Invariants logo

Invariants

Community
bencium
invariants

The ordering and evidence rules the whole bencium-loop loop rests on, stated once with their reasons and their real enforcement status. Referenced by every /bencium-* command instead of being restated in each one. Read this before deciding whether a step may proceed.

Overview

Publisherbencium
Repositorybencium-loop
Skill nameinvariants
Stars
20
Forks
2
Bundled files
Instructions only
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bencium on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bencium/bencium-loop/tree/main/bencium-loop/skills/invariants
  4. 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.
2

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/bencium/bencium-loop.git /tmp/bencium-loop
mkdir -p .claude/skills
cp -r /tmp/bencium-loop/bencium-loop/skills/invariants .claude/skills/invariants
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Invariants 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 Invariants 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 Invariants 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.

invariants

Some pairs of actions have a required order, and some claims require evidence before they may be made. Getting either wrong rarely produces a loud error — the second step just quietly means less than it appears to.

These are stated here once. Commands reference this skill and carry only the rule specific to them.

What is actually enforced

Be precise about this, because the difference matters most in exactly the conditions where it is easiest to forget: a long session, a tired user, a model under context pressure.

invarianthow it holds
PLAN → BUILDhook — the edit is blocked (plan-gate.sh)
VERIFY → DEPLOYhook — the deploy command is blocked (deploy-gate.sh)
[deployed] rows only flipped by SMOKEhook — the edit is blocked (acceptance-gate.sh)
RED → GREENasked
evidence before a check is tickedasked
no row vanishes from a workflow resultcode, in the workflow scripts

The three hooks exist only on Claude Code. On Codex, opencode, and pasted chats every line above is a convention the model follows because the prompt says so. That works most of the time. It is not the same thing, and the loop should never claim otherwise.

The ordering invariants

PLAN → BUILD. No implementation file is edited before the user approves the plan. The plan is the spec; approving it is the only place the user gets to disagree cheaply. The one exception is the PROTOTYPE detour, which writes throwaway files to a disposable location and nothing else.

RED → GREEN. The declared failing test is observed failing, for the expected reason, before any implementation file is edited. A test written after the code tends to assert what the code does rather than what was wanted. Exceptions are declared in the plan (none — trivial (declared)) or logged as a bypass.

VERIFY (local) → DEPLOY. Deploy does not run while any check fails. The gate is the point of having a checklist at all.

DEPLOY → SMOKE. [deployed]-tagged rows are never evaluated against the working tree — only against the live URL, and only after deploying. Local pass is not shipped: environment variables, build mode, and the network are all different in the place users actually are. Only SMOKE may flip those rows, and it keeps the tag when it does (- [x] [deployed] X) so the row can never later be mistaken for a local check.

The evidence invariants

Evidence before a tick. A check is marked passed only with something you can point at — a file path, a grep hit, command output, an HTTP status. Never a recollection, never an inference from "the code looks right".

An unevaluated check is a failed check. If a check could not be run at all — a dead agent, no response, a missing fixture with no stated reason — it is FAIL. Not absent, not skipped, not passed. Zero evaluated checks is a FAIL, never a vacuous pass. A missing row reads as a row that was never on the list, which is how a silent drop turns into a false green.

A gamed pass is a FAIL. Green is necessary, not sufficient: the test must still assert the real behaviour. A weakened assertion, a skipped test, a hardcoded echo of current output, or a narrowed verify command all fail the check they appear to support.

Declaring your own

If a project step must run before another — database provisioned before secret request, auth before any user-route, migration before seed — declare it in .harness/rules.md under an "Ordering invariants" heading. The agent reads rules.md every session and will refuse step Y without step X.

Document the why on each one, not just the order. "Migration before seed (seed scripts assume the schema exists)" is enforceable. "Migration before seed" alone forces the agent to guess the reason, and a guessed reason permits a clever-but-wrong shortcut.

Adding a rule costs something

Instruction-following degrades as rule count climbs: past some point a new rule silently displaces compliance with an older one, and nothing announces when that line was crossed.

So a release that adds a hard rule should remove or subsume one. If the count goes up anyway, the CHANGELOG says why. tests/rule-budget.test.mjs holds the per-command line and rule budgets and fails when one is exceeded — which makes raising a budget a visible diff rather than a drift.

That test measures volume, not compliance. It tells you the surface is growing; it cannot tell you the rules are still being followed.

Frequently asked questions

What does the Invariants AI skill do?

The ordering and evidence rules the whole bencium-loop loop rests on, stated once with their reasons and their real enforcement status. Referenced by every /bencium-* command instead of being restated in each one. Read this before deciding whether a step may proceed.

Why use Invariants on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bencium/bencium-loop/tree/main/bencium-loop/skills/invariants. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Invariants?

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 Invariants?

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

Is the Invariants AI skill free?

It is published on GitHub by bencium. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇