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Flight Deck

Community
phuryn
flight-deck

'What's the status', 'where were we', a progress report or status deck; long work that must survive compaction: multi-hour runs, background jobs, waiting on a decision.

Overview

Publisherphuryn
Repositoryproduct-engineering
Skill nameflight-deck
Stars
189
Forks
27
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Flight Deck 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/phuryn/product-engineering/tree/main/agent-harness/skills/flight-deck
  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/phuryn/product-engineering.git /tmp/product-engineering
mkdir -p .claude/skills
cp -r /tmp/product-engineering/agent-harness/skills/flight-deck .claude/skills/flight-deck
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flight Deck 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 Flight Deck 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 Flight Deck 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.

Flight Deck — surviving your own context window

An agent's memory of what it is doing is the first casualty of a long session. Tool output scrolls away, context gets summarized, and what remains is a plausible narrative with the operational details sanded off. The work keeps running; the agent's model of it quietly goes stale. Then it re-runs a paid job it already ran, reports a number from memory, or forgets that something is blocked.

The fix is not a better memory. It is writing state down where it cannot be summarized.

The flight deck is STATUS.md, LOG.md and the owner's page. In any session where the Artifact tool exists, build the page in the first pass as an artifact with a database (capabilities: {db: {}}) from assets/owner-page.html, following references/owner-page.md; use OWNER.md only when the tool does not exist. A test run, or a run with nothing for the owner to decide yet, still builds the page: it carries the status too, and a test is where the page gets exercised.

The two files (per area, not per repo)

STATUS.md — the instrument panel. Rewritten in place. Only ever describes NOW.

Never append to it. A status file that grows is a log wearing a status file's name, and the reader cannot tell which line is still true. Sections that earn their place:

  • RUNNING NOW — with process ids, log paths, and what each thing is waiting on
  • QUEUED — in order, so "what is next" is not a judgement call
  • BLOCKED — and why, and what would unblock it
  • HELD — finished work deliberately not shipped yet, and who owes the decision
  • AWAITING — the questions you cannot answer yourself
  • DONE — one line each, enough to prevent redoing it
  • EVIDENCE — measurements with their actual numbers (see below)
  • STANDING RULES — the constraints a fresh reader would otherwise violate

LOG.md — the flight recorder. Append only. Explains WHY.

Decisions, corrections, dead ends, the reasoning behind a call. Status answers "where are we"; the log answers "how did we get here, and what did we already rule out". Both are needed and they are not the same file.

Optionally, when the machinery is non-obvious, a committed how-it-works doc. Status and log are volatile; that one is durable and belongs in version control and in your routing file.

The owner's page

STATUS.md and LOG.md are for you. The owner's page is for the person who isn't watching the session (the owner), so they never read the transcript to find out where the work stands or what you need. It holds only what needs them (a decision, a go, a task only they can do) under a short status: they open it, answer, save, and you read the answers at your next check-in. It is STATUS.md's AWAITING section made answerable. Without the Artifact tool it is OWNER.md, with the same shape. Before building it, open references/owner-page.md (the data, publishing, the loop) and assets/owner-page.html (the page you copy). (2026-10-27: of two sessions that ran this skill the same day, one read only this file, called the page overkill for a test, and built it only when the owner asked for it as an artifact.) The rules below come from a monitoring product built 2026-10-21 to 25, where the owner ran the whole build from such a page.

A progress report starts here. Asked for "where we are", a status report, "what's done and what's next" or a deck presenting the progress, update STATUS.md and the owner's page first and project the report from them; a deck is a view of the flight deck, never a separate account of the work. (2026-11-01, an evals product: asked for a deck of the progress and open decisions, an agent built slides from nothing, and the owner corrected it: the flight deck is the core way to present where the work stands.)

What is on it

  • At the top, where we are, short: the phase ("3 of 5") and its milestones with the current one marked, then at most three lines each of what was just built, what's in progress and what's next, and how many things wait on them. Project it from STATUS.md, not every detail; a status block nobody reads to the end is no status.
  • Only what needs them: a decision, a go on a one-way door, or a task only they can do. Never status, receipts, finished work, or anything you could do yourself. (2026-10-24: one item asked the owner to create database logins, which the agent then made itself. The owner's reply: "I'm not sure what you need… should I put it where exactly?")
  • Each item says what you need, where (the link, the file or the setting), why only they can, your recommendation (preselected), and what happens after they answer.
  • Check every step against the product before it goes on the page. A checklist sent the owner to open an incident with a button the product doesn't have.
  • Never collect a secret. Name the file or setting the key goes into.
  • A go typed on the page doesn't clear your harness's permission check for a one-way door. The page asks; the go is typed in the chat (2026-10-24, a production release).

Once you have read an answer, the item and the answer are deleted, except a gate, which stays until the go is typed in chat: a note on it deletes only the answer. No reply on the page, no "settled" list, nothing showing that their feedback was processed. (2026-10-24, the owner: "I don't need answers when something is done." Replies left on finished items had left him not knowing what to do with them.) Work in this order:

  1. Record the decision (the product's decision log, STATUS.md QUEUED for the work it creates, LOG.md for the why).
  2. Delete the item and its answer in one write. For a gate, delete only the answer: the gate stays until the go is typed in chat.
  3. Act. On a gate, only once the go is in chat.
  4. Confirm in chat, in one line.

Acting first leaves answered items on the page for as long as the work takes.

Check at fixed points, because nothing notifies you when they save: each stage boundary, while a build or review runs, before ending a turn, after a compaction, and whenever they say they answered. Answers the owner never mentioned were caught only this way. An answer given in chat closes its item too, and a gate only when it is the go.

Make Save trustworthy. The owner asked "Can you really read my answers?" (2026-10-22). The page says that Save is what you read and that nothing notifies you. On first use, quote one saved answer back. Put the link in every chat message that mentions the page ("What was the url of the artifact", 2026-10-23).

The rule that makes it work

Update STATUS.md on every state change — a job starts, finishes, fails, gets published, a decision lands, something becomes blocked. Not "at the end", not "when asked". The moment it changes. A status file that is 80% true is worse than none, because it is trusted.

Keep it short enough to stay true. If maintaining it is a burden, it is too detailed.

Write down evidence, not just tasks

The highest-value lines are measurements: probe results, benchmark numbers, verdicts, costs, "we tested X and it does not work". Losing task state costs you a re-read. Losing evidence costs real money and credibility: you re-run a paid experiment, or worse, you restate a number from a summary and get it wrong. Record the numbers and the verdict, not the conclusion alone.

Where to put them

One pair per area of work, not per repository. A repo with three unrelated efforts wants three pairs; three repos serving one effort want one. The area is the unit someone would ask a status question about.

Put them where they survive but do not pollute: a scratch or working directory is right for volatile state. Then cross-link them — from the log to the status, from any long-lived doc to both — so a fresh reader lands on them by accident rather than by luck.

Reading them after a compaction

STATUS.md first, then skim LOG.md for the last few decisions, then verify: check that a process listed as running is actually running. Do not trust the file over reality — reconcile, then fix the file. A stale status file caught early is a small problem; one trusted blindly is how you report that a job finished when it crashed an hour ago.

When NOT to use this

Short sessions, single-shot tasks, anything that fits comfortably in one context window. The overhead is real and pointless there. This pattern earns its keep exactly when a session will outlive its own memory — which you can usually tell at the start, from the presence of background jobs, queues, or a run measured in hours.

How you can tell it worked

After a compaction, one read of the status board says what is running, what is blocked and on whom, and every line of it still matches what is on disk and what is running.

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 Flight Deck AI skill do?

'What's the status', 'where were we', a progress report or status deck; long work that must survive compaction: multi-hour runs, background jobs, waiting on a decision.

Why use Flight Deck on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phuryn/product-engineering/tree/main/agent-harness/skills/flight-deck. 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 Flight Deck?

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 Flight Deck?

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

Is the Flight Deck AI skill free?

Yes. It is published on GitHub by phuryn 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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