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BC mlb-trade-evaluator

Computes the full impact of a proposed MLB fantasy trade across all 10 H2H categories (R/HR/RBI/SB/OBP, K/ERA/WHIP/QS/SV), rest-of-season dollar value, positional flexibility, slot-value optionality, adverse-selection prior, and weeks 21-23 playoff impact. Produces a signed verdict (accept / counter / reject) with rationale and a specific counter if applicable. Use when user mentions "trade evaluation", "trade value", "should I accept", "trade delta", "counter offer", or pastes in a trade proposal from Yahoo. Defaults to COUNTER in the middle band — pure REJECT is reserved for clearly predatory offers.

lyndonkl/claude Agent Skills author: lyndonkl 4 files body ≈ 5 271 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Computes the full impact of a proposed MLB fantasy trade across all 10 H2H categories (R/HR/RBI/SB/OBP, K/ERA/WHIP/QS/SV), rest-of-season dollar value…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureData and analyticsResearchtype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5271 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5271 tokens
  • 100Steps. 100 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (4 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 609: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 100 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.