Has Selector
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing stylesheets or components that use JavaScript to toggle classes on parent elements based on child state, form input values, or content presence — :has() may…
Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance
AGPL-3.0Auto-check passed
Install Tom Task Selector
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selector --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .claude/skills/tom-task-selector && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .claude/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selectorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selector --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .agents/skills/tom-task-selector && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .agents/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selector --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .cursor/skills/tom-task-selector && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .cursor/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NeuroAIHub/BrainPilot.git --path packages/skills/skills/12_Social_Cognition/tom-task-selector--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selector --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .gemini/skills/tom-task-selector && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .gemini/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selectorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .github/skills/tom-task-selector && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .github/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NeuroAIHub/BrainPilot tom-task-selector --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/packages/skills/skills/12_Social_Cognition/tom-task-selector .opencode/skills/tom-task-selector && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tom-task-selector" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector into .opencode/skills/tom-task-selector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tom-task-selector", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
tom-task-selector- GitHub stars
- 1.1k
- Token cost
- ~5.7k tokens
- SKILL.md length
- 2,419 words
- Files
- 2 (incl. references)
- Skills in repo
- 59
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance
- Works in 5 steps: State the research question -- What… → Justify the method choice -- Why is this… → Declare expected outcomes -- What… → …
- SKILL.md covers Purpose, When to Use This Skill, Research Planning Protocol and ⚠️ Verification Notice, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Tom Task Selector is an agent skill from NeuroAIHub/BrainPilot. Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance
Its SKILL.md is about 5.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/task-database.md`).
The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.
Example prompts
- “Use the tom-task-selector skill to select Theory of Mind tasks matched to target population, age, and construct with psychometric guidance”
- “/tom-task-selector”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- State the research question -- What specific question is this analysis/paradigm addressing?
- Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
- Declare expected outcomes -- What results would support vs. refute the hypothesis?
- Note assumptions and limitations -- What does this method assume? Where could it mislead?
- Present the plan to the user and WAIT for confirmation before proceeding.
What it can do on your machine
Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tom Task Selector loads about 5.7k tokens when it runs, and up to ~9k if it reads all its reference files. Until then it costs about 31 tokens; SKILL.md has 2,419 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 2,419 words, ~5,659 tokens.
Download SKILL.mdSave it as
.claude/skills/tom-task-selector/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- tom-task-selector
- description
- Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance
- domain
- social-cognition
- version
- 1.0.0
- papers
- Wimmer & Perner, 1983, Baron-Cohen et al., 2001, Wellman & Liu, 2004, Happe, 1994
- dependencies.required
- research-literacy
- review_status
- ai-generated
Theory of Mind Task Selector
Purpose
This skill encodes expert knowledge for selecting, administering, and interpreting Theory of Mind (ToM) assessments. It provides a construct taxonomy, task selection decision trees, age-appropriate recommendations, psychometric properties, and guidance on confounds. A general-purpose programmer would not know which ToM tasks are appropriate for which populations, the developmental sequence of ToM abilities, or the psychometric limitations of common measures.
When to Use This Skill
- Selecting a ToM measure for a developmental, clinical, or adult study
- Matching a ToM task to the target population (children, adults, ASD, brain injury, aging)
- Designing a comprehensive ToM assessment battery
- Evaluating the psychometric properties of a proposed ToM measure
- Identifying confounds (language, executive function, IQ) that may affect ToM task performance
- Interpreting ceiling/floor effects in ToM data
Research Planning Protocol
Before executing the domain-specific steps below, you MUST:
- State the research question -- What specific question is this analysis/paradigm addressing?
- Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
- Declare expected outcomes -- What results would support vs. refute the hypothesis?
- Note assumptions and limitations -- What does this method assume? Where could it mislead?
- Present the plan to the user and WAIT for confirmation before proceeding.
For detailed methodology guidance, see the research-literacy skill.
⚠️ Verification Notice
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.
ToM Construct Taxonomy
Developmental Hierarchy
ToM develops in a predictable sequence (Wellman & Liu, 2004). Tasks should be matched to the expected level:
| Level | Construct | Age of Emergence | Key Task | Source |
|---|---|---|---|---|
| 1 | Diverse desires | ~3 years | Diverse desires task | Wellman & Liu, 2004 |
| 2 | Diverse beliefs | ~3-4 years | Diverse beliefs task | Wellman & Liu, 2004 |
| 3 | Knowledge access | ~4 years | Knowledge access task | Wellman & Liu, 2004 |
| 4 | First-order false belief | ~4-5 years | Sally-Anne (Wimmer & Perner, 1983) | Wellman et al., 2001 |
| 5 | Hidden emotion | ~5-6 years | Appearance-reality emotion task | Wellman & Liu, 2004 |
| 6 | Second-order false belief | ~6-7 years | Ice-cream van task | Perner & Wimmer, 1985 |
| 7 | Faux pas recognition | ~9-11 years | Faux pas stories | Baron-Cohen et al., 1999 |
| 8 | Advanced/adult ToM | Adolescence-adult | Strange Stories, RMET | Happe, 1994; Baron-Cohen et al., 2001 |
Construct Dimensions
| Dimension | Description | Example Tasks |
|---|---|---|
| Belief attribution | Understanding others' beliefs, especially false beliefs | Sally-Anne, unexpected contents |
| Desire attribution | Understanding others' desires differ from one's own | Diverse desires task |
| Intention attribution | Understanding goal-directed action and intentionality | Intentional vs. accidental actions |
| Emotion attribution | Understanding others' emotions from context/cues | Hidden emotion, RMET |
| Visual perspective-taking | Level 1: what others see; Level 2: how others see it | Director task, Flavell tasks |
| Implicit/spontaneous ToM | Automatic, non-verbal ToM processing | Anticipatory looking, VoE paradigms |
Task Selection Decision Tree
By Age Group
What is the participant's age?
|
+-- Infants (6-24 months)
| --> Implicit ToM tasks only
| --> Anticipatory looking (Southgate et al., 2007)
| --> Violation-of-expectation (Onishi & Baillargeon, 2005)
|
+-- Preschoolers (3-5 years)
| --> Wellman & Liu (2004) scale (5 tasks)
| --> Sally-Anne / Change of location (Wimmer & Perner, 1983)
| --> Unexpected contents / Smarties task (Gopnik & Astington, 1988)
|
+-- School-age (6-12 years)
| --> Second-order false belief (Perner & Wimmer, 1985)
| --> Faux pas stories (Baron-Cohen et al., 1999)
| --> Strange Stories (Happe, 1994) -- simplified versions
|
+-- Adolescents and Adults
--> Strange Stories (Happe, 1994)
--> RMET (Baron-Cohen et al., 2001)
--> Director task (Keysar et al., 2003)
--> Faux pas test (Baron-Cohen et al., 1999)
--> Movie for the Assessment of Social Cognition (MASC; Dziobek et al., 2006)By Population
What is the target population?
|
+-- Typically developing children
| --> Wellman & Liu (2004) scale (most validated)
| --> Standard false belief tasks
|
+-- Autism spectrum (children)
| --> Sally-Anne (Baron-Cohen et al., 1985)
| --> Unexpected contents (Perner et al., 1989)
| --> Happe Strange Stories (if verbal)
| |
| NOTE: Many autistic individuals pass standard false
| belief tasks by age 6-8. Use advanced tasks to
| avoid ceiling effects (Happe, 1994).
|
+-- Autism spectrum (adults)
| --> RMET (Baron-Cohen et al., 2001)
| --> Faux pas test (Baron-Cohen et al., 1999)
| --> MASC (Dziobek et al., 2006)
| --> Director task (Keysar et al., 2003)
|
+-- Brain injury / neurological
| --> Faux pas test (Stone et al., 1998)
| --> Strange Stories (Happe, 1994)
| --> RMET (Baron-Cohen et al., 2001)
| --> Yoni task (Shamay-Tsoory & Aharon-Peretz, 2007)
|
+-- Aging / dementia
--> Faux pas test (Gregory et al., 2002)
--> RMET (Baron-Cohen et al., 2001)
--> Strange Stories (Happe, 1994)
--> Note: control for processing speed and working memoryBy Construct
What ToM construct are you targeting?
|
+-- Belief attribution
| --> False belief tasks (Sally-Anne, unexpected contents)
| --> Second-order false belief
|
+-- Emotion recognition
| --> RMET (Baron-Cohen et al., 2001)
| --> Cambridge Mindreading Face-Voice Battery
|
+-- Social reasoning / pragmatics
| --> Faux pas test
| --> Strange Stories
|
+-- Visual perspective-taking
| --> Director task (Keysar et al., 2003)
| --> Flavell Level 1/2 tasks
|
+-- Implicit / spontaneous ToM
--> Anticipatory looking paradigms
--> Dot-perspective task (Samson et al., 2010)Key Tasks with Parameters
First-Order False Belief: Sally-Anne Task
| Property | Value | Source |
|---|---|---|
| Original citation | Wimmer & Perner, 1983; Baron-Cohen et al., 1985 | |
| Age range | 3-6 years (standard); used in ASD at any age | Wellman et al., 2001 |
| Administration | Acted out with dolls/puppets or illustrated story | Baron-Cohen et al., 1985 |
| Test question | "Where will Sally look for her marble?" | |
| Control questions | Reality question + memory question (must pass both) | Baron-Cohen et al., 1985 |
| Scoring | Pass/fail (binary) | |
| Passing criterion | Correct test question + both control questions | Baron-Cohen et al., 1985 |
| Typical passing rates | ~20% at 3 years, ~50% at 4 years, ~90% by 5-6 years | Wellman et al., 2001 |
| Limitations | Ceiling by age 6; binary scoring limits sensitivity | Wellman et al., 2001 |
Unexpected Contents (Smarties Task)
| Property | Value | Source |
|---|---|---|
| Original citation | Gopnik & Astington, 1988; Perner et al., 1987 | |
| Age range | 3-6 years | Gopnik & Astington, 1988 |
| Administration | Show container (e.g., Smarties box) with unexpected contents (e.g., pencils) | |
| Test question | "What will [name] think is in the box?" (other's belief) | |
| Self question | "What did you think was in the box before I opened it?" (own prior belief) | |
| Scoring | Pass: predicts other will say "Smarties" (or typical contents) |
Second-Order False Belief
| Property | Value | Source |
|---|---|---|
| Original citation | Perner & Wimmer, 1985 | |
| Age range | 6-9 years | Perner & Wimmer, 1985 |
| Construct | "She thinks that he thinks that..." | |
| Administration | Story scenario (ice-cream van paradigm) | Perner & Wimmer, 1985 |
| Test question | "Where does Mary think John will go to buy ice cream?" | |
| Passing rates | ~10% at 5 years, ~50% at 7 years, ~90% by 9 years | Perner & Wimmer, 1985 |
| Comprehension questions | 2-3 memory/comprehension checks required | Standard practice |
RMET (Reading the Mind in the Eyes Test)
| Property | Value | Source |
|---|---|---|
| Original citation | Baron-Cohen et al., 2001 | |
| Version | Revised version (2001) -- 36 items | Baron-Cohen et al., 2001 |
| Age range | Adults (16+ years); child version available (28 items) | Baron-Cohen et al., 2001 |
| Administration | Forced-choice: pick 1 of 4 mental state words matching eye region photo | |
| Scoring | Total correct out of 36 (adults) or 28 (children) | |
| Adult norms | Mean ~ 26.2 (SD ~ 3.6) in typical adults | Baron-Cohen et al., 2001 |
| ASD norms | Mean ~ 21.9 (SD ~ 6.6) in autistic adults | Baron-Cohen et al., 2001 |
| Reliability | Internal consistency: Cronbach's alpha ~ 0.60-0.70 (modest) | Olderbak et al., 2015 |
| Limitations | Low reliability, possible confound with emotion recognition vs. ToM per se | Olderbak et al., 2015 |
Faux Pas Test
| Property | Value | Source |
|---|---|---|
| Original citation | Baron-Cohen et al., 1999 | |
| Age range | 9 years to adult | Baron-Cohen et al., 1999 |
| Administration | Read 10 faux pas stories + 10 control stories | |
| Questions per story | Detection ("Did someone say something awkward?"), identification, belief, empathy | Baron-Cohen et al., 1999 |
| Scoring | 0-2 points per question; max 60 for faux pas stories | Baron-Cohen et al., 1999 |
| Control stories | Must also score comprehension questions for controls | |
| Sensitivity | Good for detecting subtle ToM deficits in ASD, right hemisphere lesions, frontotemporal dementia | Stone et al., 1998; Gregory et al., 2002 |
Strange Stories (Happe, 1994)
| Property | Value | Source |
|---|---|---|
| Original citation | Happe, 1994 | |
| Construct | Advanced ToM: irony, white lie, double bluff, misunderstanding, persuasion, appearance/reality, figure of speech, sarcasm, forgetting, contrary emotions | |
| Administration | Read vignettes, open-ended question: "Why did X say that?" | |
| Scoring | 0 (incorrect), 1 (partial), 2 (full mental state reference) | Happe, 1994 |
| Number of stories | 8-16 ToM stories + physical control stories | Happe, 1994 |
| Age range | Children (8+) and adults | Happe, 1994 |
| Reliability | Inter-rater reliability for scoring: kappa > 0.85 recommended | Happe, 1994 |
Director Task (Visual Perspective-Taking)
| Property | Value | Source |
|---|---|---|
| Original citation | Keysar et al., 2003 | |
| Construct | Level 2 perspective-taking under communicative demand | |
| Administration | Grid of objects; director (behind grid) instructs participant to move objects; some slots occluded from director's view | |
| Measure | Eye movements (egocentric intrusions), accuracy, RT | Keysar et al., 2003 |
| Key finding | Even adults show egocentric errors on ~30-50% of critical trials | Keysar et al., 2003 |
| Age range | 7 years to adult | Dumontheil et al., 2010 |
See references/task-database.md for the full task list with administration protocols.
Psychometric Considerations
Reliability Summary
| Task | Internal Consistency | Test-Retest | Source |
|---|---|---|---|
| Sally-Anne (single item) | N/A (binary) | Variable | Wellman et al., 2001 |
| Wellman & Liu Scale | Guttman scalability > 0.90 | Moderate | Wellman & Liu, 2004 |
| RMET | alpha ~ 0.60-0.70 | r ~ 0.63-0.83 | Olderbak et al., 2015; Fernandez-Abascal et al., 2013 |
| Faux pas test | alpha ~ 0.70-0.80 | Not well-established | Baron-Cohen et al., 1999 |
| Strange Stories | Inter-rater: kappa > 0.85 | Moderate | Happe, 1994 |
| MASC | alpha ~ 0.70 | Adequate | Dziobek et al., 2006 |
Validity Concerns
- Ceiling effects: Standard false belief tasks show ceiling by age 5-6 in typical children. Use Wellman & Liu scale or advanced tasks (Wellman & Liu, 2004).
- Floor effects: RMET and faux pas tests may show floor effects in clinical populations with severe deficits. Consider graded scoring.
- Ecological validity: Structured ToM tasks may not predict real-world social behavior (German & Hehman, 2006).
- Task purity: No ToM task measures only ToM. All tasks involve language, memory, executive function, and attention.
Confounds and Controls
Language
| Confound | Impact | Mitigation | Source |
|---|---|---|---|
| Verbal demands | False belief tasks require comprehension of complex sentences | Include vocabulary/language control measure | Milligan et al., 2007 |
| Narrative complexity | Second-order tasks have heavy memory load | Add comprehension check questions | Perner & Wimmer, 1985 |
| Word knowledge (RMET) | Vocabulary confound in forced-choice emotion labels | Control for verbal IQ | Olderbak et al., 2015 |
Executive Function
| Confound | Impact | Mitigation | Source |
|---|---|---|---|
| Inhibitory control | Must inhibit own knowledge to attribute false belief | Include inhibition measure (e.g., Stroop, day-night) | Carlson & Moses, 2001 |
| Working memory | Must hold multiple perspectives simultaneously | Control for WM span | Carlson & Moses, 2001 |
| Cognitive flexibility | Must switch between self and other perspective | Include set-shifting measure | Carlson & Moses, 2001 |
Recommended Control Measures
For any ToM study, include at minimum:
- Verbal ability: Receptive vocabulary (e.g., PPVT) or verbal IQ subscale
- Inhibitory control: Age-appropriate inhibition task
- Working memory: Forward/backward digit span or equivalent
- Non-ToM comprehension: Physical causality control stories (for Strange Stories and faux pas)
Task Combination Recommendations
Show full SKILL.md (1,012 more words)Show less
Comprehensive Battery by Population
| Population | Recommended Battery | Rationale |
|---|---|---|
| Preschool (3-5y) | Wellman & Liu Scale (5 tasks) + diverse desires + diverse beliefs | Guttman-scalable, captures developmental progression (Wellman & Liu, 2004) |
| School-age (6-12y) | First-order FB + second-order FB + faux pas + Strange Stories subset | Spans implicit to advanced ToM |
| ASD (children) | Sally-Anne + unexpected contents + Strange Stories (simplified) | Avoids ceiling; includes advanced items |
| ASD (adults) | RMET + faux pas + MASC + Director task | Multiple constructs; includes real-time and reflective tasks |
| Neurological (adults) | Faux pas + Strange Stories + RMET | Sensitive to frontal and right hemisphere lesions (Stone et al., 1998) |
| Aging research | Faux pas + RMET + Strange Stories | Control for processing speed; established aging norms |
Minimum Battery (2-3 tasks)
If time is limited, prioritize:
- One false belief task (for belief attribution)
- Faux pas or Strange Stories (for advanced ToM / social reasoning)
- RMET (for emotion/mental state recognition -- if construct-relevant)
Common Pitfalls
- Using a single task as the sole ToM measure: ToM is multidimensional. Single tasks have low reliability and capture only one construct. Use a battery (Wellman & Liu, 2004).
- Ignoring ceiling/floor effects: Standard false belief tasks ceiling by age 5-6. The RMET has modest reliability. Check for restricted range.
- Not controlling for language: Most ToM tasks have substantial verbal demands. Group differences in ToM may reflect language differences, especially in ASD (Milligan et al., 2007).
- Confounding ToM with executive function: False belief tasks require inhibitory control. Include EF measures and control statistically or use low-EF-demand tasks (Carlson & Moses, 2001).
- Age-inappropriate task selection: Giving first-order false belief to adults (ceiling) or faux pas to 4-year-olds (floor). Match task to developmental level.
- Treating the RMET as a pure ToM measure: The RMET has low reliability (alpha ~ 0.60-0.70) and may measure emotion recognition more than mental state inference (Olderbak et al., 2015).
- Assuming failed performance = absent ToM: Implicit/anticipatory looking studies suggest infants may have ToM understanding that explicit tasks fail to capture (Onishi & Baillargeon, 2005). Distinguish competence from performance.
- Not including control stories: For faux pas and Strange Stories, physical/non-mental-state control stories are essential to rule out general comprehension deficits.
Minimum Reporting Checklist
- ToM construct(s) targeted (belief, desire, emotion, perspective-taking)
- Task(s) used with full citation and version
- Administration method (live, video, computerized)
- Scoring criteria and inter-rater reliability (for open-ended tasks)
- Control questions included and pass rates
- Confound measures included (language, EF, IQ)
- Ceiling/floor analysis: report distribution of scores, not just means
- Age and developmental level of participants
- Clinical classification criteria (if clinical population)
- Effect sizes and confidence intervals for group comparisons
References
- Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a "theory of mind"? Cognition, 21(1), 37-46.
- Baron-Cohen, S., O'Riordan, M., Stone, V., Jones, R., & Plaisted, K. (1999). Recognition of faux pas by normally developing children and children with Asperger syndrome or high-functioning autism. Journal of Autism and Developmental Disorders, 29(5), 407-418.
- Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The "Reading the Mind in the Eyes" test revised version. Journal of Child Psychology and Psychiatry, 42(2), 241-251.
- Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children's theory of mind. Child Development, 72(4), 1032-1053.
- Dumontheil, I., Apperly, I. A., & Blakemore, S. J. (2010). Online usage of theory of mind continues to develop in late adolescence. Developmental Science, 13(2), 331-338.
- Dziobek, I., Fleck, S., Kalbe, E., Rogers, K., Hassenstab, J., Brand, M., ... & Convit, A. (2006). Introducing MASC: A movie for the assessment of social cognition. Journal of Autism and Developmental Disorders, 36(5), 623-636.
- Fernandez-Abascal, E. G., Cabello, R., Fernandez-Berrocal, P., & Baron-Cohen, S. (2013). Test-retest reliability of the "Reading the Mind in the Eyes" test. Journal of Autism and Developmental Disorders, 43(9), 2220-2223.
- German, T. P., & Hehman, J. A. (2006). Representational and executive selection resources in "theory of mind." Psychological Science, 17(2), 130-132.
- Gopnik, A., & Astington, J. W. (1988). Children's understanding of representational change and its relation to the understanding of false belief. Child Development, 59(1), 26-37.
- Gregory, C., Lough, S., Stone, V., Erzinclioglu, S., Martin, L., Baron-Cohen, S., & Hodges, J. R. (2002). Theory of mind in patients with frontal variant frontotemporal dementia and Alzheimer's disease. Journal of Neurology, Neurosurgery & Psychiatry, 72(6), 752-756.
- Happe, F. G. (1994). An advanced test of theory of mind. Journal of Autism and Developmental Disorders, 24(2), 129-154.
- Keysar, B., Lin, S., & Barr, D. J. (2003). Limits on theory of mind use in adults. Cognition, 89(1), 25-41.
- Milligan, K., Astington, J. W., & Dack, L. A. (2007). Language and theory of mind: Meta-analysis of the relation between language ability and false-belief understanding. Child Development, 78(2), 622-646.
- Olderbak, S., Wilhelm, O., Olaru, G., Geiger, M., Brenneman, M. W., & Roberts, R. D. (2015). A psychometric analysis of the Reading the Mind in the Eyes test. Assessment, 22(6), 798-806.
- Onishi, K. H., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308(5719), 255-258.
- Perner, J., Leekam, S. R., & Wimmer, H. (1987). Three-year-olds' difficulty with false belief. British Journal of Developmental Psychology, 5(2), 125-137.
- Perner, J., & Wimmer, H. (1985). "John thinks that Mary thinks that..." Attribution of second-order beliefs. Journal of Experimental Child Psychology, 39(3), 437-471.
- Samson, D., Apperly, I. A., Braithwaite, J. J., Andrews, B. J., & Bodley Scott, S. E. (2010). Seeing it their way: Evidence for rapid and involuntary computation of what other people see. Journal of Experimental Psychology: HPP, 36(5), 1255-1266.
- Shamay-Tsoory, S. G., & Aharon-Peretz, J. (2007). Dissociable prefrontal networks for cognitive and affective theory of mind. Neuropsychologia, 45(13), 3054-3067.
- Southgate, V., Senju, A., & Csibra, G. (2007). Action anticipation through attribution of false belief by 2-year-olds. Psychological Science, 18(7), 587-592.
- Stone, V. E., Baron-Cohen, S., & Knight, R. T. (1998). Frontal lobe contributions to theory of mind. Journal of Cognitive Neuroscience, 10(5), 640-656.
- Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory-of-mind development: The truth about false belief. Child Development, 72(3), 655-684.
- Wellman, H. M., & Liu, D. (2004). Scaling of theory-of-mind tasks. Child Development, 75(2), 523-541.
- Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception. Cognition, 13(1), 103-128.
See references/ for the full task database with administration protocols and scoring rubrics.
© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (references) in packages/skills/skills/12_Social_Cognition/tom-task-selector of NeuroAIHub/BrainPilot.
- SKILL.md
- references/task-database.md
Open the folder on GitHubat commit 93f6855
Compare with similar skills
Tom Task Selector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tom Task Selector this skillNeuroAIHub/BrainPilot | 1.1k | — | ~5.7k | Automated safety check: Pass | AGPL-3.0 | |
| Has Selectorthedaviddias/Front-End-Checklist | 74k | — | ~547 | Automated safety check: Pass | MIT | |
| Select Namethedaviddias/Front-End-Checklist | 74k | — | ~451 | Automated safety check: Pass | MIT | |
| Add Selectorsimstudioai/sim | 30k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Validate Selectorsimstudioai/sim | 30k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Bio Population Genetics Selection StatisticsGPTomics/bioSkills | 1.2k | 1 repos | ~5.6k | Automated safety check: Pass | MIT |
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Questions about Tom Task Selector
What does Tom Task Selector do?
Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance. Tom Task Selector is an agent skill from NeuroAIHub/BrainPilot.
How do I install Tom Task Selector in Claude Code?
Run `npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a claude-code`. Or copy the skill folder (packages/skills/skills/12_Social_Cognition/tom-task-selector in NeuroAIHub/BrainPilot) into .claude/skills/tom-task-selector in your project. Claude Code loads it when a task matches its description.
How do I install Tom Task Selector in Codex?
Run `npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a codex`. Or copy the skill folder (packages/skills/skills/12_Social_Cognition/tom-task-selector in NeuroAIHub/BrainPilot) into .agents/skills/tom-task-selector in your project. Codex loads it when a task matches its description.
Can I use Tom Task Selector in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeuroAIHub/BrainPilot --skill tom-task-selector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tom-task-selector, .gemini/skills/tom-task-selector, .github/skills/tom-task-selector and .opencode/skills/tom-task-selector in your project.
What does Tom Task Selector need to run?
SKILL.md names no scripts, command-line tools or credentials: Tom Task Selector is instructions for the agent only.
Does Tom Task Selector access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Tom Task Selector safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Tom Task Selector use?
Tom Task Selector is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tom Task Selector use?
About 5.7k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.3k tokens, read only when the agent opens those files.
What are the alternatives to Tom Task Selector?
Skills that share tags, products or a category with Tom Task Selector: Has Selector (thedaviddias/Front-End-Checklist, 74k stars), Select Name (thedaviddias/Front-End-Checklist, 74k stars), Add Selector (simstudioai/sim, 30k stars) and Validate Selector (simstudioai/sim, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tom Task Selector?
NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.
Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.