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AB sprint-velocity-analysis

Analyze sprint velocity data and produce an engineering team health report covering delivery trends, capacity utilization, and improvement recommendations. Use when asked to analyze sprint velocity, review team delivery health, identify delivery risks, or produce a retrospective data analysis. Produces a velocity trend analysis, health diagnosis table, top improvement recommendations with implementation steps, and a next-sprint capacity forecast.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 3 242 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze sprint velocity data and produce an engineering team health report covering delivery trends, capacity utilization, and improvement recommendations.

As a process B 78/100 · Nearly there — weak spots: when it triggers, running it twice

AnalyzerOperations and projectstype 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
78/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Failures and branches w 10
55
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: sprint-velocity-analysis (mohitagw15856/pm-claude-skills)

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 · 0

    ✓ No critical or high findings

    Files scanned: 1. 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 78/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 18 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 28 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3242 tokens
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill

    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 450: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

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