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 skills add athola/claude-night-market --skill data-flowgit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/skills/athola/claude-night-market/data-flow)<a href="https://agentmods.dev/skills/athola/claude-night-market/data-flow"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/data-flow/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/skills/athola/claude-night-market/data-flow"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/data-flow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00027 | $0.00745 |
| Opus 5 | $0.00014 | $0.00373 |
| Sonnet 5 | $0.00005 | $0.00149 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
data-flow 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 6d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Flow Diagram
Generate a Mermaid sequence diagram showing how data moves between components in a codebase.
When To Use
- Tracing how a request flows through the system
- Understanding data transformation pipelines
- Documenting API call chains
- Answering "what happens when X is called?"
When NOT To Use
- Static call structure (use
cartograph:call-chain) - Process steps and state transitions (use
cartograph:workflow-diagram)
Workflow
Step 1: Explore the Codebase
Dispatch the codebase explorer agent:
Agent(cartograph:codebase-explorer)
Prompt: Explore [scope] and return a structural model.
Focus on function calls, data transformations, and
inter-module communication for a data flow diagram.
Step 2: Generate Mermaid Syntax
Transform the structural model into a Mermaid sequence diagram.
Rules for data flow diagrams:
- Use
sequenceDiagramfor request/response flows - Participants are modules or components (not functions)
- Arrows show data direction:
->>for calls,-->>for returns - Use
activate/deactivatefor long-running operations - Add
Note overfor data transformations - Limit to 8-10 participants maximum
- Use
alt/elsefor conditional flows - Handle circular calls by showing them once with a note
Example output:
sequenceDiagram
participant User
participant Command as /commit
participant Sanctum as sanctum.commit
participant Leyline as leyline.git_platform
participant Git
User->>Command: /commit
Command->>Sanctum: generate_message()
Sanctum->>Leyline: get_staged_changes()
Leyline->>Git: git diff --cached
Git-->>Leyline: diff output
Leyline-->>Sanctum: structured changes
Note over Sanctum: Classify change type
Sanctum-->>Command: commit message
Command->>Git: git commit -m "..."
Step 3: Render via MCP
Call the Mermaid Chart MCP to render:
mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram
prompt: "Data flow diagram of [scope/feature]"
mermaidCode: [generated syntax]
diagramType: "sequenceDiagram"
clientName: "claude-code"
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.
- 6d ago First seen · 106 lines · 27 tokens per session scan A dea19a351678
data-flow is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 745 once invoked, about $0.0001 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 2026-09-03.
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