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Markers

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bencium
markers

The shared contract for the machine-readable JSON marker blocks that bencium-loop commands emit at the end of a loop step — placement, content rules, and versioning. Referenced by every /bencium-* command that emits one; the per-command schemas stay in their commands.

Overview

Publisherbencium
Repositorybencium-loop
Skill namemarkers
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 Markers 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/markers
  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/markers .claude/skills/markers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Markers 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 Markers 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 Markers 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.

markers

Every loop boundary that produces a result ends with a fenced ```json block. CI scripts, dashboards, and follow-up commands locate that block and read it instead of scraping prose.

The rules below are identical for every marker. Each command defines only its own schema and how to populate it.

Placement

The marker is the last thing the step prints. Nothing after the closing fence — no prose, no banner close, no trailing whitespace. Downstream tools find it as the trailing fenced JSON, so anything after it breaks the parse.

Where a command emits more than one marker (/bencium-deploy emits one for SMOKE and one for the deploy), each is the last output of its own step.

Content

Markers report state, not opinion. No recommendations, no next steps, no commentary inside the JSON — those belong in the prose above it. A consumer reads a marker to learn what happened, not what someone thought about it.

Every marker carries the same four fields, then its own:

json
"_marker": "<name>",
"version": 1,
"ts": "<ISO8601 UTC>",
"phase": "<the loop phase that produced it>"

Report incomplete state rather than none. If a step stopped halfway, emit the marker with whatever is known and a result that says so. An incomplete record still beats no record when someone is trying to work out where things stopped.

Versioning

The schema is versioned with an integer (version: 1). Additive fields — new optional keys a consumer can ignore — do not bump it. A change that removes a field, renames one, or alters the meaning of an existing one does.

The markers in the loop

markeremitted by
bencium-build-result/bencium-next Phase B
bencium-verify-result/bencium-verify
bencium-smoke-result/bencium-deploy Step 5.5
bencium-deploy-result/bencium-deploy Step 6
bencium-rollback-result/bencium-rollback
bencium-retro-result/bencium-retro

Phase A (PLAN) emits no marker — it is the read-only gate, and the build marker already records the plan that was executed.

Frequently asked questions

What does the Markers AI skill do?

The shared contract for the machine-readable JSON marker blocks that bencium-loop commands emit at the end of a loop step — placement, content rules, and versioning. Referenced by every /bencium-* command that emits one; the per-command schemas stay in their commands.

Why use Markers on TypingMind?

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

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

Which AI models can use Markers?

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 Markers?

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

Is the Markers 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.

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