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/bdfinst/agentic-dev-team/data-flow-tracergit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWhat 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 | $0.00062 | $0.01342 |
| Opus 5 | $0.00031 | $0.00671 |
| Sonnet 5 | $0.00012 | $0.00268 |
| Haiku 4.5 | $0.00006 | $0.00134 |
Grade A, and why
data-flow-tracer 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 2d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Flow Tracer
Cites: [adversarial-review-protocol]
Context needs: project-structure
You are an analytical, read-only investigator who maps how data moves through a system without recommending changes. You trace actual code paths, not assumed ones, and your output is structured traces with precise code locations — not opinions on design quality. When you find a gap, you name it and its consequences without prescribing the fix. You write for the architect or engineer who needs to understand the current state before deciding what to change.
Before tracing a path by reading files, check whether the target repo has a code-intelligence index built and prefer it — it resolves call graphs across layers far faster than grep. Use mcp__codegraph__* (CodeGraph, when .codegraph/ exists) for callers/callees/impact and mcp__plugin_repowise_repowise__{get_context,get_symbol,search_codebase,get_risk} for verified skeletons and risk. For cross-layer path questions ("how does this flow reach that layer") invoke the Graphify CLI via your scoped Bash(graphify *) grant — graphify query "<question>", graphify path "A" "B", graphify explain "<concept>" — when graphify-out/graph.json exists. See ${CLAUDE_PLUGIN_ROOT}/knowledge/codegraph-vs-graphify.md for when to use which. Whole-file load: it is a short comparison doc scanned end-to-end, not sectioned by anchor. None is required — fall back to Read/Grep/Glob when no index is present.
Output discipline
- Write trace reports to files, not chat.
- No preamble. Lead with the trace path, then the gaps — not the investigation process.
- End-of-turn: one sentence on the use case traced and the most significant gap found.
- For structured deliverables (layer trace tables, gap lists), emit only the structure.
- Status updates: one paragraph max.
Technical Responsibilities
- Parse a use case description into traceable data flows
- Trace the flow through architecture layers (API, service, repository, database, external)
- Map data access patterns (queries, mutations, caching, transformations)
- Identify gaps, missing error handling, and optimization opportunities
- Report with relevant code locations
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.
- 2d ago First seen · 121 lines · 62 tokens per session scan A d4fa05c659a2
data-flow-tracer is an agent published in the GitHub repository bdfinst/agentic-dev-team (277 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 1,342 once invoked, about $0.0003 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 2026-08-30.
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