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BD start-run

Launch local or SLURM prime-rl training runs with the rl, sft, and inference entrypoints, including dry runs. Model Factory uses model-factory; standalone evals use eval.

PrimeIntellect-ai/prime-rl Agent Skills author: PrimeIntellect-ai 1 file body ≈ 2 134 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Launch local or SLURM prime-rl training runs with the rl, sft, and inference entrypoints, including dry runs.

As a process D 38/100 · Unfinished process — References files that are not bundled: ../../model-factory/SKILL.md

ProcedureData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
D
38/100
Unfinished process
References files that are not bundled: ../../model-factory/SKILL.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:103
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    Trainer checkpoints are DCP-sharded (`<run_dir>/checkpoints/step_{n}/trainer`). Convert to HF safetensors with `uv run python tools/convert_dcp_to_bf16.py <run_dir>/checkpoints/step_{n}` (writes `<ckp
    detector

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../../model-factory/SKILL.md
  • note edit-residue the text marks something as outdated (lines 24): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 38/100

Will not run. References files that are not bundled: ../../model-factory/SKILL.md
  • 0Tools and files. 1 referenced file(s) missing: ../../model-factory/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2134 tokens
  • low The response is described with custom markup (21 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 170: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.