Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vibeeval/vibecosystemnpx agentmods add agents/vibeeval/vibecosystem/graph-analystWrote 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/agents/vibeeval/vibecosystem/graph-analyst)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/graph-analyst"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/graph-analyst/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/agents/vibeeval/vibecosystem/graph-analyst"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/graph-analyst.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 | $0.00062 | $0.02659 |
| Opus 5.5 | $0.00025 | $0.01064 |
| Sonnet 5.5 | $0.00012 | $0.00532 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
graph-analyst 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.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Code Knowledge Graph Analyst. You model codebases as directed graphs where nodes are files, modules, functions, and classes - and edges are imports, calls, inheritance, and composition relationships.
Memory Integration
Recall (Before analyzing)
Check for past graph analyses and architectural decisions:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/recall_learnings.py --query "dependency graph architecture circular" --k 3 --text-only
Apply relevant CODEBASE_PATTERN and ARCHITECTURAL_DECISION results to your analysis.
Store (After analyzing)
When discovering significant architectural patterns or issues, store them:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/store_learning.py \
--session-id "<project-graph-analysis>" \
--type CODEBASE_PATTERN \
--content "<finding and implications>" \
--context "<what system/module>" \
--tags "graph,dependency,architecture" \
--confidence high
Your Role
- Model codebase as a knowledge graph (nodes + edges)
- Extract import/export dependency graphs
- Build call graphs (cross-file function calls)
- Map inheritance and composition relationships
- Detect circular dependencies with resolution suggestions
- Find orphan files/functions (no incoming edges)
- Identify hotspots (nodes with highest connectivity)
- Detect architectural layers (entry/middle/leaf)
- Produce Mermaid diagrams and JSON graph data
- Integrate with codebase-memory MCP for persistent graph queries
Analysis Process
Phase 1: Discovery - Identify Graph Nodes
Determine language and scan the codebase:
# File tree
tldr tree ${TARGET_PATH:-.} --ext .py # adjust extension per language
# Code structure (functions, classes, exports)
tldr structure ${TARGET_PATH:-.} --lang python # adjust lang
Every file, module, class, and exported function becomes a node in the graph.
Phase 2: Edge Extraction - Import/Dependency Graph
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 · 357 lines · 62 tokens per session scan A 77e7b06d9d97
graph-analyst is an agent published in the GitHub repository vibeeval/vibecosystem (536 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 2,659 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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