EasyPlatform graph-query

install

source · Clone the upstream repo

git clone https://github.com/duc01226/EasyPlatform

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/duc01226/EasyPlatform "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/graph-query" ~/.claude/skills/duc01226-easyplatform-graph-query && rm -rf "$T"

manifest: .claude/skills/graph-query/SKILL.md

source content

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.

Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.

Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.

Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.

When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.

Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.

Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.

Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.

Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.

Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Graph Query

Query code relationships using the structural knowledge graph. Maps natural language questions to graph CLI queries and formats structured reports.

Prerequisites

Graph must exist -- check
```
.code-graph/graph.db
```
. If missing, tell user to run
```
/graph-build
```
first.
Requires Python 3.10+ with tree-sitter, tree-sitter-language-pack, networkx.

Intent Mapping

Map user's question to the appropriate query pattern(s):

User asks...	Pattern(s) / Command
"who/what calls X", "callers of X"	`callers_of`
"what does X call", "callees of X"	`callees_of`
"what does X import", "X depends on", "deps of X"	`imports_of`
"who/what imports X", "importers of X", "who references X"	`importers_of`
"who uses X", "what uses X", "reverse deps of X"	`importers_of`
"what's inside X", "structure of X", "contents"	`file_summary` (files) / `children_of`
"what tests cover X", "tests for X"	`tests_for`
"who inherits/extends X", "subclasses of X"	`inheritors_of`
"show all connections/related files of X", "graph connections"	`connections` command (see below)

For composite queries ("show all connections", "related files", "full picture"), use the

connections

command instead of running multiple queries manually.

Workflow

Step 1: Check graph exists

ls .code-graph/graph.db 2>/dev/null && echo "OK" || echo "MISSING"

If MISSING: stop and tell user to run

/graph-build

Step 2: Identify target

Extract the target from user's question (file path, function name, or class name).

For files: use relative path (e.g.,
```
src/utils.ts
```
)
For functions/classes: use the name (e.g.,
```
validateInput
```
) or qualified name (e.g.,
```
src/utils.ts::validateInput
```
)

Step 3: Run query

Execute via Bash with

--json

flag:

python .claude/scripts/code_graph query <pattern> <target> --json

For composite "show all connections" queries, use the

connections

command instead:

python .claude/scripts/code_graph connections <target> --json

This returns

file_summary

imports_of

importers_of

callers_of

, and

tests_for

in one call (capped at 20 results per section).

Tip: Add

--node-mode file

query

connections

, or

trace

for a file-level overview with 10-30x less noise. Options:

file

function

class

all

(default).

Step 4: Handle response status

status: "ok"
-- Parse
```
results[]
```
and
```
edges[]
```
, format report (Step 5)
status: "ambiguous"
-- Multiple matches found. Show
```
candidates[]
```
list and ask user to pick one using
```
AskUserQuestion
```
status: "not_found"
-- No match. Suggest: check spelling, use relative file path, try a different name. Optionally run
```
file_summary
```
on the parent file to show available names.
status: "error"
-- Show error message. Common: graph.db missing, Python version too old.

Step 5: Format results

Present results grouped by relationship type. For each result show:

Name and kind (function, class, method)
File path with line numbers (
```
file:line_start-line_end
```
)
Relationship (calls, imports, tests, inherits)

Single query output format:

## {Pattern Description} for `{target}`

Found {N} result(s).

| Name | Kind | File | Lines |
|------|------|------|-------|
| ... | function | src/file.ts | 10-25 |

Composite query output format:

## Connections of `{target}`

### File Summary
{N} nodes: {list functions/classes}

### Imports (outgoing)
{What this file/module imports}

### Importers (incoming)
{Who imports this file/module}

### Callers
{Functions that call functions in this file}

### Test Coverage
{Tests covering functions in this file}

Semantic Query Protocol (When User Query is Not File-Specific)

When the user asks about a FLOW or BEHAVIOR (not a specific file), follow this protocol:

Step 0: Grep/Glob/Search to find entry points

Use Grep/Glob/Search to find key classes/functions related to the user's query. Example: User asks "what happens when X is created" → grep for

CreateX

XCommand

XHandler

Step 1: Use graph to expand

Run

connections

batch-query

on the grep-discovered files to find ALL related files.

Step 2: Trace full system flow

Run the

trace

command to follow the complete chain through all edge types:

python .claude/scripts/code_graph trace <entry-file> --direction both --depth 3 --json

This traces upstream (who calls this?) AND downstream (what does this trigger?) through: CALLS → TRIGGERS_EVENT → PRODUCES_EVENT → MESSAGE_BUS → API_ENDPOINT

Step 3: Verify with grep

For any graph edge that seems surprising, verify with grep that the connection is real.

Available Query Patterns

Pattern	Description	Edge Kind
`callers_of`	Functions that call the target function	CALLS
`callees_of`	Functions called by the target function	CALLS
`imports_of`	What the target file/module imports	IMPORTS_FROM
`importers_of`	Files that import the target file/module	IMPORTS_FROM
`children_of`	Nodes contained in a file or class	CONTAINS
`tests_for`	Tests covering the target function/class	TESTED_BY + naming
`inheritors_of`	Classes inheriting from the target class	INHERITS / IMPLEMENTS
`file_summary`	All nodes (functions, classes) in a file	(direct lookup)
`trace`	Full system flow from a target node	All edge types (BFS)

Aliases (natural language mappings):

Alias	Resolves to
`references_of`	`importers_of`
`uses_of`	`callers_of`
`who_calls`	`callers_of`
`who_imports`	`importers_of`
`depends_on`	`imports_of`
`subclasses_of`	`inheritors_of`
`extends`	`inheritors_of`

Search (Find Nodes by Keyword)

When you don't know the exact name, search first to find candidates:

python .claude/scripts/code_graph search <keyword> --json
python .claude/scripts/code_graph search <keyword> --kind Function --json
python .claude/scripts/code_graph search <keyword> --kind Class --limit 5 --json

Use search to disambiguate when a query returns

status: "ambiguous"

— narrow results by

--kind

(Function, Class, File, Type, Test) then use the full qualified_name.

Find Path (Shortest Path Between Nodes)

Discover how two nodes are connected through the dependency graph:

python .claude/scripts/code_graph find-path <source> <target> --json

Returns the shortest path as a list of nodes. Useful for tracing how a command reaches an event handler, or how a frontend component connects to a backend entity.

Tip: If ambiguous, search for exact qualified names first, then use those in find-path.

Query Filtering and Limiting

Control result size for large codebases:

# Limit results
python .claude/scripts/code_graph query callers_of <target> --limit 5 --json

# Filter by file path regex
python .claude/scripts/code_graph query importers_of <target> --filter "ServiceName" --json

# Limit connections per section
python .claude/scripts/code_graph connections <target> --limit 10 --json

Implicit connection edge types (created by

connect-implicit

Edge Kind	Meaning
`TRIGGERS_EVENT`	Entity CRUD triggers event handler
`PRODUCES_EVENT`	Event handler triggers bus message producer
`MESSAGE_BUS`	Message bus producer to consumer
`TRIGGERS_COMMAND_EVENT`	Command triggers command event handler

Batch Query (Multiple Files)

When reviewing multiple files, use batch mode for deduplicated results:

python .claude/scripts/code_graph batch-query file1 file2 file3 --json

Returns: deduplicated nodes + edges (internal + 1-hop external) across all queried files. Single DB connection, no duplicate data.

Trace (Full System Flow)

Trace all connections from a target node through multiple edge types using BFS:

python .claude/scripts/code_graph trace <target> --json
python .claude/scripts/code_graph trace <target> --direction both --json
python .claude/scripts/code_graph trace <target> --direction upstream --depth 2 --json
python .claude/scripts/code_graph trace <target> --edge-kinds CALLS,MESSAGE_BUS --json
python .claude/scripts/code_graph trace <target> --direction both --node-mode file --json  # file-level overview

Direction options:

```
downstream
```
(default): Follow outgoing edges. "What happens after X?"
```
upstream
```
: Follow incoming edges. "What calls/triggers X?"
```
both
```
: Both directions. "Full flow through X" — use when entry point is a middle file (controller, command handler)

Returns a multi-level tree of connected nodes grouped by BFS depth, with edge types at each level.

Anti-Patterns

Don't rebuild graph -- use
```
/graph-build
```
for that. This skill only queries.
Don't use for change-driven analysis -- use
```
/graph-blast-radius
```
for git-diff-based impact.
Don't use for bulk export -- use
```
/graph-export
```
for full graph dump.
Don't use for diagrams -- use
```
/graph-export-mermaid
```
for Mermaid visualization.
Always use
--json
flag -- ensures structured parseable output.

Related Skills

```
/graph-build
```
-- Build or update the graph (prerequisite)
```
/graph-blast-radius
```
-- Change-driven impact analysis from git diff
```
/graph-export
```
-- Export full graph to JSON
```
/graph-export-mermaid
```
-- Export file graph as Mermaid diagram

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using
```
TaskCreate
```
BEFORE starting
MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
MANDATORY IMPORTANT MUST ATTENTION cite
```
file:line
```
evidence for every claim (confidence >80% to act)
MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.