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 agentmods add instructions/optave/ops-codegraph-tool/claude-mdgit clone --depth 1 https://github.com/optave/ops-codegraph-toolWhat 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 | $0.05991 | $0.05991 |
| Opus 5 | $0.02995 | $0.02995 |
| Sonnet 5 | $0.01198 | $0.01198 |
| Haiku 4.5 | $0.00599 | $0.00599 |
Grade A, and why
ops-codegraph-tool CLAUDE.md 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 2d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Hooks enforce code quality. This project uses Claude Code hooks (
.claude/hooks/) to automatically inject file-level dependency context on reads, rebuild the graph after edits, block commits with cycles or dead exports, run lint on staged files, and show diff-impact before commits. If codegraph reports an error or produces wrong results when analyzing itself, that's a real bug — don't work around it or ignore it. Flag it to the user and, if it's blocking the current task, fix it.
Never fabricate facts. Do not state licenses, version numbers, feature claims, or any factual information without first verifying it (read the file, run the command, check the source). If you don't know, say so — do not guess.
Never document bugs as expected behavior. If two engines (native vs WASM) produce different results, that is a bug in the less-accurate engine — not an acceptable "parity gap." Adding comments or tests that frame wrong output as "expected" blocks future agents from ever fixing it. Instead: identify the root cause, file an issue, and fix the extraction/resolution layer that produces incorrect results. The correct response to "engine A reports 8 cycles, engine B reports 11" is to fix the 3 false cycles in engine B, not to document why the difference is okay.
Never silently skip verification. If tests, builds, or any verification step cannot run or fails for any reason (compilation errors, platform issues, missing dependencies), STOP and report the issue to the user immediately. Never silently proceed with unverified changes. Let the user decide whether to proceed — do not make that decision yourself.
Scope discipline — open issues, don't expand scope. When you encounter anything out of scope — a pre-existing bug, a refactor opportunity, a potential improvement, a missing feature, or any other finding that doesn't directly affect the correctness of the current task — immediately open a GitHub issue with
gh issue createbefore continuing. Do not hold the finding in memory or defer it to a comment. Only address it inline when it directly blocks the result of the work in front of you. This keeps PRs focused (one concern per PR) while ensuring no finding is lost.
Prioritize the best architecture, not the smallest diff. Do not default to the simplest or most localized fix. Choose the approach that fits the codebase's architecture best, even when that means larger changes, moving code across modules, or restructuring an abstraction. Do not be afraid of bigger changes — a larger diff that leaves the design healthier is preferable to a small diff that entrenches a poor structure. Surface the architectural reasoning to the user; don't silently shrink the change to avoid the work.
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.
- 2d ago First seen · 252 lines · 5,991 tokens per session scan A 925ff2daf752
ops-codegraph-tool CLAUDE.md is an instructions file published in the GitHub repository optave/ops-codegraph-tool (92 stars, last pushed 3d ago), licensed Apache-2.0. It adds 5,991 tokens to every session, about $0.0300 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 instructions, from other repositories
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Instructions for flytohub/flyto-indexer, covering agent rules, gate remediation loop, flyto2 project memory contract and flyto2 frontend quality gate.
flyto-indexer CLAUDE.md
Instructions for flytohub/flyto-indexer, covering claude notes, cross-agent handoff, shared code intelligence, code intelligence (flyto-indexer) and first-time setup.
codegraph CLAUDE.md
Instructions for cognitx-leyton/codegraph, covering claude.md, project summary, using the graph during development, /graph — query the live graph and /graph-refresh — update the graph.
codebase-intelligence CLAUDE.md
Instructions for bntvllnt/codebase-intelligence, covering codebase intelligence - claude code rules, project overview, architecture, pipeline and key conventions.
codebase-intelligence AGENTS.md
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Archon CLAUDE.md
Instructions for Schr0d/Archon, a project described as: Most tools give AI access to the codebase. Archon gives AI access to the change itself. Structural Analysis tool intergrated for AI planning and review workflow both for ai and human ( Claude Code, Codex, OpenCode and Others).