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 rules/codegraph-ai/codegraph-rules-for-agents/codegraphgit clone --depth 1 https://github.com/codegraph-ai/codegraph-rules-for-agentsWhat 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 | $0.00617 | $0.00617 |
| Opus 5 | $0.00309 | $0.00309 |
| Sonnet 5 | $0.00123 | $0.00123 |
| Haiku 4.5 | $0.00062 | $0.00062 |
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 yesterday.
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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeGraph — local code intelligence
Persistent semantic graph of your codebase. Resolves references — "who calls X?" is a single tool call, not multi-file grep.
First time? Run codegraph_reindex_workspace once (5-30s). Index persists across sessions.
Top 5 tools (use these first)
| When you need | Call this | NOT this |
|---|---|---|
| Who calls function X? | codegraph_get_callers |
grep for the function name |
| What breaks if I change X? | codegraph_analyze_impact |
reading every importing file |
| Context for editing a file | codegraph_get_edit_context |
reading 5+ files manually |
| Find a symbol by name | codegraph_symbol_search |
grep -r across the project |
| Module structure overview | codegraph_get_module_summary |
ls + reading each file |
URI format: file:///absolute/path. Use paths from symbol_search results directly.
Compact mode: pass compact: true for shorter output when scanning.
Common workflows
- PR review:
pr_context— one call: blast radius, test gaps, stale docs, reviewers - Refactoring:
symbol_search→analyze_impact→get_edit_context - Bug triage:
search_by_error→get_callers→get_ai_context(intent: "debug") - Onboarding:
get_module_summary→find_entry_points→get_call_graph
Decision rule
- Structural question? (callers, deps, impact) → codegraph tools
- Text question? (exact string, regex) → grep/read
- Not sure? → Try
codegraph_symbol_searchfirst. Empty → runcodegraph_reindex_workspace.
More tools
Navigation: get_callees, get_call_graph, get_dependency_graph, traverse_graph, find_by_imports, find_entry_points
Quality: analyze_complexity, find_hot_paths, find_circular_deps, find_dead_imports
PR review: pr_context (blast radius, test gaps, stale docs, commit hint, reviewers)
Docs: index_markdown, search_docs, verify_design, design_gaps, generate_architecture_doc
Memory: memory_store (pass agentSource: "cursor"), memory_search, memory_context
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.
- yesterday First seen · 50 lines · 617 tokens per session scan A f5caca4ebcca
codegraph is a cursor rule published in the GitHub repository codegraph-ai/codegraph-rules-for-agents (4 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 617 tokens to every session, about $0.0031 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 2026-08-31.
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