Trim

CommunityPopular

danielmiessler

Trim

Reduces an always-on LifeOS context file that has grown too big via a human-gated pass — deterministic GC of stale entries first, then semantic merges and relocations — never dropping a directive and committing every change reversibly. USE WHEN /trim, trim the context, trim OPERATIONAL_RULES, this file is too big, reduce a doctrine file, prune an always-loaded file, fold the proposal inbox, shrink CLAUDE.md or DA_IDENTITY. NOT FOR general code refactoring, trimming video or audio media (use AudioEditor for audio files), or removing AI writing patterns from prose.

Overview

Publisher	danielmiessler
Repository	`LifeOS`
Skill name	`Trim`
Stars	19K
Forks	2.5K
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by danielmiessler on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/danielmiessler/LifeOS.git /tmp/LifeOS
mkdir -p .claude/skills
cp -r /tmp/LifeOS/LifeOS/install/skills/Trim .claude/skills/Trim

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trim 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 Trim 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 Trim 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.

Trim

Shrink an always-on context file that has gotten too big. /trim <file> walks the reduction, safest cuts first, never dropping a rule.

Workflow Routing

Trigger	Workflow
`/trim <file>`, "trim OPERATIONAL_RULES", "this file is too big", "reduce a doctrine file", "fold the proposal inbox"	`Workflows/Trim.md`

Quick Reference

Target resolution: a bare name (OPERATIONAL_RULES) resolves against the always-on set — the system prompt, CLAUDE.md, its @-imports, and the hook-injected memory files. No arg → wc -c that set and take the largest.
Order is safest-first: (1) show state, (2) deterministic GC (zero-risk), (3) semantic trims (human-gated), (4) safety gate, (5) re-measure. Full steps: Workflows/Trim.md.
One tool it orchestrates — never reimplement: LIFEOS/TOOLS/ProposalGC.ts (removes superseded/duplicate/absorbed entries). Sizes come from wc -c.
Three semantic moves: MERGE overlapping rules, TIGHTEN verbose ones, RELOCATE rarely-used detail to an on-demand reference (leave a stub + pointer).
The invariant: a trim never drops a distinct directive. If a merge would, keep the original.

Gotchas

USER files commit to the USER_DATA repo, not ~/.claude. LIFEOS/USER/** (OPERATIONAL_RULES, PROJECTS, the identity files) is a symlink into a separate private repo. Commit with git -C ~/.config/LIFEOS/USER …. A ~/.claude commit captures nothing under LIFEOS/USER/ — a false safety net.
The file can change mid-edit. The autonomic memory loop appends proposals to these files while you work. If a Write/Edit reports "modified since read", RE-READ before writing — a concurrent correction may have landed (this is how a real deploy-command fix was nearly reverted). Never write from a stale read.
Semantic merges must never drop a directive. Before applying any merge/tighten, confirm every proper noun, path, tool name, and imperative from the originals survives in the result. If one is missing, the merge is wrong — keep the original. Deterministic GC (superseded/dup/absorbed) is always safe; semantic edits are the risky class.
bun/bunx only, never npm/npx.
Deterministic first, always. Run ProposalGC before proposing any semantic edit — the free, zero-risk removals often clear enough that no judgment-call edit is needed.

Examples


/trim OPERATIONAL_RULES
# → shows 40,456 B → ProposalGC dry-run (0 removable) → ranks semantic trims (fold the
#   40-entry proposal tail, relocate skill-scoped directives to a reference)
#   → applies approved ones behind the safety gate → commits to USER_DATA → 28,043 B (−31%)

/trim
# → no arg: wc -c the always-on set, take the largest, then the same walkthrough

Execution Log

After completing the workflow, append a single JSONL entry:

bash
echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Trim","workflow":"Trim","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Workflows/Trim.md4 KB

Frequently asked questions

What does the Trim AI skill do?

Why use Trim on TypingMind?

Because you install it once and use it with any model. Trim 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 Trim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielmiessler/LifeOS/tree/main/LifeOS/install/skills/Trim. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Trim?

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 Trim?

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

Is the Trim AI skill free?

Yes. It is published on GitHub by danielmiessler 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Trim

Workflow Routing

Quick Reference

Gotchas

Examples

Execution Log

Bundled files

Frequently asked questions

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