SKILLEMALL.ai

BF elicitation

Psychological profiling through natural conversation using narrative identity research (McAdams), self-defining memory elicitation (Singer), and Motivational Interviewing (OARS framework). Use when you need to: (1) understand someone's core values and motivations, (2) discover formative memories and life-defining experiences, (3) detect emotional schemas and belief patterns, (4) build psychological profiles through gradual disclosure, (5) conduct user interviews that reveal deep insights, (6) design conversational flows for personal discovery, (7) identify identity themes like redemption and contamination narratives, (8) elicit authentic self-disclosure without interrogation.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 4 359 tokens Open the sourcegithub.com analyzed 2 d ago

Psychological profiling through natural conversation using narrative identity research (McAdams), self-defining memory elicitation (Singer), and Motivational…

As a process F 34/100 · Will not run — References files that are not bundled: elicitation/self-defining-memories.md, elicitation/narrative-identity.md, elicitation/motivational-interviewing.md

AnalyzerAI and agentsSoftware developmentSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
F
34/100
Will not run
References files that are not bundled: elicitation/self-defining-memories.md, elicitation/narrative-identity.md, elicitation/motivational-interviewing.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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

  • warning missing-ref reference to a missing file: elicitation/self-defining-memories.md
  • warning missing-ref reference to a missing file: elicitation/narrative-identity.md
  • warning missing-ref reference to a missing file: elicitation/motivational-interviewing.md
  • warning missing-ref reference to a missing file: elicitation/values-elicitation.md
  • warning missing-ref reference to a missing file: elicitation/schema-detection.md
  • warning missing-ref reference to a missing file: elicitation/question-sequences.md
  • warning missing-ref reference to a missing file: elicitation/language-inference.md

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: elicitation/self-defining-memories.md, elicitation/narrative-identity.md, elicitation/motivational-interviewing.md
  • 0Tools and files. 7 referenced file(s) missing: elicitation/self-defining-memories.md, elicitation/narrative-identity.md, elicitation/motivational-interviewing.md
  • 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
  • 30Running it twice. 7 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 4359 tokens
  • 100Steps. 102 steps
  • 100Consistency. Name and required fields are in place
  • low 15 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)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 684: enough signal without eating the budget
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 102 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.