Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentnpx agentmods add skills/topprismdata/cultivating-ml-agent/gsd-loop-engineeringWrote 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/topprismdata/cultivating-ml-agent/gsd-loop-engineering)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/gsd-loop-engineering"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/gsd-loop-engineering/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/topprismdata/cultivating-ml-agent/gsd-loop-engineering"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/gsd-loop-engineering.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 | $0.00136 | $0.02264 |
| Opus 5.5 | $0.00054 | $0.00906 |
| Sonnet 5.5 | $0.00027 | $0.00453 |
| Haiku 4.5 | $0.00014 | $0.00226 |
Grade A, and why
gsd-loop-engineering 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 1mo 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GSD Loop Engineering for ML Tasks
Problem
ML pipelines are naturally multi-stage loops:
- Plan → 2. Execute (data prep / train / generate submission) → 3. Verify (format / CV-LB gap / sanity) → 4. Fix or Ship.
Most agentic ML workflows fail because:
- Context rot: long sessions degrade model output (e.g., 0/1 submission bug in S6E2 was partly due to loss of focus)
- No verification: tasks accepted as done without adversarial checking (S6E4 submission would have been caught by a verifier)
- No shared memory: each step reinvents context from scratch
- Drift on style: best practices from start of session get forgotten by end
GSD Core (open-gsd/gsd-core) is the canonical loop-engineering framework solving exactly this. This skill adapts its patterns to ML.
Context / Trigger Conditions
Use this skill when:
- Working on a Kaggle competition end-to-end
- Designing a multi-stage ML pipeline (data → features → train → ensemble → submit)
- Session has > 50 turns and quality is degrading
- You want auditable proof each step worked (not just "looks right")
- You're tempted to use
--dangerously-skip-permissions(don't)
Don't use:
- Single-file one-off scripts
- Pure EDA with no downstream work
- Research tasks where you're just reading papers
Solution: 5-Phase ML Loop
┌────────────────────────────────────────────────────────────┐
│ Phase 1: DISCUSS (gsd-discuss-phase) │
│ - Resolve ambiguities in the task brief │
│ - Output: .planning/phases/<N>-DISCUSS.md │
├────────────────────────────────────────────────────────────┤
│ Phase 2: PLAN (gsd-plan-phase) │
│ - Research + plan, but in fresh-context subagent │
│ - Plans declare wave dependencies (DAG) │
│ - Output: .planning/phases/<N>/RESEARCH.md + PLAN-<M>.md │
├────────────────────────────────────────────────────────────┤
│ Phase 3: EXECUTE (gsd-execute-phase) │
│ - Run plans in waves (parallel where independent) │
│ - Each plan is a fresh-context subagent │
│ - Output: artifacts (models, submissions, logs) │
├────────────────────────────────────────────────────────────┤
│ Phase 4: VERIFY (gsd-verify) │
│ - Adversarial check: assume goal NOT achieved │
│ - Findings classified BLOCKER / WARNING / VERIFIED │
│ - Output: .planning/phases/<N>/VERIFICATION.md │
├────────────────────────────────────────────────────────────┤
│ Phase 5: SHIP or FIX │
│ - VERIFIED → ship submission, archive artefacts │
│ - BLOCKER → generate fix plan, re-enter Phase 2 │
│ - WARNING → decide case-by-case │
└────────────────────────────────────────────────────────────┘
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.
- 1mo ago First seen · 199 lines · 136 tokens per session scan A 3f82a272b62c
gsd-loop-engineering is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 11d ago), licensed MIT. It adds 136 tokens to every session and 2,264 once invoked, about $0.0005 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 2026-08-31.
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