BD knowledge-base-pro
> Corporate knowledge brain for Russian-speaking teams. Replaces HR onboarding department + internal communications team on 40-60% of routine. FAQ bot, role-based onboarding, SOP templates, smart search with filters, built-in gap analysis, knowledge audit. 14 modes in one skill. Works from day one — no external SaaS required. Battle-tested inside RAAI. Saves 10-25K RUB per new hire in onboarding costs. Russian language native. 5 proof cases with ruble ROI numbers. Корпоративный мозг компании — заменяет HR-онбординг + отдел внутренней коммуникации на 40-60% рутины. FAQ-бот, онбординг по ролям, регламенты/SOPs, умный поиск, gap-анализ пробелов знаний, аудит базы — 14 режимов в одном скилле. Работает сразу без внешнего SaaS (Guru, Botpress не нужны). Экономит 25К ₽+ на каждом новом сотруднике. 5 кейсов с рублёвыми цифрами. Русский язык нативно. Единственный KB-скилл для OpenClaw с полной RU-локализацией.
As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 35. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Not a YAML token: Corporate knowledge brain for Russian-speaking teams. Replaces HR onboarding department + internal communications team on 40-60% of routine. FAQ bot, role-based onboarding, SOP templates, smart search with filters, built-in gap analysis, knowledge audit. 14 modes in one skill. Works from day one — no external SaaS required. Battle-tested inside RAAI. Saves 10-25K RUB per new hire in onboarding costs. Russian language native. 5 proof cases with ruble ROI numbers. Корпоративный мозг компании — заменяет HR-онбординг + отдел внутренней коммуникации на 40-60% рутины. FAQ-бот, онбординг по ролям, регламенты/SOPs, умный поиск, gap-анализ пробелов знаний, аудит базы — 14 режимов в одном скилле. Работает сразу без внешнего SaaS (Guru, Botpress не нужны). Экономит 25К ₽+ на каждом новом сотруднике. 5 кейсов с рублёвыми цифрами. Русский язык нативно. Единственный KB-скилл для OpenClaw с полной RU-локализацией. at line 5, column 16: description: > Corporate knowledge brain for Russian-speaking teams. Replaces H… ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 13981 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "triggers" - note
frontmatter-keyunknown frontmatter key "target_audience" - note
frontmatter-keyunknown frontmatter key "price" - note
frontmatter-keyunknown frontmatter key "price_currency" - note
frontmatter-keyunknown frontmatter key "dogfooded_in" - note
frontmatter-keyunknown frontmatter key "differentiators"
Process rating: all ten parameters 45/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (knowledge-base-pro) differs from the folder (raai-knowledge-base-pro)
- 40Execution cost. Instruction body is 13981 tokens: crowds the task out of the window
- 100Tools and files. No external tools needed
- 100Steps. 48 steps
- 100Running it twice. No mutating operations
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 914: 120–800 characters recommended
- +4Structure: 0 headings, hard to scan
- +3Output format is not stated: the model decides each time
- -5Long text without headings
- +3Step-by-step instructions: 48 items
- +4Has examples (23 code blocks)
- +4Reference files are cited in the instructions (1 of 3)
- +1License stated
- +2Bilingual instructions (RU + EN)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 44.