Preflight logo

Preflight

Community
ogulcancelik
preflight

Use fresh-context specialists to challenge regression risk before implementation, then verify the smallest correct change. Use only when the user says "preflight," asks for a risk gate, or explicitly requests this workflow. Never auto-invoke it.

Overview

Publisherogulcancelik
Repositoryagent-skills
Skill namepreflight
Stars
81
Forks
1
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Preflight 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/ogulcancelik/agent-skills/tree/main/skills/preflight
  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/ogulcancelik/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/preflight .claude/skills/preflight
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Preflight 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 Preflight 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 Preflight 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.

Preflight

Preflight reduces implementation bias. The implementing agent becomes attached to its diagnosis and design. A fresh specialist that sees the problem and code, but not the proposed solution, can find regressions before the design is locked.

The flow is:

investigate → risk challenge → concise contract → approve → implement → verify → simplify → finish

Rules

Use this skill only when the top-level user explicitly requests preflight or a risk gate. Never invoke it from a delegated child. If preflight was not requested, suggest it once and wait.

The main agent owns investigation, scope, implementation, and communication. Children only challenge, verify, review, or simplify. Give each child one job. Reuse it only to recheck its own findings.

Prefix every child assignment with:

You are a bounded preflight child. Do not invoke preflight, delegate work, or spawn agents. Complete only the assigned analysis and return it to the parent.

Always run the pre-implementation risk challenge. For meaningful code changes, also run a contract verifier and regression reviewer after implementation. Run a simplifier when the patch adds meaningful structure. Add an evidence challenger only when the reproduction is uncertain or the change is unusually risky. Do not create agents merely to fill a process.

1. Investigate

Read repository instructions and trace the real execution path before planning. For bugs, prefer a real user-path reproduction, then an existing integration signal, then an owning-layer test. If none is available, label the diagnosis as a hypothesis. For features, define one observable acceptance signal.

Separate facts from assumptions. Investigation cannot expand the request.

2. Run the independent risk challenge

Give a fresh agent the raw request, evidence, repository instructions, relevant code, and existing tests. Do not give it the main agent's diagnosis, implementation plan, architecture, or proposed patch.

Use this prompt:

Find concrete regressions this requested change could cause. For each finding, cite the existing behavior, exact code path, evidence, and causal failure chain. Do not suggest implementation designs, tests, mitigations, general improvements, defensive hardening, or hypothetical risks without evidence. Report unknowns separately.

The child returns only concrete risks, blocking unknowns, or no findings.

A finding is not automatically new scope. The main agent classifies it:

  • Required: needed to satisfy the request, fix the observed failure, protect demonstrated existing behavior, or prevent a concrete correctness, security, data-loss, or resource-safety failure created by the patch.
  • Watch: worth checking against the finished patch, but not supported well enough to add behavior or machinery.
  • Discard: speculative, adjacent, defensive, or unrelated.

Required risks enter the contract. Watches enter the review checklist but create no code, abstraction, branch, or test. Discarded findings disappear. Investigate a blocking unknown instead of guessing.

For localized bug fixes, default to one required behavior and one regression test. Promote a risk to required only when it is reproduced, lies on a supported normal path that the patch changes, or the patch necessarily creates the failure. Unchanged control flow, unlikely interleavings, recovery mechanics, and merely reachable edge cases stay watches unless they meet that bar.

3. Present the contract

Give the user one short, plain-language brief:

Problem: what is changing and the evidence.

Contract: required observable behavior.

Protected behavior: existing behavior directly exposed by the changed path. Do not include unchanged execution-order details or implementation recovery mechanics merely to force dedicated proof.

Non-goals: adjacent work that will not be implemented.

Risk challenge: required risks, meaningful watches, and blocking unknowns.

Proof: fail-before/pass-after signal and repository checks.

Keep it to one screen when practical. Do not show internal ledgers, mechanism counts, orchestration details, or specialist transcripts. Use requirement IDs only when they prevent real ambiguity.

Ask the user to approve the contract before editing project files. Ask again only if later evidence changes required behavior, scope, or a protected invariant—not for ordinary implementation choices.

4. Implement the smallest contract

Implement only approved behavior. Never implement watches or optional hardening.

Prefer the least new state, control flow, API surface, and test machinery. Every new mechanism must prevent a distinct required failure. If a mechanism creates recovery branches or more tests, first try removing it.

Capture fail-before and pass-after evidence when practical. For a localized bug, start with one regression test. Add another only when the first cannot prove a distinct required behavior. Do not create one test per contract sentence. Unchanged paths may be protected by code inspection and the existing suite. Add tests for required behavior or distinct regression boundaries, not speculative paths or implementation details. Reuse existing harnesses. Run targeted checks, then repository-required checks.

5. Verify with specialists

Give post-implementation specialists the raw request, approved brief, diff, evidence, and check results. Keep the jobs separate.

Contract verifier: Check that the patch and evidence satisfy the approved contract. Report only missing, contradictory, or unreliable proof for changed or newly required behavior. Do not demand one focused test per contract item; accept direct code-path evidence and existing broad checks for unchanged behavior. Do not ask for redesign, hardening, or new requirements.

Regression reviewer: Find regressions introduced by the patch. Every blocker must cite severity, exact code path, existing behavior at risk, and a causal failure scenario. Check earlier watches against the actual diff. “Could be more robust” is not a blocker.

Evidence challenger: Use only when the signal is uncertain or risk is high. Check whether the reproduction reaches the real owner and whether the proof can falsify the contract. Do not review code style.

The main agent validates each finding. Fix concrete contract or regression failures, rerun affected checks, and ask the same specialist to recheck its resolved findings. Do not turn suggestions into scope.

6. Simplify

If the patch adds state, phases, recovery branches, abstractions, API surface, or substantial tests, ask a simplifier what can be removed while preserving the contract. It cannot weaken or expand required behavior.

Apply useful removals and rerun checks. If production behavior or control flow changed, ask the regression reviewer to check the final diff again.

7. Finish

Run final repository checks. Report only:

Changed: implemented behavior.

Evidence: fail-before/pass-after and checks.

Independent review: concrete risks found and resolved.

Simplified: meaningful complexity removed, when applicable.

Remaining uncertainty: unavailable validation or residual risk.

Omit empty sections. Do not narrate the process unless the user asks.

If fresh-context agents are unavailable

Use another isolated session supported by the harness. If independent review is unavailable, tell the user and ask before using same-context review. Never call same-context review independent.

Frequently asked questions

What does the Preflight AI skill do?

Use fresh-context specialists to challenge regression risk before implementation, then verify the smallest correct change. Use only when the user says "preflight," asks for a risk gate, or explicitly requests this workflow. Never auto-invoke it.

Why use Preflight on TypingMind?

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

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

Which AI models can use Preflight?

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 Preflight?

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

Is the Preflight AI skill free?

Yes. It is published on GitHub by ogulcancelik 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.

View all

Set up your own AI workspace now

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