Borrowing it
Nothing to install: this file belongs to sakrut/ai-code-graph. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sakrut/ai-code-graph/main/.claude/commands/cg/context.mdgit clone --depth 1 https://github.com/sakrut/ai-code-graphWrote 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/sakrut/ai-code-graph/context)<a href="https://agentmods.dev/commands/sakrut/ai-code-graph/context"><img src="https://agentmods.dev/badge/commands/sakrut/ai-code-graph/context/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/commands/sakrut/ai-code-graph/context"><img src="https://agentmods.dev/badge/commands/sakrut/ai-code-graph/context.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00149 |
| Opus 5.5 | $0.00000 | $0.00060 |
| Sonnet 5.5 | $0.00000 | $0.00030 |
| Haiku 4.5 | $0.00000 | $0.00015 |
Grade A, and why
context 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.
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
Get method context before editing: $ARGUMENTS
Steps:
- Run
ai-code-graph context "$ARGUMENTS" --db ./ai-code-graph/graph.db - If the database doesn't exist, inform the user to run
ai-code-graph analyzefirst - Review the output: complexity, callers, callees, cluster, and duplicates
- If complexity (CC) is high (>10), warn about the method's complexity before making changes
- If the method has callers, note that changes may affect those callers
- If duplicates exist, suggest whether the change should also apply to the duplicate methods
- Proceed with the user's requested edit, keeping the context in mind
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.
- 1mo ago First seen · 11 lines · 0 tokens per session scan A 7168dedd5721
context is a command published in the GitHub repository sakrut/ai-code-graph (3 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 149 tokens. 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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