Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Authors
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

ProTermsPrivacyRefunds
Back to skills

Graphify Usage

ASecurity

This skill should be used when querying the graphify knowledge graph for structural codebase information, choosing between graph tools and grep, or interpreting graph query results.

7 stars
0 votes
0 copies
0 views
Added 9/28/2026
ai-agentsrustgonode

Works with

mcp

Security Analysis

A100/100

Scanned 9/28/2026

Install to Claude Code

$npx -y skills add josix/agent-flow --skill graphify-usage --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Graphify Usage?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for Graphify Usage
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/josix-graphify-usage/badge)](https://www.skillsdirectory.com/skills/josix-graphify-usage)

More formats (shields.io, HTML) on the badges page.

Files
SKILL.md
---
name: graphify-usage
user-invocable: false
description: This skill should be used when querying the graphify knowledge graph for structural codebase information, choosing between graph tools and grep, or interpreting graph query results.
---

# Graphify Usage

Query the knowledge graph effectively, interpret results accurately, and stay within token budgets.

## Overview

The graphify MCP server exposes the codebase as a pre-built knowledge graph stored in `graphify-out/graph.json`. The graph encodes structural relationships between concepts, modules, and files extracted by the graphify pipeline. This skill governs when, how, and with what discipline agents should query it.

**Owner**: Riko (Explorer Agent) — Riko is the primary graph query agent and owns interpretation of results.
**Consumers**: Senku (Planner Agent), Lawliet (Reviewer Agent) — both may consult the graph during planning and review, but Riko is the preferred query agent for deep exploration.
**Out of scope**: Loid (Executor) and Alphonse (Verifier) do NOT have graph access by design. Loid performs file writes and needs test output, not structural queries; Alphonse runs verification commands that cannot rely on graph freshness. This enforces the one-writer invariant: only agents that need structural context hold graph tool permissions.

All 7 tools are accessed via the MCP prefix `mcp__plugin_agent-flow_graphify__*`. See [references/tool-reference.md](references/tool-reference.md) for full signatures.

---

## When to Query the Graph vs. Grep

Use the graph when you need structural relationships. Use grep when you need literal text matches.

| Trigger condition | Preferred approach |
|---|---|
| "What modules import X?" / dependency mapping | Graph: `get_neighbors` with `relation_filter` |
| "What is the main entry point?" / orientation | Graph: `graph_stats` then `god_nodes` |
| "How does component A connect to component B?" | Graph: `shortest_path` |
| "Which community does file F belong to?" | Graph: `get_community` on node label |
| "Find the string literal `TODO: fix`" | Grep |
| "Find all files that define function `parse_args`" | Grep (pattern match) |
| "What does a specific config key say?" | Read the file directly |
| File is freshly edited (within this session) | Grep/Read — graph may be stale |
| Graph does not exist at `graphify-out/graph.json` | Grep/Read only |

**Rule of thumb**: If the answer requires traversal (who calls what, what clusters together, how far apart are two concepts), use the graph. If the answer is a literal substring or you need up-to-the-edit accuracy, use Grep or Read.

---

## Tool Decision Table

Choose the right tool for the question type. See [references/query-patterns.md](references/query-patterns.md) for detailed decision sequences.

| Question type | Primary tool | Follow-up |
|---|---|---|
| How large is this codebase? | `graph_stats` | `god_nodes` for core abstractions |
| What are the central concepts? | `god_nodes` | `get_community` to explore clusters |
| Which modules are in the same cluster? | `get_community` | `get_node` on members |
| What does this node connect to? | `get_neighbors` | `get_node` on callers/callees |
| Blast radius of changing X | `get_neighbors` then `shortest_path` | Manual review of connected files |
| Full structural/semantic question | `query_graph` (BFS for broad, DFS for path) | `get_node` on returned labels |
| Specific node details | `get_node` | — |
| Path between two concepts | `shortest_path` | `get_node` on intermediate nodes |

---

## Token Hygiene

Hard rules for staying within context budget:

1. **Always set `top_k` / `top_n`** when available. Default `god_nodes` returns 10 nodes — only request more if explicitly needed.
2. **Set `token_budget`** on `query_graph` calls. The default is 2000 tokens; lower it (e.g., 500) for quick orientation queries.
3. **Set `depth` conservatively**. Default depth is 3; start at 1-2 for narrow questions, increase only if the result is insufficient.
4. **Do NOT paste raw subgraph JSON downstream** into task prompts or summaries. Extract only node IDs, labels, and `source_location` fields.
5. **Summarize before handing off**. Convert graph results to bullet lists of `label → source_location` pairs. Downstream agents (Senku, Lawliet) need names and file paths, not raw graph output.
6. **Chain calls, don't parallelize blindly**. Run `graph_stats` first, then decide if `god_nodes` is needed. Avoid firing all 7 tools simultaneously.

---

## Result Interpretation

How to read and trust what the graph returns:

### Cite `source_location` fields

Every node in the graph carries a `source_location` field (file path, sometimes line range). When reporting a finding, always cite this field:

```
Node: AgentOrchestrator
source_location: commands/orchestrate.md:1
```

Never report a concept without its `source_location` — downstream agents cannot verify unsourced claims.

### Surface confidence tags

The graph annotates edges with confidence labels:

| Tag | Meaning | Trust level |
|---|---|---|
| EXTRACTED | Directly observed in source text | High — treat as fact |
| INFERRED | Model-reasoned relationship | Medium — verify if load-bearing |
| AMBIGUOUS | Multiple interpretations possible | Low — always verify with Read/Grep |

When an edge is INFERRED or AMBIGUOUS, note this explicitly in your summary. Do not present inferred relationships as certainties.

### Trust-but-verify for freshly edited files

The graph is built once (at `/graphify` time) and is not updated during a session. If Loid has modified a file in the current session, graph data for that node is stale. For any file touched in this session:
- Use the graph for structural orientation only
- Use Read or Grep for current content

---

## What NOT to Do

- **Do not query the graph on freshly edited files** expecting current content — it will be stale.
- **Do not set `depth` > 4** without a specific reason — the output will overflow context.
- **Do not pass raw graph JSON blobs** in task descriptions or summaries to other agents.
- **Do not fire `query_graph` for questions answerable by `get_node` or `get_neighbors`** — cheaper specific tools first.
- **Do not report INFERRED edges as facts** — always surface the confidence tag.
- **Do not skip `source_location` citations** — every claimed relationship needs a file anchor.
- **Do not use graph results as a substitute for reading changed files** — the graph captures structure, not current content.

---

## Cross-References

- [docs/guides/using-graphify.md](../../docs/guides/using-graphify.md) — installation, build steps, and how to run the graphify pipeline
- [references/tool-reference.md](references/tool-reference.md) — full MCP tool signatures, parameters, cost profiles
- [references/query-patterns.md](references/query-patterns.md) — decision table mapping question types to tool sequences
- [examples/worked-queries.md](examples/worked-queries.md) — end-to-end query scenarios with expected result shapes

Attribution

josixjosix
View sourceMore from josix →
SSkills DirectorySkills Directory

Ship a skill? Prove it's safe.

Free 120-pattern security scan, letter grade, and an embeddable README badge.

Submit a skill

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Ship a skill? Prove it's safe.

Free 120-pattern security scan, letter grade, and an embeddable README badge.

Submit a skill

Related Skills

Caveman

Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".

1074701 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

695601 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3351 votes

catchup

Recovers the conversation and failed tool calls of a previous Codex, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or report a catchup failure. Do NOT use for the current conversation, git history, or any non-agent log.

691 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →