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[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 configured frontend HTTP-call patterns with configured backend route patterns via project-config.json configuration.
Workflow:
Detect โ classify request scope and target artifacts.
Execute โ apply required steps with evidence-backed actions.
Verify โ 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:
Exact match โ normalized paths identical
Prefix-augmented โ prepends routePrefix to frontend path
Suffix match โ strips routePrefix from backend, matches remainder
Deep strip โ strips leading {param} segments from backend (handles class-level {companyId} routes)
Controller resolution โ resolves configured route placeholders to actual route owner names
Zero-Config Auto-Detection
No configuration needed. The connector auto-detects frameworks by scanning for marker files:
Frontend
Markers
Configured frontend framework
framework manifests and package metadata from project config
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
Configured backend framework
backend manifests and route metadata from project config
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:
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 โ /api/users
3
Suffix match
0.9
BE /api/users stripped โ /users = FE /users
4
Deep strip
0.85
BE /api/{param}/users โ /users = FE /users
5
Deep strip both
0.8
Both sides have {param} segments stripped
See Also
Implicit Connections โ For event buses, entity events: graphConnectors.implicitConnections[] in project-config.json. CLI: connect-implicit --json
Related Skills
/graph-build โ Build/update the knowledge graph (prerequisite)
/graph-trace โ Trace full system flow (API_ENDPOINT edges enable frontend-to-backend tracing)
/graph-blast-radius โ Analyze structural impact of changes
/graph-query โ 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 โ Failure modes to avoid on every task:
Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting.
Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing.
Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first.
Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done.
Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect.
Assume existing values are intentional โ ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history.
Surface ambiguity before acting โ don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk.
Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.
Critical Thinking Mindset โ Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act.
Anti-hallucination: Never present guess as fact โ cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence โ certainty without evidence root of all hallucination.
MUST ATTENTION apply critical + sequential thinking โ every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.
MUST ATTENTION apply AI mistake prevention โ verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.
Closing Reminders
IMPORTANT MUST ATTENTION Goal: [Code Intelligence] Detect frontend-to-backend API connections via the knowledge graph, matching configured frontend HTTP-call patterns against configured backend route patterns.
Protocols in force (concise digest of the SYNC/shared blocks this skill carries):
AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
