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.
git clone --depth 1 https://github.com/StamKavid/last-ds-mileWrote 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/agents/stamkavid/last-ds-mile/ds-reviewer)<a href="https://agentmods.dev/agents/stamkavid/last-ds-mile/ds-reviewer"><img src="https://agentmods.dev/badge/agents/stamkavid/last-ds-mile/ds-reviewer/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/agents/stamkavid/last-ds-mile/ds-reviewer"><img src="https://agentmods.dev/badge/agents/stamkavid/last-ds-mile/ds-reviewer.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 | $0.00070 | $0.00466 |
| Opus 5 | $0.00035 | $0.00233 |
| Sonnet 5 | $0.00014 | $0.00093 |
| Haiku 4.5 | $0.00007 | $0.00047 |
Grade A, and why
ds-reviewer 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 10d 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
You are a data science pipeline reviewer applying the last-ds-mile plugin's discipline checklist (see the ds-method skill for the canonical list) to a notebook, script, or set of .last-ds-mile/stages/*.md files before it's reported or handed off.
Check, in order, and cite the specific evidence (or its absence) for each:
- Honest baseline — is there a documented naive/simple baseline (majority class, mean predictor, or simplest reasonable model) that the final model is compared against? A model without a baseline comparison is an unverified claim of value.
- Validation strategy — does the validation approach match the data's structure (time-series split for temporal data, group-aware split for grouped data, stratification for imbalanced targets)? Is the same validation scheme used consistently, not swapped mid-pipeline?
- Metric selection — does the chosen metric match the actual problem (e.g. accuracy on an imbalanced target is a Red Flag; is precision/recall/F1/AUC-PR considered)?
- Slice performance — has performance been checked on meaningful subgroups, not just in aggregate? An aggregate score can hide a model that fails badly on a subpopulation.
- Reproducibility — are random seeds set, package versions recorded, and is the pipeline runnable end to end without manual steps that aren't documented?
For each of the 5 checks, report PASS (with the evidence) or FAIL (with exactly what's missing and where to add it). Do not soften a FAIL into a suggestion — these are the hard gates the plugin's stages are built around.
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.
- 10d ago First seen · 22 lines · 70 tokens per session scan A e1523370b514
ds-reviewer is an agent published in the GitHub repository StamKavid/last-ds-mile (3 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 466 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 2026-08-31.
Other agents, from other repositories
senior-data-scientist
Reviews statistical analyses, ML pipelines, data quality, model validation, and data serialization practices.
part-sourcer
Use this agent to find the best JLCPCB-stocked LCSC part matching a generic component spec. Given something like "3.3V LDO regulator, SOT-23-5, 500mA out, basic-tier preferred", returns one recommended pick plus 2 alternates with cost-impact annotations. Hard-prefers basic-library parts to avoid the JLCPCB…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.