SKILLEMALL.ai
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BB sales-analytics

Use this skill when the user wants to analyze sales pipeline, review win rates, investigate sales velocity or cycle length, build a sales report, evaluate rep performance, look at lead source quality, or answer questions about their CRM data. Triggers include 'how is the pipeline', 'win rate by segment', 'sales cycle', 'pipeline coverage', 'rep performance', 'forecast accuracy', 'why are deals stalling', 'lead source quality', 'stage conversion', 'pull my Salesforce numbers', 'HubSpot deals report', 'sales report'.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 486 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use this skill when the user wants to analyze sales pipeline, review win rates, investigate sales velocity or cycle length, build a sales report, evaluate rep…

As a process B 76/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureSalesforceHubSpotData and analyticsInfrastructuretype 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%
100
Quality 40%
84
Run on models
none yet
Process rating
B
76/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Running it twice w 4
30
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 · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 60): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 76/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 17 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 41 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3486 tokens
    • 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 520: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 41 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.