BC boss-ai-agent
Boss AI Agent — AI management advisor and team operations middleware. Use this skill whenever the user needs management advice, leadership guidance, or team operations help. Triggers for: 1:1 meeting prep, daily briefings ('what's important today'), team performance reviews (advice and analysis, not templates), risk assessments, KPI health checks, check-in question design, conflict resolution, cross-cultural feedback ('how do I give feedback to my Filipino/Chinese/Indonesian employee'), mentor philosophy application ('what would Musk/Inamori/Ma say'), C-Suite board simulation, promotion/hiring decisions, employee engagement issues, weekly reports, and incentive reviews. Supports 16 mentor philosophies (Musk, Inamori, Ma, Dalio, Grove, Bezos, etc.), 9 culture packs, and learns boss preferences over time. Works offline as advisor or connected to manageaibrain.com MCP for full 33-tool automation (check-ins, tracking, messaging, sync). Use this even if the user doesn't say 'management' explicitly — any people leadership question, team dynamics issue, or boss-level decision qualifies. Do NOT trigger for software development tasks (building apps, APIs, bots, schemas) even if they relate to HR/employees — this skill is for management advice, not code implementation.
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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: 19. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1279 chars, limit 1024 - note
frontmatter-keyunknown frontmatter key "title" - note
frontmatter-keyunknown frontmatter key "emoji" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4401 tokens
- 100Steps. 69 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
- medium 3 test cases, all positive: not one "should refuse" or "should ask first"
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 1279: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 31 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.