E2e Red Triage logo

E2e Red Triage

Organization
sno-ai
e2e-red-triage

Classify one unexpected end-to-end test failure as a product defect or an environment fault needing a new preflight check. MANUAL-ONLY: load when the suite runner names a specific failure. NEVER auto-load from context.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill namee2e-red-triage
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 E2e Red Triage 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/e2e/skills/e2e-red-triage
  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/e2e/skills/e2e-red-triage .claude/skills/e2e-red-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable E2e Red Triage 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 E2e Red Triage 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 E2e Red Triage 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.

e2e-red-triage — for the reds the preflight could not have predicted

Terms are as in e2e-environment-preflight: a row is one acceptance check in the test plan, a red is a row that failed or gave no answer, a journey is one end-to-end scenario made of rows, the suite is all the rows of a journey, and preflight is the set of environment checks that must be green before the first row runs.

Never auto-triggered, and normally never needed. One caller — the agent running the suite — and one moment:

The e2e-environment-preflight checks were all green, and a row went red anyway — or produced a null, an empty, a zero, or a silence nobody can yet explain.

If preflight was not green, the suite should not have started. If a red is an ordinary product failure with a clear cause, fix it and move on; this is not the ceremony for an ordinary bug. This is the residual after the gate has done its work — a small fraction, and one that shrinks every time move 3 is honoured.

Why this needs its own protocol

The preflight handles the failures somebody could enumerate in advance. What is left is the class where the failure produced nothing rather than an error, and the nothing appeared far from its cause. One null can mean five different things across five runs of the same suite; "no results" can mean the query matched nothing or the search never ran.

The hours such a red costs are spent attributing an absence — arguing about what a missing thing meant, from evidence that never contained the answer. Guessing feels faster than instrumenting and is not: a wrong guess costs a full suite run to disprove, and a plausible wrong guess is worse than an implausible one because it stops the search.

The deeper version, and the one worth carrying: the failure is usually a two-part outcome collapsed into one verdict. An HTTP 202 "accepted" means the request was queued, not that the work was done, and both caller and monitor read it as success until someone looks. A cleanly stopped service records an exit code that looks like a crash. In each case the evidence was there, in plain words, and nobody split it.

So the first question is never "why did it fail" — it is "what are the two halves of this result, and which half am I looking at?"

The three moves, in order

These are defaults; a project may choose otherwise and record it in its local binding.

1. Do not guess. Enter diagnosis.

Stop proposing causes. Write down first the two things this result could be a collapse of: succeeded versus reached, stopped versus crashed, empty versus unreadable, absent versus never-asked, zero-matches versus source-missing.

2. The first change makes the absence name itself. Not the product.

The first commit after this kind of red does not touch product behaviour. It changes whatever produced the empty so that it says why it is empty — in the artifact, as a field, not in a log line, which is read only by someone who already suspects the answer.

result: null
→  result: {value: null, because: "<the reason, as a code>"}

Then re-run. Only now diagnose. This inverts the usual instinct and that is the point: instrumenting first looks slower for one iteration and is faster from the second onward, because every future occurrence of this absence becomes a single read instead of an investigation.

Two shapes to watch for here, because both report health while broken:

  • A check that passes when its input source is missing. A count over a path that does not exist returns zero — the same zero a real path with no matches returns. Before any "it is absent" claim, prove the place you looked at is real, then look.
  • A condition that can no longer fire. An alarm testing an absolute count rather than a delta switches itself off permanently the first time the count moves.

Feed every new check a known-wrong input and watch it go red. Instruments fail toward "healthy".

3. The exit condition: come back with a durable check.

A diagnosis is not the end. The finding becomes a new preflight check for that system and a line in that system's primer (see e2e-environment-preflight), or this red is not closed.

Invoke e2e-environment-preflight to write it, and record the red in the red scoreboard described in e2e-environment-preflight, with its bucket and the new check's id:

{"row":"<row-id>","bucket":"environment","note":"…","owed_check":"<check-id>"}

This is the only mechanism that makes the residual shrink. Without it the same class of absence is rediscovered every campaign and the gate never gets stronger — the process runs, everyone is diligent, and the hours are spent again. Hitting the wall is allowed; coming back with nothing durable is not.

Classify before you close

Every red gets exactly one bucket — source, environment, stuck, harness. Be honest about stuck: a run that produced no verdict at all is not a pass and not a skip, and burying it is how a campaign reports coverage it never had.

The counts are the point. A journey whose reds are mostly source found bugs. One whose reds are mostly environment or stuck measured its own setup, and the fix for that is upstream in the preflight — never another round here.

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 E2e Red Triage AI skill do?

Classify one unexpected end-to-end test failure as a product defect or an environment fault needing a new preflight check. MANUAL-ONLY: load when the suite runner names a specific failure. NEVER auto-load from context.

Why use E2e Red Triage on TypingMind?

Because you install it once and use it with any model. E2e Red Triage 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 E2e Red Triage 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/e2e/skills/e2e-red-triage. 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 E2e Red Triage?

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 E2e Red Triage?

As many as you like. As long as a model supports skills, you can use E2e Red Triage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the E2e Red Triage 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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