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Accelerated Mode

Community
bencium
accelerated-mode

Single source of truth for the bencium-loop opt-in workflow fan-out — the two gates that must both hold, the fallback rule, the guardrails, and how a run is recorded in the marker. Referenced by every /bencium-* command that offers an accelerated path. Read this skill before invoking any Workflow.

Overview

Publisherbencium
Repositorybencium-loop
Skill nameaccelerated-mode
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 Accelerated Mode 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/accelerated-mode
  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/accelerated-mode .claude/skills/accelerated-mode
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Accelerated Mode 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 Accelerated Mode 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 Accelerated Mode 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.

accelerated-mode

The loop runs one step at a time, each gated. On Claude Code there is an optional faster gear for the slow, repetitive parts: a dynamic workflow — a small program that fans work out across many short-lived agents at once instead of walking a list one item at a time.

This skill holds the rules that are identical across every surface that offers one. The commands hold only what differs: which script, what arguments, and how to fold the results back in.

The two gates — both must hold, every time

  1. Runtime gate. A Workflow tool must be available. It exists only in Claude Code. On Codex, opencode, or any copy-paste chat surface there is no Workflow tool. Do not emulate one with extra Task subagents — that would break the 3-agent cap and produce an unbounded fan-out on a surface that was never designed for it.
  2. Opt-in gate. The user asked for it this run — "verify with a workflow", a workflow argument, or an ultracode request. Never automatic. A single run can spawn many agents (up to 16 concurrent, 1000 over a run) at real token cost, so it is always a deliberate escalation the user chose.

If either gate is false, run the serial path. Not a degraded version of it — the same path that has always been the default.

Fallback is the design, not a safety net

The serial path is the portable one: it works on every surface the loop runs on. The accelerated path can only ever be a speed-up on one of them. So every accelerated step is written to fall through cleanly, and nothing in the loop may depend on a workflow having run.

Detection is automatic: no Workflow tool means not Claude Code, which means serial.

Guardrails

  • Never fan out a gate, and never fan out a build. The PLAN→BUILD approval, the verify verdict, and the deploy decision stay serial and human-facing. A workflow may read and evaluate inside an approved step; it never replaces the step's gate, and it never writes code.
  • A workflow is advisory to the gate. Its output flows into the command's own report and marker. It does not deploy, does not check off [deployed] rows, and does not decide anything the serial path wouldn't.
  • Every row comes back. The scripts return one result per item they were given — an item that could not be evaluated returns fail with unverified: true, never a missing entry. Report those as failures and say how many. A missing row reads as a row that was never on the list, which is how a silent drop becomes a false pass.
  • Take the stricter verdict. Where a workflow result and your own differ, take the stricter one unless you can refute it with concrete evidence. Same reconciliation rule as the independent-evaluator step.
  • The 3-agent cap is untouched. The cap governs Task subagents. Workflow mode is a separate, named escalation — not a loosening of the cap, and not a licence to spawn subagents outside one.

Recording the run

Every marker that can be produced by an accelerated step carries two optional, additive fields at version: 1:

json
"mode": "serial" | "workflow",
"agents": <int>

Set mode: "workflow" and the agent count only when a workflow actually ran; otherwise mode: "serial" and agents: 0. Consumers that predate these fields ignore them — the serial path is byte-for-byte the old behavior.

Where the scripts live

Prefer the project copy at .claude/workflows/<name>.mjs; fall back to ${CLAUDE_PLUGIN_ROOT}/workflows/<name>.mjs. /bencium-init copies them into a project on Claude Code as a convenience — a project without them still gets workflow mode through the plugin-root fallback.

SurfaceScriptShape
/bencium-verify Step 3cverify-acceptance.mjsevaluate each local row, then a fresh skeptic tries to refute each PASS
/bencium-deploy Step 5.5smoke-deployed.mjsone agent per [deployed] row, real HTTP against the live URL
/bencium-init Step 2brownfield-scan.mjsmulti-lens read-only sweep, then one synthesis

Mechanical stages default to Haiku; adversarial verification and synthesis to Sonnet.

Every accelerated path is read-only

All three read, evaluate, and report. None of them writes code, and none of them is on the build path. That is the boundary, and it is deliberate.

A parallel build fan-out existed briefly and has since been removed. It bought throughput on the one part of the loop that was never the bottleneck — the bottleneck is the human reading the plan — and being the only fan-out that wrote code, it was where most of the bugs lived. If a task is really N independent mechanical edits, split it at the PLAN gate and build the parts one at a time. Slower in wall-clock, and the wall-clock was never the constraint.

Frequently asked questions

What does the Accelerated Mode AI skill do?

Single source of truth for the bencium-loop opt-in workflow fan-out — the two gates that must both hold, the fallback rule, the guardrails, and how a run is recorded in the marker. Referenced by every /bencium-* command that offers an accelerated path. Read this skill before invoking any Workflow.

Why use Accelerated Mode on TypingMind?

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

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

Which AI models can use Accelerated Mode?

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 Accelerated Mode?

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

Is the Accelerated Mode 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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