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molting
Perform the shell reset required when an agent's primary model, provider, or effective prompt language changes. Use when you need to refactor prompts, persona, tool rules, and workflow assumptions into a coherent canonical spec with traceability and evaluation. Also use for broader shell cleanup when the operating layer has become bloated or contradictory.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Molting
Molting is the controlled rewrite of an agent shell after its primary model changes.
When the default LLM changes, the shell around it rarely transfers cleanly. Prompts, persona, tool habits, formatting rules, and provider-specific patches were tuned around the previous model. If you swap the brain and keep the shell untouched, regressions often show up as lower discipline, weaker tool use, flatter persona, or inconsistent response language.
This repo packages a practical way to handle that transition.
What Molting Is
Molting is instruction-layer refactoring:
- reread the current operating materials
- separate hard rules from soft preferences and stale clutter
- rewrite a cleaner canonical spec for the new primary model
- preserve traceability and test the rewrite before adoption
What Molting Is Not
- retraining or fine-tuning
- a substitute for fixing broken tools or missing documentation
- permission to mix shells from different primary models without review
- a reason to skip evaluation just because the rewrite sounds cleaner
When To Use It
- the primary model or provider changed
- an established shell performs worse on a new model family
- response language, verbosity, or tool discipline drifted after migration
- the shell accumulated provider-specific patches, duplicated rules, or contradictions
- you want a portable, inspectable operating spec instead of patch-on-patch prompt edits
What A Good Molt Produces
- a canonical operating spec for the new primary model
- a traceability map showing what was preserved, merged, clarified, inferred, or removed
- a risk review covering constraint erosion, persona drift, and tool confusion
- a focused evaluation plan before the rewrite is adopted
Repository Contents
| Path | Purpose |
|---|---|
| docs/framework.md | Human-readable explanation of the molting framework and decision model. |
| docs/checklist.md | Step-by-step checklist for running a disciplined molt. |
| docs/discussion.md | Open questions and research directions for the framework. |
| skill/molting/SKILL.md | Portable skill entrypoint for agent systems that support packaged skills. |
| skill/molting/references/molting-framework.md | Portable framework reference optimized for in-agent use. |
| skill/molting/references/molting-prompts.md | Staged prompts for interpret -> classify -> rewrite -> trace -> test. |
| templates/molting-report-template.md | Report template for documenting a completed molt. |
| examples/coding-agent-primary-model-transition.md | Worked example of a coding-agent shell migration. |
Workflow
- Inventory the current shell: prompts, persona files, tool policies, formatting rules, examples, and patches.
- Interpret the shell before rewriting: mission, priorities, hard constraints, tone, tool semantics, and contradictions.
- Classify material into hard rules, soft rules, heuristics, and legacy clutter.
- Rewrite a cleaner canonical spec for the new primary model.
- Trace the rewrite back to source material so nothing important is silently weakened.
- Evaluate before adoption on the behaviors that actually matter.
Common Use Cases
- moving a coding agent from one primary model family to another
- translating an existing shell into a new prompt language because the new model behaves better there
- cleaning up a long-lived agent whose prompts accreted through patches and emergency workarounds
- packaging an agent definition so it can survive future model or provider swaps with less drift
Design Principles
- preserve hard constraints before polishing prose
- treat prompt language changes as shell changes, not cosmetic rewrites
- remove stale model-specific hacks instead of carrying them forward by default
- prefer explicit traceability over elegant but unverifiable simplification
- adopt a molt only after the new shell actually behaves better or more consistently
Status
This repository is intentionally lightweight and doc-first. The framework is ready to use, the skill is portable, and the surrounding materials are meant to make the concept easy to inspect, apply, and extend in public.
If you want to contribute examples, evaluation rubrics, or sharper migration patterns, start with CONTRIBUTING.md.
License
This project is licensed under the MIT License. Use it, modify it, remix it, and ship it.
// HOW IT'S BUILT
KEY FILES