion-iteration-transformer## PurposeConvert between recursive and iterative formulations while preserving correctness, complexity, and readability.## Trigger this skill when- You need a structure or algorithm recommendation grounded in the real workload.- A current implementation choice feels arbitrary, costly, or hard to justify.- You want to reason about correctness or performance before writing more code.## Expected inputs- current formulation- base/termination conditions- state carried across calls/loops- space constraints- readability goals## Deliverables- transformed formulation- state mapping- correctness cautions- space/runtime notes- preferred final form## Operating procedure1. Identify the data or algorithmic concern this skill is meant to assess.2. Separate facts about operations, sizes, ordering, and mutation from assumptions.3. Review the current or proposed approach against the workload, invariants, complexity, and representation constraints.4. Surface concrete risks, tradeoffs, and improvement options.5. Prefer the least complex structure or algorithm that meets the stated needs.6. Hand off to the next most relevant skill if the issue is broader than this skill alone can resolve.## Quality gates- Findings are tied to the stated workload or concrete algorithm steps.- Recommendations explain why the choice fits the context.- Tradeoffs are stated when multiple options are viable.- Output distinguishes asymptotic concerns from practical implementation risks.## Handoff targets- algorithm-trace-checker- invariant-finder- complexity-red-flag-detector## Output style- Be explicit about uncertainty.- Prefer short, evidence-based findings over generic claims of efficiency.- Call out hidden assumptions such as sorted input, bounded size, or low mutation.- End with recommended next actions.## Failure modes to avoid- Do not recommend advanced structures without a workload that justifies them.- Do not rely on Big-O alone when locality or constant factors dominate.- Do not conflate proof of correctness with a single successful example.- Do not ignore edge cases, degenerate inputs, or mutation semantics.## Minimum output skeleton```md## Summary## Findings## Risks## Recommendations## Tradeoffs## Recommended next skill```

Frequently asked questions

Recursion Iteration Transformer