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BB mlb-playoff-scheduler

Counts MLB games per team during the Yahoo fantasy playoff window (weeks 21, 22, 23 -- Aug 17 through Sep 6, 2026) and grades the quality of each team's opponents. Emits three signals per rostered player -- playoff_games (int, max ~21), playoff_matchup_quality (0-100), holding_value (0-100) -- that drive trade-deadline and playoff-lineup decisions. Use when the user mentions playoff weeks, weeks 21-23, playoff schedule, game count, holding value, or asks whether to keep/trade a player for the playoff run. Pre-July 1 this skill returns "insufficient signal -- too early"; from July 1 onward it fires weekly.

lyndonkl/claude Agent Skills author: lyndonkl 4 files body ≈ 2 976 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Counts MLB games per team during the Yahoo fantasy playoff window (weeks 21, 22, 23 -- Aug 17 through Sep 6, 2026) and grades the quality of each team's…

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions

ProcedureResearchPersonal productivityData and analyticstype 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
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    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 Risky intent intent-offensive-security resources/methodology.md:237
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      2. **Flag red team**: Add a finding listing the missing data and its likely magnitude of impact.

    Files scanned: 4. 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 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) 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. 42 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2976 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 612: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 42 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.