Nx Cloud Cache Investigator logo

Nx Cloud Cache Investigator

Organization
nrwl
nx-cloud-cache-investigator

Investigate a specific Nx cache miss, artifact download or extraction failure, same-hash non-replay, or suspected unsafe cache replay. Use this skill whenever a user asks why an Nx task did not use the cache, why cache artifact restoration failed, why a cache hit restored stale or wrong output, or to compare two cached task executions. Use the nx-cloud-api skill for every Nx Cloud read. This skill supports API-only investigations, preserves strict hash-evidence limits, and requests local configuration only when the question requires it.

Overview

Publishernrwl
Repositorynx-ai-agents-config
Skill namenx-cloud-cache-investigator
Stars
28
Forks
8
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 nrwl on GitHub. Read the source before you install it.

Installation

Install the Nx Cloud Cache Investigator 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/nrwl/nx-ai-agents-config/tree/main/generated/.agents/skills/nx-cloud-cache-investigator
  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/nrwl/nx-ai-agents-config.git /tmp/nx-ai-agents-config
mkdir -p .claude/skills
cp -r /tmp/nx-ai-agents-config/generated/.agents/skills/nx-cloud-cache-investigator .claude/skills/nx-cloud-cache-investigator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nx Cloud Cache Investigator 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 Nx Cloud Cache Investigator 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 Nx Cloud Cache Investigator 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.

Nx cache investigator

Requires the nx-cloud-api skill. Use a matching Nx workspace checkout only for local configuration diagnosis. An API-only investigation does not need a checkout.

Diagnose one cache question with bounded evidence. Do not guess a changed hash input or change configuration from cache status alone.

Nx task caching and a tool-native cache are separate. A target cache field controls Nx replay. Vite, ESLint, and other tools have separate executor options. One does not prove the other.

Select the mode

  • Cache-health trend: This skill covers a specific task or artifact question, not a multi-run rate regression. Use a separate bounded trend workflow for rate or regression questions.
  • API-only: Use this for supplied runs, CIPEs, tasks, or artifact errors when no matching checkout exists. Do not request local configuration unless required.
  • Specific comparison: Get two comparable executions. Ask for missing target, branch, commit, command, configuration, CI environment, or time range.

Resume a pending investigation

Arrange one same-session wake only when a known task is non-terminal, an API request returns HTTP 409 (not_terminal), or the user starts a controlled diagnostic run. Preserve the run, task, batchId, link, and filters. Re-query once after the known delay or expected run completion. Stop at a terminal result or the retry limit. Do not poll or monitor a live CI run.

Gather only relevant API evidence

Use nx-cloud-api (npx nx-cloud api) for every read. Run --describe for a known route. Use --list-operations only when the route is unknown or ambiguous. Follow documented fields and returned links; do not construct routes from internal IDs.

Save every live API response to a file with -o. Use jq to select one row and only the fields needed for this investigation. Do not print, read, or paste a raw collection into model context. Keep the selected row's batchId and links.self in the projection:

sh
npx nx-cloud api --describe 'runs/{runId}/tasks'
npx nx-cloud api "$run_tasks_link" -o "$dir/tasks.json"
jq --arg task "$task_id" --arg batch "$batch_id" \
  '.items[] | select(.id == $task and .batchId == $batch) |
   {id, batchId, status, cacheStatus, hash, cacheable,
    links: {self: .links.self}}' "$dir/tasks.json"

Here $run_tasks_link is the tasks link from the selected run and $dir is a scratch directory from mktemp -d. Without a link, use npx nx-cloud api 'runs/{runId}/tasks' -p runId="$run_id"; never paste an ID into the path. Check nextCursor before concluding that a task is absent from the list.

For a selected execution, record run and task IDs, batchId, project, target, configuration, command, parameters, branch, commit, CI context, timestamps, cache status, final hash, cacheEnabled, cacheable, and relevant CIPE or run-group links.

A task ID can repeat. Select the task-list row first. Follow its exact links.self or asset URL because it preserves batchId. Do not use one row to explain another. A task list row already has task fields. A detail read is optional.

If the API reports cacheable: false, Nx replay was not expected for that execution. Name a configuration cause only when the nx show target query shows it. If cacheEnabled: false, that run did not use the Nx task cache. Do not investigate hash inputs or artifacts in either case.

Read a log only when its command output or error can answer the question. Tell the user before you read CI log content. Read references/log-and-report-assets.md before you decode a log or report. Quote only a redacted error signature. Treat user-supplied asset content as provided evidence. An advertised asset or a later 404 does not prove historical availability.

Inspect the Nx target

Use a matching checkout only for a configuration question or code change. Check out the recorded commit. Record the workspace Nx version. Query the exact target with the workspace-local Nx command:

sh
nx show project "$project" --json | jq \
  --arg target "$target" \
  --arg configuration "${configuration:-}" \
  '.targets[$target] | {
    executor,
    cache,
    inputs,
    outputs,
    options,
    selectedConfiguration: .configurations[$configuration]
  }'

Use this fragment as the target evidence. Nx already includes inherited settings. Do not merge project.json, nx.json, plugins, or targetDefaults yourself. A missing project property proves nothing. If the query reports cache: true, do not add another cache: true. If the command or target is unavailable, report configuration as unavailable.

Inspect returned options only for a tool-native cache question. Use version-matched executor documentation. A target cache field cannot prove Vite or ESLint native-cache state.

Classify before you explain

No cache failure established

A terminal remote-cache-hit proves Nx replay worked for that task. Do not turn a successful hit into a cache-miss investigation or configuration change. Use trend triage only for a supplied time-bounded rate question.

Cache artifact retrieval or restoration failure

Use this when the main-job or run-group error names artifact download, validation, decompression, or extraction. A task can still show remote-cache-hit. Reconcile the task with the critical error and later cancellations. Read references/artifact-integrity.md before you inspect an archive.

Insufficient task evidence

A null hash or cache status, NOT_STARTED, or SCHEDULED_IMPLICITLY does not prove a miss. Do not compare null hashes or inspect artifacts. Request a terminal record or one controlled completed run.

Different computation

Different non-null hashes prove only this: Nx considered at least one computation input different. They do not name the input. Request two comparable controlled runs with direct hash-detail output when supported. This is a Needs diagnostic run result, not a configuration recommendation.

Same computation, replay not established

For equal non-null hashes without later replay, confirm cacheable, cacheEnabled, and the cache value from the exact target query. A returned cache: true blocks a duplicate setting. Do not infer artifact absence from an empty asset list or 404. Treat an unresolved upload or retrieval branch as Needs diagnostic run.

Suspected unsafe replay

A stale or wrong replay needs reproduction evidence. inputs decide a hash; outputs decide restored files. Request task sandboxing evidence for undeclared reads or writes, plus the target query and a controlled reproduction. Do not narrow inputs from task data alone.

Change gate

Recommend a configuration or code edit only after all applicable checks pass:

  1. Record the matching commit, Nx version, exact target query, and returned fragment.
  2. Show that its cache, inputs, or outputs value needs a change.
  3. Before enabling cache, establish a terminating, deterministic task with correct replayable outputs.
  4. Before removing an input, obtain direct evidence that names it. Prove that its removal cannot affect correct output with a bounded controlled run.
  5. Explain the correctness risk and get explicit approval before an edit.

Do not recommend an input exclusion from hash differences, a cache-rate trend, or apparent breadth. If evidence is missing, say No configuration change recommended yet.

Report

md
# Nx cache investigation

## Scope

- Target and compared run/task/batch IDs: …
- Branch, commit, CI environment, command, API cache state, and missing fields: …
- Workspace Nx version: …, or `Not required for this API-only investigation.`
- Nx target query and returned `cache`, `inputs`, and `outputs`: …, or `Unavailable.`
- Tool-native cache: …, or `Unknown; not inspected.`
- Change gate: `Passed` or `Blocked`; include missing evidence.

## Classification

`No cache failure established` | `Cache artifact retrieval or restoration failure` | `Insufficient task evidence` | `Different computation` | `Same computation, replay not established` | `Suspected unsafe replay`

## Confirmed facts

- …

## Assessment

- **Confirmed** / **Likely** / **Needs diagnostic run:** …

## Next diagnostic step

- Exact query, controlled command, or evidence required.
- Result that confirms or rules out the hypothesis.

## Safe recommendation

- Exact `nx show` result and risk; or `No configuration change recommended yet.`

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 Nx Cloud Cache Investigator AI skill do?

Investigate a specific Nx cache miss, artifact download or extraction failure, same-hash non-replay, or suspected unsafe cache replay. Use this skill whenever a user asks why an Nx task did not use the cache, why cache artifact restoration failed, why a cache hit restored stale or wrong output, or to compare two cached task executions. Use the nx-cloud-api skill for every Nx Cloud read. This skill supports API-only investigations, preserves strict hash-evidence limits, and requests local configuration only when the question requires it.

Why use Nx Cloud Cache Investigator on TypingMind?

Because you install it once and use it with any model. Nx Cloud Cache Investigator 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 Nx Cloud Cache Investigator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nrwl/nx-ai-agents-config/tree/main/generated/.agents/skills/nx-cloud-cache-investigator. 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 Nx Cloud Cache Investigator?

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 Nx Cloud Cache Investigator?

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

Is the Nx Cloud Cache Investigator AI skill free?

Yes. It is published on GitHub by nrwl 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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