SKILLEMALL.ai
GitHub ↗ RU Sign in

AA sales-enablement

Create sales collateral such as decks, one-pagers, objection docs, demo scripts, playbooks, and proposal templates. Use when a sales team needs assets that help reps move deals forward and close.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 6 files body ≈ 3 512 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Create sales collateral such as decks, one-pagers, objection docs, demo scripts, playbooks, and proposal templates.

As a process A 81/100 · Runs to the end — weak spots: when it triggers, running it twice

GeneratorData and analyticsWriting and documentsMarketingtype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
A
81/100
Runs to the end
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

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

How to improve

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security references/demo-scripts.md:216
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Penetration testing cadence

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 81/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 85Steps. 123 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Result and completion. Output format and completion criterion are stated
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3512 tokens
    • 100Progress reporting. Reports progress
    • low 16 top-level sections: this looks like several domains in one skill
    • 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 195: enough signal without eating the budget
    • +4Structure: 47 headings
    • +3Step-by-step instructions: 123 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (4 of 4)

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