graph-connect-api

name	graph-connect-api
description	[Code Intelligence] Use when you need to detect frontend-to-backend API connections using the knowledge graph.
version	2.0.0

Quick Summary

Goal: [Code Intelligence] Detect frontend-to-backend API connections using the knowledge graph. Matches HTTP calls (Angular, React, Vue, fetch, axios) with backend routes (.NET, Spring, Express, FastAPI) via project-config.json configuration.

Workflow:

1. Detect — classify request scope and target artifacts.
2. Execute — apply required steps with evidence-backed actions.
3. Verify — confirm constraints, output quality, and completion evidence.

Key Rules:

• MUST ATTENTION keep claims evidence-based (file:line) with confidence >80% to act.
• MUST ATTENTION keep task tracking updated as each step starts/completes.
• NEVER skip mandatory workflow or skill gates.

How It Works

The connector scans frontend files for HTTP calls and backend files for route definitions, normalizes URL paths, and matches them using a multi-strategy algorithm:

1. Exact match — normalized paths identical
2. Prefix-augmented — prepends routePrefix to frontend path
3. Suffix match — strips routePrefix from backend, matches remainder
4. Deep strip — strips leading {param} segments from backend (handles class-level {companyId} routes)
5. Controller resolution — resolves .NET [controller] placeholder to actual class name

Zero-Config Auto-Detection

No configuration needed. The connector auto-detects frameworks by scanning for marker files:

Frontend	Markers
Angular	angular.json, nx.json, @angular/core in package.json
React	react in package.json
Vue	vue.config.js, vue in package.json
Next.js	next.config.js, next in package.json
Svelte	svelte.config.js, svelte in package.json

Backend	Markers
.NET	*.csproj with Microsoft.AspNetCore
Spring	pom.xml/build.gradle with spring-boot
Express	express in package.json
NestJS	@nestjs/core in package.json
FastAPI	fastapi in requirements.txt
Django	manage.py with django
Rails	Gemfile with rails
Go	go.mod (Gin/Echo patterns)

Auto-Run Behavior

The connector runs automatically in these situations:

When	Behavior
After build / update / sync	Always runs via _auto_connect()
First trace / query / connections	Runs once via _ensure_connectors_ran() if never run before

You almost never need to run this manually. The graph CLI handles it automatically.

Custom Config (Optional)

For projects with custom HTTP patterns (e.g., base class API service), add to docs/project-config.json:

{
    "graphConnectors": {
        "apiEndpoints": {
            "enabled": true,
            "frontend": {
                "framework": "angular",
                "paths": ["src/app/"],
                "customPatterns": ["this\\.\\s*(get|post|put|delete|patch)\\s*[<(]\\s*['\"]([^\"']+)"]
            },
            "backend": {
                "framework": "dotnet",
                "paths": ["src/Api/Controllers/"],
                "routePrefix": "api",
                "customPatterns": []
            }
        }
    }
}

Custom patterns extend (not replace) built-in framework patterns. Explicit config overrides auto-detected config for paths.

Manual Run

python .claude/scripts/code_graph connect-api --json

Steps (when manually invoked)

1. Run connector via Bash:

   python .claude/scripts/code_graph connect-api --json
   

2. Report results: Frontend calls found, backend routes found, matches created, edge count

Matching Strategies

The connector tries 5 strategies in order (highest confidence first):

#	Strategy	Confidence	Example
1	Exact match	1.0	FE /api/users = BE /api/users
2	Prefix-augmented	0.95	FE /users + prefix api → /api/users
3	Suffix match	0.9	BE /api/users stripped → /users = FE /users
4	Deep strip	0.85	BE /api/{param}/users → /users = FE /users
5	Deep strip both	0.8	Both sides have {param} segments stripped

See Also

• Implicit Connections — For event buses, entity events: graphConnectors.implicitConnections[] in project-config.json. CLI: connect-implicit --json

Related Skills

• /graph-build — Build/update the knowledge graph (prerequisite)
• /graph-trace — Trace full system flow (API_ENDPOINT edges enable frontend-to-backend tracing)
• /graph-blast-radius — Analyze structural impact of changes
• /graph-query — Query code relationships in the graph

---

Connect API

Detect frontend HTTP calls and match them to backend route definitions, creating API_ENDPOINT edges in the knowledge graph.

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.

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.

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.

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

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.