codegraph

codegraph is a skill for Claude Code, Codex from linkpranay-ai/context-engineering-protocol. It costs 40 tokens per session (4,514 once invoked), scanned A, original, Apache-2.0.

A tool that builds a knowledge graph of a codebase: a map of files, functions, classes, imports, calls, uses, and inheritance relationships.

In plain words
What is it for?
Use it to generate or refresh the graphify-out/ graph so other tools can inspect cross-file relationships before editing code.
Why use it?
It reveals connections across files that a text search may miss, helping agents understand how a change can affect the wider codebase.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument; mentions AGENTS.md.

Good fit Use it to generate or refresh the graphify-out/ graph so other tools can inspect cross-file relationships before editing code.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/linkpranay-ai/context-engineering-protocol/ult-codegraph
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add linkpranay-ai/context-engineering-protocol --skill ult-codegraph
Clone the repo
git clone --depth 1 https://github.com/linkpranay-ai/context-engineering-protocol

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for codegraph

README.md
[![agentmods](https://agentmods.dev/badge/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph/github.svg)](https://agentmods.dev/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph)
Your own site
<a href="https://agentmods.dev/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph"><img src="https://agentmods.dev/badge/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for codegraph

Your own site · 80×15
<a href="https://agentmods.dev/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph"><img src="https://agentmods.dev/badge/skills/linkpranay-ai/context-engineering-protocol/ult-codegraph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,514 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.04514
Opus 5.5 $0.00016 $0.01806
Sonnet 5.5 $0.00008 $0.00903
Haiku 4.5 $0.00004 $0.00451

Measured 1mo ago against content hash 35ec649d5332, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

codegraph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/check_graphify_output.py, scripts/tests/test_check_graphify_output.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/skills/ult-codegraph/SKILL.md · 345 lines

How it starts

The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Codebase Graph (graphify wrapper)

Wraps the external graphify CLI (https://github.com/safishamsi/graphify, MIT license, distributed as the PyPI package graphifyy) to build a knowledge graph of a codebase — files, functions, classes, and their calls/imports/uses/inherits relationships — at graphify's own fixed output location, graphify-out/, where other skills query it directly.

Status: piloting. Validated on a real ~40 KSLOC FastAPI codebase (a structural lead — webhook_dispatcher.py → RetryPolicy, the bridge between two otherwise-separate subsystems — that a textual grep could not find, since the bridging file never mentions the target term) before this migration. Now rolling out to a small set of engineering volunteers piloting it on substantially larger codebases (500 KSLOC+), where graph.json itself can run to tens of MB and the scoped-query pattern below stops being optional and starts being the only thing that scales. Report findings (works well / doesn't / surprises) as an issue in this repo so this can graduate out of pilot status or be reworked.

How to generate / refresh the graph

Step 0 — scope the index (first run only)

Before running graphify update on the whole repo, take a quick look at what's actually there:

# Top-level dirs by file count — surfaces generated/vendored/other-language subtrees
for d in */; do n=$(find "$d" -type f | wc -l); echo "$n $d"; done | sort -rn | head -10

Then ask: is there one directory that holds essentially all the source you care about, separate from generated code, vendored third-party libraries, or bindings for other languages?

  • Yes — point graphify update at that directory instead of . (e.g. graphify update src/ --no-cluster). Smaller, cleaner graph; less cross-language noise in affected/explain results.
  • No (everything's genuinely mixed) — run graphify update . --no-cluster on the repo root. Validated on a real ~300 KSLOC, 8-language, 2425-file monorepo (google/protobuf): completed in 5m28s, 73K nodes / 254K edges, with affected/explain queries returning in ~3s — an unscoped run on a messy repo is workable, just expect occasional cross-language results.
  • No, but multiple genuinely independent roots (e.g. sibling repos or workspace directories with no shared parent worth scoping to) — index each root separately, then combine:

Read the full file on GitHub · 345 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +30 lines 35ec649d5332
  2. 1mo ago First seen · 315 lines · 40 tokens per session scan A b7534f2bb814

Subscribe to this mod's changes

codegraph is a skill published in the GitHub repository linkpranay-ai/context-engineering-protocol (9 stars, last pushed 16d ago), licensed Apache-2.0. It adds 40 tokens to every session and 4,514 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

recovery

Handle blocked verification outcomes.

BrianNguyen29/x-harness · 4 tokens

ambiguity-detector

Takes surface scan results, optional LLM synthesis open-question entries, and optional tree-sitter AST signals to score project ambiguities (1-10). Outputs structured ambiguityfeed for smart-question-generator. Pure heuristic — no LLM calls. Designed for Stage 3.5 of the /gaai:bootstrap pipeline (between LLM synthesis…

digipulse-engineering/GAAI-framework · 77 tokens

memory-index-lint

Validate discoverability invariants of memory index.md and archive/superseded-decisions.archive.md. Tool-agnostic L3 prompt + capability-dependent L4 script. Detects table corruption, dangling/orphan supersession pointers, frontmatter inconsistencies, missing affordance signals.

digipulse-engineering/GAAI-framework · 64 tokens

remediate-failures

Correct failures, rule violations, and acceptance criteria gaps detected during QA review. Activate when qa-review returns FAIL. Fixes without redefining scope — loops until all quality gates pass.

digipulse-engineering/GAAI-framework · 43 tokens

post-mortem-learning

Analyze failures and suboptimal deliveries to identify root causes, contributing factors, and raw lessons. Activate after significant delivery failures, repeated QA failures, or when patterns of issues need to be understood.

digipulse-engineering/GAAI-framework · 44 tokens

aicrew-fix

Run the aicrew /fix fast bug fix flow in Codex (triage -> root cause -> TDD -> verify).

AKSoftCode/aicrew · 32 tokens