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 toflow-ai/toflow-mcp --skill toflow-build-reportgit clone --depth 1 https://github.com/toflow-ai/toflow-mcpWrote 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/toflow-ai/toflow-mcp/toflow-build-report)<a href="https://agentmods.dev/skills/toflow-ai/toflow-mcp/toflow-build-report"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-build-report/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/toflow-ai/toflow-mcp/toflow-build-report"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-build-report.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 | $0.00051 | $0.00477 |
| Opus 5.5 | $0.00020 | $0.00191 |
| Sonnet 5.5 | $0.00010 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
toflow-build-report 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 4d 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
toflow — Build a Report or Dashboard
Steps
- Call
list_datasetsfirst and use only field names it returns — never guess. If the user's request is ambiguous (unclear metric, grouping, or filter), ask before proceeding. - Call
list_dashboardsto check whether a suitable dashboard already exists for this report, orcreate_dashboardif the user wants a dedicated new view. - Call
get_report_guidefor the exact configuration shape. In short:querytakesgroup_by(groupable fields),metrics({field, function, alias}withcount/count_distinct/sum/avg/min/max),select_fields,order_by, andlimit;charttakeschart_type(line/bar/area/pie/scatter/table/kpi/funnel/heatmap),x_axis,y_axis,color_field;filtersis a list of{field, operator, value}(eq,contains,in,between,before/after,is_null, etc.). - Always call
validate_and_preview_reportbefore saving. Show the preview rows to the user and ask if it looks right before saving — ifvalid=false, explain the errors in plain terms and don't just retry blindly. - Call
create_reportonly after the user has seen the preview and confirmed. This saves the report permanently to a dashboard. - Use
run_reportto re-fetch the latest data for an existing saved report.
Guardrails
- Never call
create_reportwithout first callingvalidate_and_preview_reportand showing the preview to the user. - If the preview looks wrong (empty, unexpected values), fix the filter/field configuration before saving — don't save a broken report and iterate afterward.
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.
- 4d ago First seen · 21 lines · 51 tokens per session scan A 74538f51fa67
toflow-build-report is a skill published in the GitHub repository toflow-ai/toflow-mcp (6 stars, last pushed 5d ago), licensed MIT. It adds 51 tokens to every session and 477 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 2026-10-10.
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