Query

OrganizationPopular

NeoLabHQ

query

Search the FPF knowledge base and display hypothesis details with assurance information

Overview

Publisher	NeoLabHQ
Repository	`context-engineering-kit`
Skill name	`query`
Stars	1.7K
Forks	159
Bundled files	Instructions only
License	GPL-3.0
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Query AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/query

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/query .claude/skills/query

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Query 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 Query 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 Query 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.

Query Knowledge

Search the FPF knowledge base and display hypothesis details with assurance information.

Action (Run-Time)

Search .fpf/knowledge/ and .fpf/decisions/ by user query.
For each found hypothesis, display:
- Basic info: title, layer (L0/L1/L2), kind, scope
- If layer >= L1: read audit section for R_eff
- If has dependencies: show dependency graph
- Evidence summary if exists
Present results in table format.

Search Locations

Location	Contents
`.fpf/knowledge/L0/`	Proposed hypotheses
`.fpf/knowledge/L1/`	Verified hypotheses
`.fpf/knowledge/L2/`	Validated hypotheses
`.fpf/knowledge/invalid/`	Rejected hypotheses
`.fpf/decisions/`	Design Rationale Records
`.fpf/evidence/`	Evidence and audit files

Output Format

markdown
## Search Results for "<query>"

### Hypotheses Found

| Hypothesis | Layer | Kind | R_eff |
|------------|-------|------|-------|
| redis-caching | L2 | system | 0.85 |
| cdn-edge | L2 | system | 0.72 |

### redis-caching (L2)

**Title**: Use Redis for Caching
**Kind**: system
**Scope**: High-load systems, Linux only

**R_eff**: 0.85
**Weakest Link**: internal test (0.85)

**Dependencies**:

[redis-caching R:0.85] └── (no dependencies)



**Evidence**:
- ev-benchmark-redis-caching-2025-01-15 (internal, PASS)

### cdn-edge (L2)

**Title**: Use CDN Edge Cache
**Kind**: system
**Scope**: Static content delivery

**R_eff**: 0.72
**Weakest Link**: external docs (CL1 penalty)

**Evidence**:
- ev-research-cdn-2025-01-10 (external, PASS)

Search Methods

By Keyword

Search file contents for matching text:


/fpf:query caching
-> Finds all hypotheses with "caching" in title or content

By Specific ID

Look up a specific hypothesis:


/fpf:query redis-caching
-> Shows full details for redis-caching
-> Displays dependency tree
-> Shows R_eff breakdown

By Layer

Filter by knowledge layer:


/fpf:query L2
-> Lists all L2 hypotheses with R_eff scores

By Decision

Search decision records:


/fpf:query DRR
-> Lists all Design Rationale Records
-> Shows what each DRR selected/rejected

R_eff Display

For L1+ hypotheses, read the audit section and display:

markdown
**R_eff Breakdown**:
- Self Score: 1.00
- Weakest Link: ev-research-redis (0.90)
- Dependency Penalty: none
- **Final R_eff**: 0.85

Dependency Tree Display

If hypothesis has depends_on, show the tree:


[api-gateway R:0.80]
  └──(CL:3)── [auth-module R:0.85]
  └──(CL:2)── [rate-limiter R:0.90]

Legend:

R:X.XX = R_eff score
CL:N = Congruence Level (1-3)

Examples

Search by keyword:


User: /fpf:query caching

Results:
| Hypothesis | Layer | R_eff |
|------------|-------|-------|
| redis-caching | L2 | 0.85 |
| cdn-edge-cache | L2 | 0.72 |
| lru-cache | invalid | N/A |

Query specific hypothesis:


User: /fpf:query redis-caching

# redis-caching (L2)

Title: Use Redis for Caching
Kind: system
Scope: High-load systems
R_eff: 0.85
Evidence: 2 files

Query decisions:


User: /fpf:query DRR

# Design Rationale Records

| DRR | Date | Winner | Rejected |
|-----|------|--------|----------|
| DRR-2025-01-15-caching | 2025-01-15 | redis-caching | cdn-edge |

Frequently asked questions

What does the Query AI skill do?

Search the FPF knowledge base and display hypothesis details with assurance information

Why use Query on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/query. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Query?

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 Query?

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

Is the Query AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 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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Investigate First

Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.

Surgical Patch

Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Query Knowledge

Action (Run-Time)

Search Locations

Output Format

Search Methods

By Keyword

By Specific ID

By Layer

By Decision

R_eff Display

Dependency Tree Display

Examples

Frequently asked questions

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