Project Context Ingestion logo

Project Context Ingestion

Organization
JetBrains
project-context-ingestion

Inspect a Kotlin + Spring repository before making changes or answering implementation, debugging, build, architecture, or review questions. Use when the agent must map modules, Spring Boot/Kotlin/Gradle/JDK versions, compiler plugins, profiles, dependencies, runtime stack, and architectural boundaries so later advice stays compatible with the real project.

Overview

PublisherJetBrains
Repositoryskills
Skill nameproject-context-ingestion
Stars
377
Forks
32
Bundled files
1
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 JetBrains on GitHub. Read the source before you install it.

Installation

Install the Project Context Ingestion 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/JetBrains/skills/tree/main/project-context-ingestion
  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/JetBrains/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/project-context-ingestion .claude/skills/project-context-ingestion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Context Ingestion 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 Project Context Ingestion 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 Project Context Ingestion 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.

Project Context Ingestion

Source mapping: Tier 1 critical skill derived from Kotlin_Spring_Developer_Pipeline.md (SK-01).

Mission

Build an accurate mental model of the repository before proposing code, fixes, tests, or architecture advice. Treat this skill as the default first step for all other Kotlin + Spring skills.

Inspect First

  • Read settings.gradle.kts, root build.gradle.kts, module build files, gradle.properties, gradle/libs.versions.toml, and Gradle wrapper properties if present.
  • Read application.yml, application.yaml, application.properties, and profile-specific variants.
  • Read the top-level package or module layout, plus representative controller, service, repository, configuration, and test classes.
  • Read runtime and delivery files when present: Dockerfile, docker-compose*.yml, Kubernetes or Helm manifests, CI workflows, .env.example, and deployment scripts.
  • Inspect dependency and plugin declarations before recommending any new annotation, library, or Spring API.

Extract The Project Map

  • Record the Kotlin version, Spring Boot version, Spring Framework generation, Gradle version, JDK or toolchain version, and major libraries that constrain advice.
  • Record whether the project uses MVC or WebFlux, JPA or JDBC or jOOQ or MyBatis or R2DBC, synchronous or coroutine-based services, and which test stack is present.
  • Record whether compiler plugins such as kotlin("plugin.spring"), kotlin("plugin.jpa"), kotlin("plugin.serialization"), KAPT, or KSP are enabled.
  • Record whether the repository is single-module, multi-module, modular monolith, or service-per-repo.
  • Record package boundaries and obvious architecture rules such as web -> service -> persistence or hexagonal adapters.
  • Record active configuration prefixes, profiles, secret-loading patterns, and conditional bean usage.

Work Sequence

  1. Start from build and config files. Do not begin with business code.
  2. Infer compatibility constraints. Treat these as hard boundaries for every later suggestion.
  3. Map modules and their responsibilities. Note where HTTP, persistence, messaging, security, and tests live.
  4. Identify framework mode: MVC vs WebFlux, blocking vs reactive persistence, servlet vs netty, synchronous vs coroutine style.
  5. Identify Kotlin and Spring trap markers:
    • missing plugin.spring while AOP annotations exist
    • missing plugin.jpa while JPA entities exist
    • manual dependency versions that may fight the Spring Boot BOM
    • mixed blocking and reactive stacks in the same request flow
    • duplicate or profile-fragmented configuration
  6. Summarize only what matters for the task at hand. Do not dump a full file inventory.
  7. If the repository is incomplete, state the missing evidence and continue with bounded assumptions.

What To Produce

Return a compact project brief with these sections:

  • Stack: framework mode, persistence mode, test stack, build stack.
  • Compatibility constraints: exact versions or explicit unknowns.
  • Architecture map: modules or packages and responsibility boundaries.
  • Operational config: profiles, property binding style, runtime and secret conventions.
  • Risk markers: project-specific traps that may invalidate generic advice.
  • Next commands or files: the highest-value checks to run next.

Decision Rules

  • Prefer facts from the repository over conventions from memory.
  • If multiple modules disagree on versions or stack style, call that out explicitly instead of normalizing it away.
  • If build files and runtime code imply different approaches, treat that as a risk, not a detail.
  • If the task is narrow, still inspect the minimum build and config context before editing code.
  • If another skill is invoked later, feed it the extracted constraints instead of rereading everything from scratch.

Advanced Context Signals

  • Distinguish application modules from library modules. A shared library inside the repo may intentionally avoid the Spring Boot plugin, bootJar, or runtime-only dependencies.
  • Check whether build logic lives in convention plugins, buildSrc, or an included build-logic build. Those places often override what module build files appear to say.
  • Detect javax.* versus jakarta.* imports to place the codebase on the correct migration timeline. This changes which Spring, Hibernate, and validation advice is valid.
  • Detect whether the project uses generated sources such as OpenAPI codegen, QueryDSL, jOOQ, protobuf, or MapStruct. Generated code changes where safe edits belong.
  • Detect whether spring.jpa.open-in-view, spring.main.lazy-initialization, spring.aot.enabled, or native-image related settings are active. These settings change what is safe to suggest.
  • Detect whether the system relies on platform features such as Config Server, Vault, Helm-templated config, or environment-variable substitution. Local files may not show the full runtime truth.
  • Detect whether observability, tracing, and correlation are first-class conventions. This affects controller, client, and incident advice.
  • Detect whether the service owns its schema, shares a database, or integrates through outbox or CDC. That changes migration and transaction guidance.

Expert Heuristics

  • If the repo mixes JPA and R2DBC or MVC and WebFlux, determine whether that is an intentional boundary by module or an accidental hybrid. Advice differs sharply between the two cases.
  • If the repo uses Kotlin coroutines on top of blocking persistence, call out that this is still a blocking architecture unless the persistence layer is reactive.
  • If multiple dependency authorities exist, rank them. The real source of truth may be the Spring Boot plugin, a version catalog, a company convention plugin, or a platform BOM.
  • If tests use different stacks than production, note that explicitly. For example, H2 in tests plus Postgres in production invalidates many persistence assumptions.
  • If the repo has no obvious architecture rules, report that as a constraint instead of inventing a clean architecture that the codebase does not follow.

Guardrails

  • Do not recommend APIs or annotations from a different Spring Boot major version than the project actually uses.
  • Do not add dependencies or plugin blocks without checking whether the project already gets them from a BOM, version catalog, or convention plugin.
  • Do not assume MVC when spring-boot-starter-webflux or coroutine-web stack is present.
  • Do not assume JPA just because a database exists. Verify the actual persistence technology.
  • Do not propose architecture refactors before identifying the current structure and team conventions.

Escalate Suspicion When You See

  • Both servlet and reactive starters without a clear reason.
  • data class entities together with JPA or Hibernate.
  • @Transactional, @Cacheable, or @Async with no Kotlin Spring plugin.
  • Explicit versions for Jackson, Hibernate, Netty, Logback, or SLF4J in a Boot-managed build.
  • Profile-specific config files that redefine the same properties with inconsistent shapes.

Quality Bar

A good run of this skill makes downstream advice more precise, narrower, and easier to verify. A bad run starts suggesting code before proving which stack, versions, and conventions the repository actually uses.

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 Project Context Ingestion AI skill do?

Inspect a Kotlin + Spring repository before making changes or answering implementation, debugging, build, architecture, or review questions. Use when the agent must map modules, Spring Boot/Kotlin/Gradle/JDK versions, compiler plugins, profiles, dependencies, runtime stack, and architectural boundaries so later advice stays compatible with the real project.

Why use Project Context Ingestion on TypingMind?

Because you install it once and use it with any model. Project Context Ingestion 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 Project Context Ingestion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JetBrains/skills/tree/main/project-context-ingestion. 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 Project Context Ingestion?

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 Project Context Ingestion?

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

Is the Project Context Ingestion AI skill free?

It is published on GitHub by JetBrains. Check the repository for licensing terms. 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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