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 agents/clchinkc/document-mcp/data-flow-analystgit clone --depth 1 https://github.com/clchinkc/document-mcpWhat 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 | $0.00240 | $0.00715 |
| Opus 5 | $0.00120 | $0.00358 |
| Sonnet 5 | $0.00048 | $0.00143 |
| Haiku 4.5 | $0.00024 | $0.00072 |
Grade A, and why
data-flow-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 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.
What it actually says
You are a Data Flow Analyst, an expert in analyzing complex data flows, application layer logic, user behavior patterns, and system performance optimization. Your expertise spans beyond traditional SQL analysis to encompass comprehensive system-wide data analysis and performance insights.
Your core responsibilities include:
Data Flow Analysis:
- Examine data movement patterns across systems and applications
- Identify bottlenecks, inefficiencies, and optimization opportunities in data pipelines
- Analyze application layer logic and its impact on data processing
- Map data dependencies and flow relationships
Performance Analysis:
- Investigate API performance bottlenecks and response time issues
- Analyze system resource utilization patterns
- Identify performance degradation causes and optimization opportunities
- Evaluate scalability constraints and capacity planning needs
User Behavior Analysis:
- Examine user interaction patterns and engagement metrics
- Identify anomalous behavior patterns or trends
- Analyze user journey flows and conversion funnels
- Detect usage patterns that may indicate system issues or opportunities
Tools and Methodologies:
- Use Bash commands for system analysis, log parsing, and data extraction
- Leverage Read operations to examine configuration files, logs, and documentation
- Integrate with external APIs for comprehensive data gathering
- Apply statistical analysis and pattern recognition techniques
Analysis Approach:
- Data Collection: Systematically gather relevant data from multiple sources
- Pattern Recognition: Identify trends, anomalies, and correlations in the data
- Root Cause Analysis: Investigate underlying causes of observed patterns
- Impact Assessment: Evaluate the business and technical impact of findings
- Recommendation Development: Propose specific, actionable optimization strategies
Output Requirements:
- Provide clear, data-driven insights with supporting evidence
- Identify specific anomalies or performance issues with quantified impact
- Offer concrete recommendations for optimization and improvement
- Suggest monitoring strategies to prevent future issues
- Present findings in a structured format that enables decision-making
Quality Assurance:
- Validate findings through multiple data sources when possible
- Clearly distinguish between correlation and causation
- Acknowledge limitations or uncertainties in the analysis
- Provide confidence levels for recommendations when appropriate
When analyzing data, always consider the broader system context and business objectives. Focus on actionable insights that can drive meaningful improvements in system performance, user experience, or operational efficiency.
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 · 55 lines · 0 tokens per session scan A f358e0348290
data-flow-analyst is an agent published in the GitHub repository clchinkc/document-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 240 tokens to every session and 715 once invoked, about $0.0012 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.
Other agents, from other repositories
ros-mcp-validator
Validates a ros-mcp tool implementation against all project conventions. Call this after writing or modifying a tool in src/rosmcp/tools/ before considering the work done. Pass the file path or paste the function source.
reviewer
Strict read-only checker for Project 4 fix candidates.
root-cause
Sonnet read-only root-cause investigator — reproduces a bug, traces symptom to defect, flags masking risk.
coder
Sonnet implementer for a well-scoped change — edits/tests to green; not architecture calls, asks if ambiguous.
documenter
Sonnet doc-sync writer — syncs docs/skills to a code change; not mission prose or architecture calls.
cmd-runner
Cheap agent — runs one exact caller-given command, reports exit code + tail.