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.
npx skills add selvarajmurugesan90/ops-engineering-skills --skill code-knowledge-graph-tools-for-ai-agentsgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsWrote 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.
[](https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/code-knowledge-graph-tools-for-ai-agents)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/code-knowledge-graph-tools-for-ai-agents"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/code-knowledge-graph-tools-for-ai-agents/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.
<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/code-knowledge-graph-tools-for-ai-agents"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/code-knowledge-graph-tools-for-ai-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 71 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 128 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 71 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Agent Snooping · line 97 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 237 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 329 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 231 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00135 | $0.04339 |
| Opus 5 | $0.00068 | $0.02169 |
| Sonnet 5 | $0.00027 | $0.00868 |
| Haiku 4.5 | $0.00014 | $0.00434 |
Grade A, and why
code-knowledge-graph-tools-for-ai-agents 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 10d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Knowledge Graph Tools for AI Agents
Purpose
An AI coding agent without any precomputed structural index explores a
codebase the same slow way every time: repeated grep, file reads, and
directory listings to reconstruct facts (what calls this function, what
would break if I change this type, what's the blast radius of this file)
that are stable properties of the code and don't need to be rediscovered on
every session. Code-knowledge-graph tools solve this by parsing the
codebase once into a structural graph (or graph-like index) — call graphs,
type relationships, module dependencies — and exposing queries over that
graph to the agent via MCP tools, so a question like "what depends on this
function" becomes one precomputed lookup instead of a multi-step
grep-and-read exploration the agent has to redo from scratch each time.
This is a distinct concern from a RAG pipeline's semantic retrieval over
document/code text
(rag-pipeline-design) and from operating
a general-purpose vector database
(vector-database-operations-pinecone-weaviate-milvus):
a code knowledge graph indexes structure (calls, references, definitions,
dependencies), not just embedding-similar text chunks, though some of these
tools combine both. This skill compares three current tools in this space —
GitNexus, Graphify, and CodeGraph — on architecture, language coverage,
query model, and licensing, and covers choosing between them and wiring the
one you pick into an agent via MCP
(mcp-server-development covers
building an MCP server generally; this skill covers consuming these three
specific pre-built ones).
When to use
- An AI coding agent repeatedly burns tool calls and tokens re-exploring the same codebase structure (grep for callers, read files to trace a type) across sessions, and you want to give it a precomputed structural index instead.
- Deciding which of GitNexus, Graphify, or CodeGraph fits a given repository's language mix, size, and licensing constraints (open-source vs. commercial product).
- Setting up impact-radius or blast-zone analysis before a refactor — "what breaks if I change this function's signature" — as a query an agent can run directly rather than inferring from manual exploration.
- A codebase includes non-code artifacts (design docs, PDFs, recorded walkthrough videos) that should also be queryable alongside code structure, favoring a tool that ingests more than source files.
- Evaluating whether a commercial product can adopt one of these tools, which requires checking each tool's license (GitNexus's noncommercial license is a real blocker for commercial use without a paid tier; Graphify and CodeGraph are fully permissive).
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.
- 10d ago First seen · 330 lines · 135 tokens per session scan A a7563d7f91b5
code-knowledge-graph-tools-for-ai-agents is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 135 tokens to every session and 4,339 once invoked, about $0.0007 per session on Opus 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 2026-08-30.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.