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Loop Engineering

Community
Mark393295827
loop-engineering

Use when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.

Overview

PublisherMark393295827
Repositorythird-brain-v7-skills
Skill nameloop-engineering
Stars
141
Forks
22
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mark393295827 on GitHub. Read the source before you install it.

Installation

Install the Loop Engineering 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/Mark393295827/third-brain-v7-skills/tree/main/skills/loop-engineering
  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/Mark393295827/third-brain-v7-skills.git /tmp/third-brain-v7-skills
mkdir -p .claude/skills
cp -r /tmp/third-brain-v7-skills/skills/loop-engineering .claude/skills/loop-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Loop Engineering 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 Loop Engineering 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 Loop Engineering 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.

Loop Engineering

<skill_contract> A repeatable task with inspectable state, finite budgets, permissions, and an independent verifier. A validated Trigger -> Execute -> Verify -> State contract plus resumable run receipts. The declared metric or stop condition is supported by fresh validator and verifier evidence. <non_goals>Dependency-graph orchestration, unbounded autonomy, or self-certified completion.</non_goals>

Build loops only when repeated execution creates evidence. Every loop needs admission, a validated contract, durable state, independent evaluation, bounded retries, stop/recovery rules, and a final receipt.

Usage Template

Provide: objective, trigger, scope/non-goals, inputs, state/artifact paths, metric, verifier, permissions, budgets, stop condition, recovery, and write-back. See references/ci-repair-loop-example.md for a worked contract.

Workflow

Select one mode:

  • Goal: run until a defined end state or cap.
  • Loop: poll/iterate while eligible work exists.
  • Automation: start from an external schedule/event; the trigger is not execution evidence.
  • AutoResearch: vary experiments against an objective metric in a sandbox.

Admit only if work is repeatable, outputs are inspectable, a verifier exists, failures are recoverable, and autonomy is worth the orchestration/review cost. Otherwise use a one-shot workflow.

Use graph-engineering instead when explicit data dependencies, independent branches, typed joins, or node-local recovery create measurable value. A Graph node may use this Loop contract for local repetition; Graph width does not replace finite Loop depth.

<unknowns_gate>

Classify unknowns as known, probeable, testable, or blocked. Missing objective, verifier, permission boundary, budget, or recovery is NEEDS_INPUT; do not infer these controls from intent. Unknown implementation details may be resolved inside the loop only when the probe is bounded and reversible.

</unknowns_gate>

Write this contract before acting:

text
Objective:                 Mode: Goal | Loop | Automation | AutoResearch
Trigger:                   Scope:                 Non-goals:
Owner:                     Inputs:
Artifacts path:            State path:            Work clock:
Success metric:            Evidence:              Verifier:
Topology: single-agent | maker-checker | manager-workers
Max iterations:            Time limit:            Budget:
Review budget:             Stop condition:
Write-back:                Permission boundary:   Recovery:

Validate it with scripts/validate_loop_contract.py --strict. Then iterate:

  1. Observe: load durable state, fresh environment evidence, budgets, and last error.
  2. Orient: update one hypothesis; choose the smallest action that can change the metric.
  3. Decide: check scope, permissions, expected evidence, and rollback.
  4. Act: execute one bounded action and capture artifact/diff/receipt.
  5. Verify: use a deterministic check or independent checker; compare metric and guardrails.
  6. State: append diagnosis, action, evidence, delta, budget, and next decision atomically.
  7. Stop/continue: stop on success, cap, permission boundary, regression, repeated signature, or no useful work.

Normalize the model/runtime termination signal after every action. complete still requires the declared verifier; tool_request returns proposed arguments to the host permission gate; checkpoint/truncation persists state before any continuation; refusal, error, or unknown escalates. Never infer completion from fluent prose or from the word "stop" alone.

For maintenance and queue loops, declare an allowed NO_OP outcome and its eligibility query. A quiet iteration is successful only when the query proves there was no eligible work, output count is within policy, and no side effect occurred.

Use single-agent by default, maker-checker for ambiguous/high-risk evaluation, and manager-workers only for genuinely independent work with an explicit integration gate.

If a validated Graph owns the dependency topology, this skill owns only the bounded retry behavior inside its declared loop nodes.

The verifier must test the declared result rather than reward activity. Check evidence freshness, metric movement, guardrails, scope, and state replay. For external or consequential actions, require approval and verified rollback before crossing the boundary.

<retry_policy>

max_attempts equals the contract's finite max iterations. Retry only after a named diagnosis and a changed input, tool, scope, or strategy. Stop on the same failure signature twice, metric regression, exhausted review budget, or NO_PROGRESS.

</retry_policy>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus contract version, trigger receipt, hypothesis, action, normalized termination reason, artifact/diff, metric/guardrail delta, permissions, output count, no-op receipt, work clock, and recovery point. Append iterations; write current state atomically.

</state_contract>

Failure Protocol

  • NEEDS_INPUT: a mandatory contract field is absent; do not start.
  • BLOCKED_PERMISSION: the next action crosses authority; checkpoint and request approval.
  • VERIFY_FAILED: result or guardrail fails; rollback/regroup before another attempt.
  • NO_PROGRESS: the same signature repeats or the metric is unchanged after a changed attempt.
  • BUDGET_STOP: any iteration, time, tool, cost, or review cap fires. max_attempts is always finite.

Output Contract

Return status, result (metric/end-state decision), evidence (validator and iteration receipts), unknowns, and next_action (stop, retry, approval, recovery, or handoff).

Edge Cases

  • A scheduled job fired but produced no run receipt: status is triggered, not completed; inspect executor state.
  • The model ends because its context or token budget is exhausted: checkpoint and return BUDGET_STOP or resume from state; do not label truncation success.
  • A queue poll returns zero items: accept NO_OP only after the declared query and side-effect check pass.
  • The metric improves while a safety guardrail regresses: rollback and return VERIFY_FAILED; never optimize the headline metric alone.

Success Metrics

  • The strict validator passes before execution.
  • Every iteration changes evidence, state, or diagnosis within finite budgets.
  • A fresh verifier supports the final status and residual risk.

Quality Gates

  • Trigger, owner, topology, budgets, stop, recovery, and write-back are explicit.
  • Builder opinion is not the only verifier.
  • Termination classes and any legal no-op have host-owned routing and evidence.
  • State replay recovers the next decision losslessly.
  • External mutation requires approval and rollback.

</skill_contract>

Bundled files

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

Frequently asked questions

What does the Loop Engineering AI skill do?

Use when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.

Why use Loop Engineering on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mark393295827/third-brain-v7-skills/tree/main/skills/loop-engineering. 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 Loop Engineering?

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 Loop Engineering?

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

Is the Loop Engineering AI skill free?

Yes. It is published on GitHub by Mark393295827 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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