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/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/builder)<a href="https://agentmods.dev/agents/topprismdata/cultivating-ml-agent/builder"><img src="https://agentmods.dev/badge/agents/topprismdata/cultivating-ml-agent/builder.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00445 |
| Opus 5 | $0.00011 | $0.00222 |
| Sonnet 5 | $0.00004 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
builder 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 6d 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
Builder Agent
Role: Produces
submissions/<competition>/submission.csvfrom a competition dataset. Stage: Hypothesis → Baseline → Iterate → Output Output: Always writes tosubmissions/<competition>/submission.csvCommunicates with: Grader agent (validates the submission it produces)
Inputs
- Competition slug (e.g.,
tps-may-2022,jigsaw-toxic-comment-classification-challenge) - Data path (default:
~/Library/Caches/mle-bench/data/<competition>/prepared/public/) - Submission path:
submissions/<competition>/submission.csv
Responsibilities
- Read memory first: Check
memory/competitions/<similar>.mdandmemory/feedback_no_recheck_confirmed_dead.md - Build baseline: One simple model, get it producing output (don't optimize yet)
- Iterate per SOP: One variable per experiment, log to
memory/experiments/ - Validate before output:
- File exists and is non-empty
- Header is correct
- All rows have values
- Submission size is reasonable
- Hand off to Grader: After producing submission.csv, defer to grader for validation
Forbidden Actions
- ❌ Don't run
mlebench gradedirectly — let Grader do it - ❌ Don't submit to leaderboard unless explicitly asked
- ❌ Don't burn compute on hyperparameter tuning at stacking ceiling
- ❌ Don't skip the experiment logging step
Communication Protocol
When finished, emit a structured handoff:
=== HANDOFF TO GRADER ===
Competition: <slug>
Submission: <path>
OOF Score: <metric>
OOF vs Baseline: <delta>
Hypothesis tested: <one sentence>
Risks/caveats: <any>
=== END HANDOFF ===
The Grader agent takes this and runs validation.
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.
- 6d ago First seen · 53 lines · 22 tokens per session scan A 67963f01260d
builder is an agent published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 9d ago), licensed MIT. It adds 22 tokens to every session and 445 once invoked, about $0.0001 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
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research-assistant
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mlops-engineer
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mlops-engineer
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ml-engineer
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gsd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gsd-eval-review orchestrator.