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BF training-data-annotation

Design and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active acquisition, annotator guidance, calibration, disagreement, adjudication, provenance, and cost-quality stopping. Do not use for general statistical inference, data storage pipelines, model training, or annotation-interface implementation; route those concerns to data-scientist, data-engineering, ml-engineering, or product-design-and-ux.

magnus919/agent-skills Agent Skills author: magnus919 MIT 11 files body ≈ 1 186 tokens Open the sourcegithub.com↗ analyzed 27 h ago

Design and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active…

As a process F 41/100 · Will not run — References files that are not bundled: ../data-scientist/SKILL.md, ../data-engineering/SKILL.md, ../ml-engineering/SKILL.md

AnalyzerData and analyticsDesignInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
41/100
Will not run
References files that are not bundled: ../data-scientist/SKILL.md, ../data-engineering/SKILL.md, ../ml-engineering/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
  • 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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../data-scientist/SKILL.md
  • warning missing-ref reference to a missing file: ../data-engineering/SKILL.md
  • warning missing-ref reference to a missing file: ../ml-engineering/SKILL.md
  • warning missing-ref reference to a missing file: ../product-design-and-ux/SKILL.md

Process rating: all ten parameters 41/100

Will not run. References files that are not bundled: ../data-scientist/SKILL.md, ../data-engineering/SKILL.md, ../ml-engineering/SKILL.md
  • 0Tools and files. 4 referenced file(s) missing: ../data-scientist/SKILL.md, ../data-engineering/SKILL.md, ../ml-engineering/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 50When it triggers. No condition that starts the skill
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1186 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low No test case covers injection arriving through data

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
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 488: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 11 items
  • +4Reference files are cited in the instructions (5 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.