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Tom Task Selector

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NeuroAIHub
tom-task-selector

Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance

Overview

PublisherNeuroAIHub
RepositoryBrainPilot
Skill nametom-task-selector
Stars
1.1K
Forks
74
Bundled files
1
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NeuroAIHub on GitHub. Read the source before you install it.

Installation

Install the Tom Task Selector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git /tmp/BrainPilot
mkdir -p .claude/skills
cp -r /tmp/BrainPilot/packages/skills/skills/12_Social_Cognition/tom-task-selector .claude/skills/tom-task-selector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tom Task Selector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tom Task Selector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tom Task Selector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  1. State the research question -- What specific question is this analysis/paradigm addressing?
  2. Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
  3. Declare expected outcomes -- What results would support vs. refute the hypothesis?
  4. Note assumptions and limitations -- What does this method assume? Where could it mislead?
  5. 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:

LevelConstructAge of EmergenceKey TaskSource
1Diverse desires~3 yearsDiverse desires taskWellman & Liu, 2004
2Diverse beliefs~3-4 yearsDiverse beliefs taskWellman & Liu, 2004
3Knowledge access~4 yearsKnowledge access taskWellman & Liu, 2004
4First-order false belief~4-5 yearsSally-Anne (Wimmer & Perner, 1983)Wellman et al., 2001
5Hidden emotion~5-6 yearsAppearance-reality emotion taskWellman & Liu, 2004
6Second-order false belief~6-7 yearsIce-cream van taskPerner & Wimmer, 1985
7Faux pas recognition~9-11 yearsFaux pas storiesBaron-Cohen et al., 1999
8Advanced/adult ToMAdolescence-adultStrange Stories, RMETHappe, 1994; Baron-Cohen et al., 2001

Construct Dimensions

DimensionDescriptionExample Tasks
Belief attributionUnderstanding others' beliefs, especially false beliefsSally-Anne, unexpected contents
Desire attributionUnderstanding others' desires differ from one's ownDiverse desires task
Intention attributionUnderstanding goal-directed action and intentionalityIntentional vs. accidental actions
Emotion attributionUnderstanding others' emotions from context/cuesHidden emotion, RMET
Visual perspective-takingLevel 1: what others see; Level 2: how others see itDirector task, Flavell tasks
Implicit/spontaneous ToMAutomatic, non-verbal ToM processingAnticipatory 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 memory

By 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

PropertyValueSource
Original citationWimmer & Perner, 1983; Baron-Cohen et al., 1985
Age range3-6 years (standard); used in ASD at any ageWellman et al., 2001
AdministrationActed out with dolls/puppets or illustrated storyBaron-Cohen et al., 1985
Test question"Where will Sally look for her marble?"
Control questionsReality question + memory question (must pass both)Baron-Cohen et al., 1985
ScoringPass/fail (binary)
Passing criterionCorrect test question + both control questionsBaron-Cohen et al., 1985
Typical passing rates~20% at 3 years, ~50% at 4 years, ~90% by 5-6 yearsWellman et al., 2001
LimitationsCeiling by age 6; binary scoring limits sensitivityWellman et al., 2001

Unexpected Contents (Smarties Task)

PropertyValueSource
Original citationGopnik & Astington, 1988; Perner et al., 1987
Age range3-6 yearsGopnik & Astington, 1988
AdministrationShow 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)
ScoringPass: predicts other will say "Smarties" (or typical contents)

Second-Order False Belief

PropertyValueSource
Original citationPerner & Wimmer, 1985
Age range6-9 yearsPerner & Wimmer, 1985
Construct"She thinks that he thinks that..."
AdministrationStory 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 yearsPerner & Wimmer, 1985
Comprehension questions2-3 memory/comprehension checks requiredStandard practice

RMET (Reading the Mind in the Eyes Test)

PropertyValueSource
Original citationBaron-Cohen et al., 2001
VersionRevised version (2001) -- 36 itemsBaron-Cohen et al., 2001
Age rangeAdults (16+ years); child version available (28 items)Baron-Cohen et al., 2001
AdministrationForced-choice: pick 1 of 4 mental state words matching eye region photo
ScoringTotal correct out of 36 (adults) or 28 (children)
Adult normsMean ~ 26.2 (SD ~ 3.6) in typical adultsBaron-Cohen et al., 2001
ASD normsMean ~ 21.9 (SD ~ 6.6) in autistic adultsBaron-Cohen et al., 2001
ReliabilityInternal consistency: Cronbach's alpha ~ 0.60-0.70 (modest)Olderbak et al., 2015
LimitationsLow reliability, possible confound with emotion recognition vs. ToM per seOlderbak et al., 2015

Faux Pas Test

PropertyValueSource
Original citationBaron-Cohen et al., 1999
Age range9 years to adultBaron-Cohen et al., 1999
AdministrationRead 10 faux pas stories + 10 control stories
Questions per storyDetection ("Did someone say something awkward?"), identification, belief, empathyBaron-Cohen et al., 1999
Scoring0-2 points per question; max 60 for faux pas storiesBaron-Cohen et al., 1999
Control storiesMust also score comprehension questions for controls
SensitivityGood for detecting subtle ToM deficits in ASD, right hemisphere lesions, frontotemporal dementiaStone et al., 1998; Gregory et al., 2002

Strange Stories (Happe, 1994)

PropertyValueSource
Original citationHappe, 1994
ConstructAdvanced ToM: irony, white lie, double bluff, misunderstanding, persuasion, appearance/reality, figure of speech, sarcasm, forgetting, contrary emotions
AdministrationRead vignettes, open-ended question: "Why did X say that?"
Scoring0 (incorrect), 1 (partial), 2 (full mental state reference)Happe, 1994
Number of stories8-16 ToM stories + physical control storiesHappe, 1994
Age rangeChildren (8+) and adultsHappe, 1994
ReliabilityInter-rater reliability for scoring: kappa > 0.85 recommendedHappe, 1994

Director Task (Visual Perspective-Taking)

PropertyValueSource
Original citationKeysar et al., 2003
ConstructLevel 2 perspective-taking under communicative demand
AdministrationGrid of objects; director (behind grid) instructs participant to move objects; some slots occluded from director's view
MeasureEye movements (egocentric intrusions), accuracy, RTKeysar et al., 2003
Key findingEven adults show egocentric errors on ~30-50% of critical trialsKeysar et al., 2003
Age range7 years to adultDumontheil et al., 2010

See references/task-database.md for the full task list with administration protocols.

Psychometric Considerations

Reliability Summary

TaskInternal ConsistencyTest-RetestSource
Sally-Anne (single item)N/A (binary)VariableWellman et al., 2001
Wellman & Liu ScaleGuttman scalability > 0.90ModerateWellman & Liu, 2004
RMETalpha ~ 0.60-0.70r ~ 0.63-0.83Olderbak et al., 2015; Fernandez-Abascal et al., 2013
Faux pas testalpha ~ 0.70-0.80Not well-establishedBaron-Cohen et al., 1999
Strange StoriesInter-rater: kappa > 0.85ModerateHappe, 1994
MASCalpha ~ 0.70AdequateDziobek et al., 2006

Validity Concerns

  1. 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).
  2. Floor effects: RMET and faux pas tests may show floor effects in clinical populations with severe deficits. Consider graded scoring.
  3. Ecological validity: Structured ToM tasks may not predict real-world social behavior (German & Hehman, 2006).
  4. Task purity: No ToM task measures only ToM. All tasks involve language, memory, executive function, and attention.

Confounds and Controls

Language

ConfoundImpactMitigationSource
Verbal demandsFalse belief tasks require comprehension of complex sentencesInclude vocabulary/language control measureMilligan et al., 2007
Narrative complexitySecond-order tasks have heavy memory loadAdd comprehension check questionsPerner & Wimmer, 1985
Word knowledge (RMET)Vocabulary confound in forced-choice emotion labelsControl for verbal IQOlderbak et al., 2015

Executive Function

ConfoundImpactMitigationSource
Inhibitory controlMust inhibit own knowledge to attribute false beliefInclude inhibition measure (e.g., Stroop, day-night)Carlson & Moses, 2001
Working memoryMust hold multiple perspectives simultaneouslyControl for WM spanCarlson & Moses, 2001
Cognitive flexibilityMust switch between self and other perspectiveInclude set-shifting measureCarlson & Moses, 2001

Recommended Control Measures

For any ToM study, include at minimum:

  1. Verbal ability: Receptive vocabulary (e.g., PPVT) or verbal IQ subscale
  2. Inhibitory control: Age-appropriate inhibition task
  3. Working memory: Forward/backward digit span or equivalent
  4. Non-ToM comprehension: Physical causality control stories (for Strange Stories and faux pas)

Task Combination Recommendations

Comprehensive Battery by Population

PopulationRecommended BatteryRationale
Preschool (3-5y)Wellman & Liu Scale (5 tasks) + diverse desires + diverse beliefsGuttman-scalable, captures developmental progression (Wellman & Liu, 2004)
School-age (6-12y)First-order FB + second-order FB + faux pas + Strange Stories subsetSpans 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 taskMultiple constructs; includes real-time and reflective tasks
Neurological (adults)Faux pas + Strange Stories + RMETSensitive to frontal and right hemisphere lesions (Stone et al., 1998)
Aging researchFaux pas + RMET + Strange StoriesControl for processing speed; established aging norms

Minimum Battery (2-3 tasks)

If time is limited, prioritize:

  1. One false belief task (for belief attribution)
  2. Faux pas or Strange Stories (for advanced ToM / social reasoning)
  3. RMET (for emotion/mental state recognition -- if construct-relevant)

Common Pitfalls

  1. 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).
  2. Ignoring ceiling/floor effects: Standard false belief tasks ceiling by age 5-6. The RMET has modest reliability. Check for restricted range.
  3. 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).
  4. 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).
  5. 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.
  6. 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).
  7. 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.
  8. 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tom Task Selector AI skill do?

Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance

Why use Tom Task Selector on TypingMind?

Because you install it once and use it with any model. Tom Task Selector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tom Task Selector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/12_Social_Cognition/tom-task-selector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tom Task Selector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tom Task Selector?

As many as you like. As long as a model supports skills, you can use Tom Task Selector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tom Task Selector AI skill free?

Yes. It is published on GitHub by NeuroAIHub under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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