BC social-persona-profiling
Profile people from social-media traces — avatar, profile/cover background, nickname, privacy/visibility settings, chat behavior, shared content, self-reported labels — across platforms (WeChat, WhatsApp, Instagram, LinkedIn, Telegram, X) and cultures, and deliver an honest, evidence-weighted persona read, relationship analysis, or "what should I do next" guidance. Built for anyone sizing up a colleague, date, friend, or stranger from limited online signals. Capabilities: Big Five (OCEAN) trait estimation, self-presentation & self-monitoring (Goffman/Snyder), Higgins self-discrepancy, defense mechanisms (Vaillant), attachment and burnout signals, three-layer persona mapping (public persona / private self / self-reported), relationship-crisis attribution, and exploratory non-directive next-step discussion by scenario (workplace / dating / friendship / scam detection). Safeguards: three-tier confidence grading (objective fact / behavioral inference / working hypothesis), moderator adjustment for age, culture (individualist vs collectivist), platform, personality baseline and digital-native generation, projection-symmetry check, and a Barnum-effect filter that deletes any statement true of anyone.
Profile people from social-media traces — avatar, profile/cover background, nickname, privacy/visibility settings, chat behavior, shared content…
As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1214 chars, limit 1024 - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "description_en" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 61/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 4311 tokens
- 85Steps. 68 steps, 3 vague phrases
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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 1213: 120–800 characters recommended
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Structure: 13 headings
- +3Step-by-step instructions: 68 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.