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Run npx skillmds@latest add spillwavesolutions/okf-query in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Okf Query free to use?

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Who published Okf Query?

spillwavesolutions (@spillwavesolutions) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Okf Query by spillwavesolutions — Agent Skill

Name: Okf Query
Author: spillwavesolutions

Okf Query

Multi-hop query and subgraph extraction over OKF bundles for progressive disclosure. Use when packing minimal context for long-running agents, exploring the neighborhood of a concept, or answering what relates to X within N hops.

spillwavesolutions 8a12da4 2 files · 4.5 KB Updated Sep 17, 2026

File contents

OKF Query & Progressive Disclosure

Goal

Answer graph questions and emit minimal context packs (subgraphs) instead of dumping entire directories.

This is the read-path optimizer. Defaults exist so a long-running agent does not eat the whole second brain.

Query types

Intent	Approach
Neighbors of X	1-hop inbound + outbound
Context pack for agent run	2-hop outbound subgraph around the entry concept
Path A → B	BFS / path search on typed edges
By type	Filter concepts where frontmatter `type` matches
By tag	Filter on `tags`
Trust slice	Prefer `verified: true` and non-stale nodes

Process

Resolve bundle root and query target(s).

Prefer deterministic tools:

okf graph, search, or list commands if available

Fallback (defaults: 2 hops, 20 nodes, outbound-only):

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" pack <bundle> <concept> --hops 2 --max-nodes 20
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" subgraph <bundle> <concept> --hops 2
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" backlinks <bundle> <concept>
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" edges <bundle> --rel depends_on

Shape results for the consumer:
- Human / agent pack: use pack → markdown field (preferred)
- JSON: nodes + typed edges for downstream steps
Cap pack size. Default hops = 2, max-nodes = 20. Increase only if the user needs deeper context.
Annotate trust: mark unverified or draft nodes so consumers can deprioritize them. Do not silently omit schema-declared high-impact unverified nodes — flag them.

Deep reference: references/progressive-disclosure.md.

Progressive disclosure pack format

# Context pack: <root concept>
Hops: 2 | Nodes: N | Generated: <timestamp>

## Entry
- path, type, one-line description

## Included concepts (read order)
1. ...
2. ...

## Graph (Mermaid)
```mermaid
graph LR
  A --> B

Excluded (available on request)

siblings / deeper hops summary


## Rules

- Outbound-only by default. `--undirected` floods through Catalog hubs; prefer `impact` for “what would break?”.
- Prefer verified, active nodes when trimming packs under a size budget.
- Do not silently omit high-impact unverified nodes — flag them (`x-impact` from the owning plugin).
- Absolute paths in links inside packs so agents can open files reliably.
- Never fabricate nodes missing from the bundle.

## Done when

- Query answered with explicit hop depth and node count
- Pack is small enough for agent context (or user approved a larger extract)

---name: okf-querydescription: Multi-hop query and subgraph extraction over OKF bundles for progressive disclosure. Use when packing minimal context for long-running agents, exploring the neighborhood of a concept, or answering what relates to X within N hops.---# OKF Query & Progressive Disclosure## GoalAnswer graph questions and emit **minimal context packs** (subgraphs) instead of dumping entire directories.This is the **read-path optimizer**. Defaults exist so a long-running agent does not eat the whole second brain.## Query types| Intent | Approach ||--------|----------|| Neighbors of X | 1-hop inbound + outbound || Context pack for agent run | 2-hop **outbound** subgraph around the entry concept || Path A → B | BFS / path search on typed edges || By type | Filter concepts where frontmatter `type` matches || By tag | Filter on `tags` || Trust slice | Prefer `verified: true` and non-stale nodes |## Process1. Resolve bundle root and query target(s).2. Prefer deterministic tools: - `okf graph`, search, or list commands if available - Fallback (defaults: **2 hops**, **20 nodes**, **outbound-only**): ```bash python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" pack <bundle> <concept> --hops 2 --max-nodes 20 python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" subgraph <bundle> <concept> --hops 2 python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" backlinks <bundle> <concept> python3 "${CLAUDE_PLUGIN_ROOT}/scripts/okf-graph.py" edges <bundle> --rel depends_on ```3. Shape results for the consumer: - **Human / agent pack**: use `pack` → `markdown` field (preferred) - **JSON**: nodes + typed edges for downstream steps4. Cap pack size. Default hops = 2, max-nodes = 20. Increase only if the user needs deeper context.5. Annotate trust: mark unverified or draft nodes so consumers can deprioritize them. Do not silently omit schema-declared high-impact unverified nodes — flag them.Deep reference: `references/progressive-disclosure.md`.## Progressive disclosure pack format```markdown# Context pack: <root concept>Hops: 2 | Nodes: N | Generated: <timestamp>## Entry- path, type, one-line description## Included concepts (read order)1. ...2. ...## Graph (Mermaid)```mermaidgraph LR A --> B```## Excluded (available on request)- siblings / deeper hops summary```## Rules- Outbound-only by default. `--undirected` floods through Catalog hubs; prefer `impact` for “what would break?”.- Prefer verified, active nodes when trimming packs under a size budget.- Do not silently omit high-impact unverified nodes — flag them (`x-impact` from the owning plugin).- Absolute paths in links inside packs so agents can open files reliably.- Never fabricate nodes missing from the bundle.## Done when- Query answered with explicit hop depth and node count- Pack is small enough for agent context (or user approved a larger extract)

Frequently asked questions