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Forward Implementation First

Community
Vuk97
forward-implementation-first

Keeps an agent building and validating real output instead of servicing its own bookkeeping. Use for multi-stage pipelines, capability roadmaps, long-running implementation loops, migrations, staged data or build runs, and any run where administrative hashes, locks, receipts, dashboards, certification markers, or progress metadata can block correct work. Use when an agent refuses to advance, rewinds finished stages, or reruns unchanged work because a marker is missing or stale.

Overview

PublisherVuk97
Repositoryforward-implementation-first
Skill nameforward-implementation-first
Stars
180
Forks
4
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 Vuk97 on GitHub. Read the source before you install it.

Installation

Install the Forward Implementation First 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/Vuk97/forward-implementation-first/tree/main
  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/Vuk97/forward-implementation-first.git \
  .claude/skills/forward-implementation-first
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Forward Implementation First 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 Forward Implementation First 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 Forward Implementation First 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.

Forward implementation first

Build working capability and correct output before administrative bookkeeping. Apply this contract both while building a system and while running it.

Scope

This skill applies to:

  • infrastructure and roadmap implementation;
  • input intake and intermediate artifact production;
  • transformation, derivation, and analysis stages;
  • test construction, runtime verification, and measurement;
  • publication of final output and closeout;
  • manual invocation of any authorized pipeline stage.

It applies whether the action runs through a full orchestrator, a focused command, or a manual stage invocation.

Decision rule

Before each action, classify it as one of:

  1. Semantic implementation: builds or connects a producer, consumer, adapter, runtime path, schema, fixture, or final output.
  2. Focused validation: tests the changed dependency cone through behavior, schema, counts, samples, conservation, consistency, nontruncation, or measured resources.
  3. Administrative bookkeeping: generates or repairs hashes, locks, receipts, dashboards, certification markers, progress metadata, or presence-only records.

Choose categories 1 and 2. Skip category 3 unless the user asks for it or the artifact is itself part of the product. When administrative work blocks a path without protecting correctness, remove that dependency from the path.

Hard constraints

  • Build the producer and consumer before polishing status or certification surfaces.
  • Do not generate, repair, compare, or propagate administrative hashes.
  • Do not create or wait on filesystem locks.
  • Do not rerun an unchanged stage to regenerate a receipt or marker.
  • Never invalidate valid output because an administrative receipt, hash, certification marker, dashboard row, or progress record is missing, stale, or incompatible.
  • Never move the forward cursor backward for an administrative metadata change.
  • Never invalidate a wide stage range when only one producer or consumer changed. Replay only that producer's dependency cone.
  • Do not treat certification, receipts, dashboards, progress metadata, or artifact presence as the product.
  • Remove hash-only, lock-only, receipt-only, and presence-only credit or gates from execution paths.
  • Do not claim a capability works because its file exists. Run the smallest changed dependency cone and inspect the output.
  • Publish valid output after focused validation. Do not add an extra review cycle when no defect remains.

Forward cursor and manual execution

A stage may move backward or be replayed only when at least one real condition holds:

  • its input meaning changed;
  • its target or pinned revision changed;
  • its output is malformed, truncated, nonconserving, internally inconsistent, or incompatible with the consumer;
  • an observed run disproves the earlier static result;
  • the changed producer's declared dependency cone requires replay.

Missing or stale administrative metadata is not one of those conditions.

When the orchestrator refuses to run a stage only because of a receipt, certification marker, progress record, administrative hash, or lock:

  1. Run the exact stage manually.
  2. Validate its output with the focused checks below.
  3. Publish the valid output atomically.
  4. Continue from the forward cursor.
  5. Remove or downgrade the administrative-only gate in the execution path.

Do not refuse an authorized manual run because the full pipeline cannot issue a receipt. Do not push the user toward another agent or tool to get around your own bookkeeping.

Required validation

Use the checks that match the change:

  • runtime behavior and exit status;
  • schema and type validity;
  • exact input, output, accepted, rejected, and unknown counts;
  • deterministic first, middle, and last samples;
  • identity and partition conservation;
  • join consistency and flag polarity;
  • nontruncation and bounded diagnostic output;
  • wall-clock time and peak memory for material stages.

Hashes may identify inputs or revisions, but they never grant correctness, execution, or roadmap credit.

Exceptions that stay substantive

Bookkeeping is cheap to skip. Evidence is not. Keep these:

  • Integrity that belongs to the product itself. A checksum your users verify, a signature your format requires, a hash that is part of the output contract. Those are features, not paperwork.
  • Input and revision identity when it decides which version of the thing you are operating on. Getting that wrong means correct work on the wrong target.
  • Real measurement: tests that assert results, benchmarks, reproductions, end-to-end runs, and whatever your domain treats as proof.
  • The difference between coverage and consequence. A run that exercises a path is not a check that the path produced the right answer.
  • An execution record is substantive when it carries the command, the input, the result, and the expectation it was checked against. Its absence blocks that specific claim. It does not invalidate unrelated earlier stages.

Parallel work invariants

These are behavior contracts, not a scheduler. Your runtime owns scheduling, concurrency, resource contention, and worker topology, and this skill carries no policy about them. Keep these invariants under any scheduler:

  • Exactly one writer owns publication, cursor movement, acceptance, and conclusions. Parallel workers prepare, implement, inspect, and test nonoverlapping support work. They never become competing truth.
  • Verify a worker's output locally before consuming it. A worker's report states intent, not result.
  • Forward progress does not wait for every worker to finish. Consume completed output when the dependency that needs it is reached.
  • Do not manufacture busywork to occupy idle workers. An idle slot is cheaper than fake work.
  • If a run exposes a defect in a stage, fix that stage before rerunning its dependency cone.
  • If the next proposed action is bookkeeping-only, select the next unresolved producer, consumer, adapter, or output instead.

If no system owns scheduling in your setup, write a small profile for it yourself; examples/execution-profile.md is a starting point. The skill never loads it on its own.

Status reporting

Report implemented behavior and measured output first. List blockers literally. Keep infrastructure progress separate from evidence about the output. Do not turn administrative completion into a substitute for working capability.

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 Forward Implementation First AI skill do?

Keeps an agent building and validating real output instead of servicing its own bookkeeping. Use for multi-stage pipelines, capability roadmaps, long-running implementation loops, migrations, staged data or build runs, and any run where administrative hashes, locks, receipts, dashboards, certification markers, or progress metadata can block correct work. Use when an agent refuses to advance, rewinds finished stages, or reruns unchanged work because a marker is missing or stale.

Why use Forward Implementation First on TypingMind?

Because you install it once and use it with any model. Forward Implementation First 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 Forward Implementation First in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Vuk97/forward-implementation-first/tree/main. 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 Forward Implementation First?

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 Forward Implementation First?

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

Is the Forward Implementation First AI skill free?

Yes. It is published on GitHub by Vuk97 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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