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.
git clone --depth 1 https://github.com/linkpranay-ai/context-engineering-protocolWrote 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/commands/linkpranay-ai/context-engineering-protocol/ult-codegraph)<a href="https://agentmods.dev/commands/linkpranay-ai/context-engineering-protocol/ult-codegraph"><img src="https://agentmods.dev/badge/commands/linkpranay-ai/context-engineering-protocol/ult-codegraph.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00041 | $0.00392 |
| Opus 5 | $0.00020 | $0.00196 |
| Sonnet 5 | $0.00008 | $0.00078 |
| Haiku 4.5 | $0.00004 | $0.00039 |
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 today.
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.
What it actually says
Read and follow the skill at .github/skills/ult-codegraph/SKILL.md.
When invoked directly by an engineer:
- Check whether
graphifyis installed (graphify --help); if not, runuv tool install graphifyy(orpipx install graphifyy). - Run
graphify update . --no-clusterto (re)generate the graph (no LLM needed — incremental on subsequent runs, only re-parsing changed files). - Optionally run
graphify cluster-only . --no-labelto also produceGRAPH_REPORT.md(community names stay generic without a configured LLM backend — that's fine, the graph data itself is unaffected and the report's God Nodes / Surprising Connections / Import Cycles sections are useful either way). - Report back: how many nodes/edges were generated, and point the user at
graphify-out/GRAPH_REPORT.mdfor the human-readable summary. Nothing to copy or normalize —graphify-out/is graphify's own fixed, predictable output location, and consuming skills query it there directly (seeCONSUMING-CODE-GRAPH.md).
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.
- today First seen · 33 lines · 41 tokens per session scan A 682a6225036c
codegraph is a command published in the GitHub repository linkpranay-ai/context-engineering-protocol (8 stars, last pushed today), licensed Apache-2.0. It adds 41 tokens to every session and 392 once invoked, about $0.0002 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 2026-09-08.
Other commands, from other repositories
cleanup
Detect and remove orphaned code, unused components, dead routes, and stale database artifacts.
fix
Use for a fast bug fix with minimal ceremony (3 questions, TDD, done).
harness-audit
Use when auditing the Claude Code / AI harness setup for reliability, cost, and completeness.
refactor-clean
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
lint-check
Runs linting and code quality checks. Catches style issues, potential bugs, and enforces project standards.
ui-aqa-flow-test-report-analysis
Phase 7 Test Report Analysis of ui-aqa-flow.