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AA marketing-cialdini

Проверяет и усиливает маркетинг курса, коучинга, консалтинга или любой услуги по 7 принципам влияния Роберта Чалдини: взаимность, обязательство и последовательность, социальное доказательство, авторитет, симпатия, дефицит, единство. Режимы: аудит (оценка 0-3 по каждому принципу, красные флаги, правки было/стало), создание (текст или страница с нуля, где каждый принцип стоит на своем месте), дополнение (встроить недостающие принципы в готовый текст, не ломая голос автора), quick. Работает по контексту (лендинг, пост, письмо, вебинар, скрипт, папка проекта) или без него – по короткому описанию. Только честные рычаги: без фейкового дефицита и выдуманных отзывов. Use when the user wants to apply Cialdini's principles of persuasion/influence to a landing page, sales copy, launch, email, webinar or offer. Триггеры: 'Чалдини', 'Cialdini', 'принципы влияния', 'психология убеждения', 'проверь лендинг на убедительность', 'добавь социальное доказательство', 'усиль оффер', 'почему текст не продает', /marketing-cialdini.

alexivanovvv/turboskills Agent Skills author: alexivanovvv MIT 8 files body ≈ 4 548 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Проверяет и усиливает маркетинг курса, коучинга, консалтинга или любой услуги по 7 принципам влияния Роберта Чалдини: взаимность, обязательство и…

As a process A 82/100 · Runs to the end — weak spots: inputs and preconditions

AnalyzerWriting 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
A
82/100
Runs to the end
Inputs and preconditions w 11
30
Result and completion w 14
60
Failures and branches w 10
70
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: 8. 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 82/100

    • 30Inputs and preconditions. Does not say what the process needs to start
    • 60Result and completion. Output format stated, no completion criterion
    • 70Failures and branches. 7 branches
    • 70Execution cost. Instruction body is 4548 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 46 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 12 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)
    • +3Description length 1023: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +2Bilingual instructions (RU + EN)

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