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 ADIKANT/datalens-dev-mcp --skill datalens-dataset-wizardgit clone --depth 1 https://github.com/ADIKANT/datalens-dev-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/adikant/datalens-dev-mcp/datalens-dataset-wizard)<a href="https://agentmods.dev/skills/adikant/datalens-dev-mcp/datalens-dataset-wizard"><img src="https://agentmods.dev/badge/skills/adikant/datalens-dev-mcp/datalens-dataset-wizard/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/adikant/datalens-dev-mcp/datalens-dataset-wizard"><img src="https://agentmods.dev/badge/skills/adikant/datalens-dev-mcp/datalens-dataset-wizard.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 | $0.00045 | $0.00720 |
| Opus 5.5 | $0.00018 | $0.00288 |
| Sonnet 5.5 | $0.00009 | $0.00144 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
datalens-dataset-wizard 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 17d 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
DataLens Dataset and Wizard
Work from the exact dashboard project/subproject. A description-only edit needs a narrow patch and saved readback. Field, formula, filter or source changes also need applicable field validation and bounded data preview; JSON alone cannot prove metric meaning. Use Dataset/Wizard contracts for typed drafts, field roles, preview and revision-safe updates. Use current field GUIDs; ambiguous labels cannot identify fields. Preserve dataset-global versus chart-local fields, calculation levels, field order and the existing object's technology. Choose technology and source using the project-first order; a JavaScript request alone does not exclude an existing Dataset.
For a new metric or upstream-dependent field, first follow current source readiness. A merged change or green CI does not prove that this connection exposes the field or data. Complete independent authorized edits while naming the dependent remainder.
New typed Wizard drafts resolve the same profile as recipes. Pass wizard.project_root for local overrides and wizard.presentation only for explicit exceptions; wizard.family retains a chosen family. Readback dataset_fields on the draft enables complete local SDK payload validation before create. Labels are actual measure roles, grids default off (including y2), and titles belong in Dashboard widgets. Supported grid overrides need no justification. Default palette collisions resolve deterministically; larger series sets receive an advisory. Use measure_colors keyed by GUID for meaningful series roles; preserve those identities across filters/periods. Default single-series bars use one color and category labels. Chart descriptions are not widget hints.
A new dataset draft keeps object_type, name and client_ref at its root; connection_id, source and fields belong inside dataset. Use typed public operations; the backend compiles against its pinned SDK. Measure Names and Measure Values are chart technical fields, not physical columns.
Follow authorized scope and delivery. Load visualization decisions only for the relevant semantic choice. Preview establishes bounded data evidence; provider acceptance and rendered results require their own checks.
Route placement to datalens-dashboard, JavaScript variants to datalens-editor, metadata or RLS identity lookup to datalens-inspect, and backup/cleanup to datalens-maintenance.
For unknown effects follow repeat-effect recovery: only proven non-application or separate informed repeat authorization permits another attempt. Failed reconciliation, absent inventory and a new operation_id do not make a retry safe.
Treat write_verified, dataset_validation, bounded data preview and Browser
rendering as separate evidence. An invalid saved Dataset is not task success;
metadata-only repairs may preserve pre-existing invalid state, but newly changed
fields must pass prospective title/GUID validation before a batch writes. If a
required upstream field is not exposed, finish independent metadata work and mark
the dependent part blocked; do not wait for a merge or substitute a source.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 17d ago Changed 4ee8fb03a9cf
- 22d ago Changed · +7 lines 1fd711fe3c7c
- 1mo ago Changed · +2 lines 8af57665b7dc
- 1mo ago Changed · +2 lines b30a9a12ffe7
- 1mo ago Changed · +2 lines 68ddb8960414
- 1mo ago Changed 95926b9eaed8
- 1mo ago Changed · -4 lines · +18 tokens per session cd5c8e039e95
- 1mo ago Changed · +2 lines 5ed2f6980515
- 1mo ago Changed · +4 lines d7603a0b86de
- 1mo ago First seen · 13 lines · 27 tokens per session scan A f4f53e42c158
datalens-dataset-wizard is a skill published in the GitHub repository ADIKANT/datalens-dev-mcp (7 stars, last pushed 11d ago), licensed Apache-2.0. It adds 45 tokens to every session and 720 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 2026-09-08.
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