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BC dispositional-knowledge-assessment-designer

Design multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.

GarethManning/education-agent-skills Claude Code author: GarethManning 3 files body ≈ 12 676 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Design multi-informant assessment approaches for dispositional competencies like curiosity or resilience.

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerSoftware developmenttype 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
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 12676 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "effort"
  • note frontmatter-key unknown frontmatter key "skill_id"
  • note frontmatter-key unknown frontmatter key "skill_name"
  • note frontmatter-key unknown frontmatter key "domain"
  • note frontmatter-key unknown frontmatter key "evidence_strength"
  • note frontmatter-key unknown frontmatter key "evidence_sources"
  • note frontmatter-key unknown frontmatter key "input_schema"
  • note frontmatter-key unknown frontmatter key "output_schema"
  • note frontmatter-key unknown frontmatter key "chains_well_with"
  • note frontmatter-key unknown frontmatter key "teacher_time"

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Execution cost. Instruction body is 12676 tokens: crowds the task out of the window
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 195: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.