AC tinker
Use this skill whenever the user mentions Tinker, tinker CLI, training runs, checkpoints, model fine-tuning with Tinker, tinker-cookbook, tinker recipes, or any Thinking Machines AI SDK operations. Also trigger when users ask about listing/inspecting/downloading/deleting training checkpoints, pushing checkpoints to HuggingFace, managing checkpoint TTL, configuring post-training pipelines (SFT, RL, math RL, code RL, distillation, preference learning, RLHF, tool use training, multi-agent RL, prompt distillation, rubric grading, VLM classification, Harbor RL), or working with tinker:// paths. Use this skill even if the user just mentions "tinker" in passing — it covers the full Tinker ecosystem including CLI, Python SDK, and cookbook recipes.
As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (tinker) differs from the folder (tinker-rlskill)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Steps. 10 steps
- 100Execution cost. Instruction body is 2171 tokens
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)
- +1No license
- +2Single-language instructions
- +3Description length 749: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 10 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.