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Agentic Walkthrough

Organization
sno-ai
agentic-walkthrough

Prove that a reported run caused its intended real effect. Walk the executor path from entry to effect, capture one observable per hop, and report a result a no-op could not produce.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill nameagentic-walkthrough
Stars
604
Forks
415
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

    Published by sno-ai on GitHub. Read the source before you install it.

Installation

Install the Agentic Walkthrough 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/sno-ai/sno-station-skills/tree/main/skills/J-peer-review-and-audit/agentic-walkthrough/skill
  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/sno-ai/sno-station-skills.git /tmp/sno-station-skills
mkdir -p .claude/skills
cp -r /tmp/sno-station-skills/skills/J-peer-review-and-audit/agentic-walkthrough/skill .claude/skills/agentic-walkthrough
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentic Walkthrough 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 Agentic Walkthrough 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 Agentic Walkthrough 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.

Agentic Walkthrough — take the program's seat

The stance is the whole method. You are not a reviewer standing outside the code with a checklist; you are the thing the source describes — the job, the stage, the handler — and you run it in your head, hop by hop, exactly as it would run, noticing every switch you pass and every place your output goes. Only then do you turn to the real system and make it show you that what you just walked actually happened. The principle: BE the executor, then make the executor prove itself.

Ordinary debugging starts from a failure — an error, a red test, a wrong number. This starts from the opposite and much quieter case: everything reports success and you still do not know whether the work happened.

That case is real and it is not rare. A stage that cannot do its work can still report done. A job can start, finish, write a trace, and pass its own verification while touching nothing. No test suite catches this, because no assertion in the suite is false.

Three moves, in order. How far to take each is your judgment — often the first cannot finish without the ones after it.

1. Walk the path — be the program, read, don't run

Put yourself where the code sits and go hop by hop from entry point to effect. You are reading, not executing, which is the whole point: a live run cannot show you the branch it never took.

Three questions at each hop are usually enough:

  • What switch controls this hop, and what is its value right now? A config key, an env var, a feature flag, a job type, a default argument.
  • If that switch is off, does it fail loud or continue quiet? Quiet defaults are where these bugs live. Loud failures are already visible — you are hunting the silent ones.
  • What does this hop actually do — which line? If you cannot name the line, you have not shown the hop does anything.

The last hop is the one everyone skips and it is usually the decisive one: is this output visible to whatever consumes it downstream? A stage that runs perfectly and writes where nothing reads is indistinguishable from a stage that never ran. Check the reader's path, not the writer's — they are frequently not the same file, the same directory, or the same database.

Where to start and how deep to go is judgment. Go where the switches are dense, or where the evidence gets thin.

2. Probe every hop that has to be real

The walk tells you which hops matter. A probe is how you make one of them prove itself.

Keep each probe narrow: one observable that only the real thing could have produced. A broad health check is worth little here, because everything passes it — that is the whole problem. The walk already told you what this hop is supposed to do; probe exactly that.

These classes come up again and again, and each fails in a way that looks fine from the caller's side. Treat them as places to look, not a checklist to complete.

  • Stored state — the rows themselves. Take a concrete failing case and pull out the actual record behind it. A log says a write happened; only the row can distinguish a correct write from one that hit the wrong record, wrote the wrong value, or was counted but never committed. Landed → the defect is downstream. Did not land → the write logic is wrong, and nothing built on top of it is worth tuning. Expect to need the product's own accessor — encryption, a key held elsewhere, a custom codec. A generic CLI failing to open the store is normal, not a wall.
  • Database identity. Which file did the process actually open — resolve the real path — and is it the same one the reader queries? Path resolution usually consults several sources in an order nobody remembers, and the first one wins.
  • Model and API calls. Did a request actually leave? How many, to which endpoint, under which model or account? Then the part usually skipped: does the response contain what the caller assumes? A 200 with an empty body sails through an if (ok) check. Zero calls is the loudest possible answer and the easiest one to miss.
  • Accelerator and environment setup. A path that silently falls back to CPU still finishes, just differently. Ask which device the work actually landed on, not whether setup returned without error. Same shape for any "configure, then hope" step.
  • File input and output. Did the bytes land where the consumer looks? A temp directory that gets cleaned, a relative path resolved from a different working directory, an output written somewhere nothing reads — all of these leave a successful write behind.
  • Child processes and third-party services. Did it actually start, is the thing you matched actually it, is it listening, and did it stay up — or exit a second later while the parent carried on?

3. Report a number, not an impression

This is the one part worth being strict about, because it is what separates a gate from a reading exercise.

Finish with a number a no-op could not have produced, plus the line where each action happens. N rows changed, M requests sent, and here is where I can see those N rows differ.

  • Zero is a failure of the walk, not a result. It means you found the path broken.
  • "The walk went well" is not a finding. Neither is a summary with the numbers dropped.
  • If you cannot name the line, that action is not proven to run.

Before any absence becomes a finding, check your instrument

Most wrong conclusions here come from a check that could never have said anything else. Prove your check could have produced the other answer — feed it a case you know is positive and watch it fire. Shapes this actually takes:

  • grep -c against a path that does not exist returns 0, identical to a real file with no matches. Confirm the path exists before the count means anything.
  • A process match can match the wrong process: a wrapper, a script that names your target as an argument, or your own shell.
  • A progress check can range over the wrong quantity and read as stalled while the job is healthy.
  • Timestamps are asymmetric. An unchanged modification time is strong evidence nothing touched the file. A changed one is evidence of almost nothing — opening a store, checkpointing, or migrating a schema all touch the file without doing the work you are after.

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 Agentic Walkthrough AI skill do?

Prove that a reported run caused its intended real effect. Walk the executor path from entry to effect, capture one observable per hop, and report a result a no-op could not produce.

Why use Agentic Walkthrough on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sno-ai/sno-station-skills/tree/main/skills/J-peer-review-and-audit/agentic-walkthrough/skill. 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 Agentic Walkthrough?

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 Agentic Walkthrough?

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

Is the Agentic Walkthrough AI skill free?

Yes. It is published on GitHub by sno-ai under the Apache-2.0 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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