graph-update

name	graph-update
description	[Code Intelligence] Use when you need to update the knowledge graph with uncommitted working tree changes.
version	1.0.0

Quick Summary

Goal: [Code Intelligence] Update the knowledge graph with uncommitted working tree changes. Detects staged/unstaged file changes and re-parses them into the graph. Use mid-session after editing files, or when graph-sync reports no committed changes but working tree has modifications.

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.

When to Use

• After editing files mid-session (working tree dirty, nothing committed yet)
• When /graph-sync reports "up to date" but you've made local changes
• Before running /graph-blast-radius on uncommitted work
• At the end of /graph-sync (explicitly invoked as step 4)

How It Works

1. Diffs working tree against a base commit (default: HEAD~1)
2. Finds changed/added/deleted files in the working tree
3. Re-parses changed/added files, removes deleted files from graph
4. Re-runs API connector and implicit connector (same as sync)

Steps

1. Run update via Bash:

   python .claude/scripts/code_graph update --json
   

2. Report results: Files updated, added, deleted
3. If no changes detected, report "Working tree clean — graph already up to date"

Options

Flag	Description	Default
--base	Base commit to diff against	HEAD~1
--repo	Repository root path	.
--json	Output results as JSON	off

Related Skills

• /graph-sync — Sync committed changes (invokes /graph-update in step 4)
• /graph-build — Full or incremental graph build
• /graph-blast-radius — Analyze structural impact of changes
• /graph-query — Query code relationships

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Update Graph (Working Tree)

Update the knowledge graph with uncommitted working tree changes (staged + unstaged).

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.