Anneal Temper
- 1 repo stars
- Updated May 30, 2026
- krzemienski/anneal
anneal-temper is an agent skill that iteratively deepens architecture or plans via multi-agent fixed-point refinement until variance-based convergence.
About
anneal-temper is an agent skill from the Anneal family that applies a fixed-point deepen architecture pattern: your agent iteratively refines plans or system design with multi-agent review until changes fall below a variance threshold. It is aimed at developers using Claude Code, Cursor, Codex, or similar agents when a task is genuinely complex yet still scoped—you know the boundary, but one pass is not enough to lock the shape. Keywords in the package emphasize plan, review, multi-agent deepen, fixed-point, and anneal, which signals a procedural workflow rather than a single integration. Use it when architecture or implementation planning keeps oscillating between agent runs and you need an explicit convergence rule before coding or shipping. It is less appropriate for trivial features or when an approved spec already exists. Pair it mentally with other Anneal skills if your repo documents a stack; this entry tags temper as the convergence-oriented deepen step for bounded hard problems.
- Fixed-point deepen loop that refines architecture or plans across repeated agent passes
- Variance-based convergence criteria to stop iteration instead of endless rewrites
- Multi-agent plan-and-review workflow tuned for complex but scoped tasks
- Anneal-style tempering: progressive refinement rather than one-shot generation
- Best when a single chat pass leaves architecture still drifting between revisions
Anneal Temper by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
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| repo stars | ★ 1 |
|---|---|
| Last updated | May 30, 2026 |
| Repository | krzemienski/anneal ↗ |
What it does
Run fixed-point, multi-agent deepen cycles on complex but bounded architecture or plans until variance-based convergence before you commit to full implementation.
Who is it for?
Best when you're facing bounded, high-complexity architecture or planning work and want multi-agent deepen cycles with an explicit convergence stop rule.
Skip if: Simple one-shot tasks, already-approved specs, or work where iterative multi-agent refinement would add cost without narrowing design variance.
When should I use this skill?
Best for complex but scoped tasks that need fixed-point iterative refinement of architecture or plans with variance-based convergence via multi-agent deepen.
What you get
You get a stabilized architecture or plan artifact after variance-based fixed-point iterations, ready to hand off to implementation, review, or deployment prep without endless rewrite loops.
- Converged architecture or plan document after fixed-point iterations
- Documented convergence rationale (variance threshold met)
Files
Fixed-point deepen architecture. Iterative refinement with variance-based convergence. Best for complex but scoped tasks.
anneal-temper
Source: ./temper
{ "name": "anneal-temper", "author": { "name": "Nick Krzemienski" }, "source": "./temper", "version": "0.1.0", "keywords": [ "plan", "review", "multi-agent", "deepen", "fixed-point", "anneal" ], "description": "Fixed-point deepen architecture. Iterative refinement with variance-based convergence. Best for complex but scoped tasks." }
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Use instead of ad-hoc multi-turn architecture chat when you need fixed-point convergence criteria rather than subjective “looks good enough.”
FAQ
Who is anneal-temper for?
It is for developers using agentic coding tools who need to deepen complex but scoped architecture or plans with multi-agent iterations and a defined convergence rule.
When should I use anneal-temper?
Use it during Validate to converge scope and architecture, during Build when PM-style plans still drift across agent runs, and during Ship when review feedback needs fixed-point refinement before you lock the design—always for complex tasks with clear boundaries.
Is anneal-temper safe to install?
Treat it like any third-party agent skill: review the Security Audits panel on this Prism page and inspect SKILL.md in your repo before granting shell, network, or secrets access to your agent.