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KshitijpalsinghtomarKshitijpalsinghtomar/depth-skills/skills/mobile-engineer/SKILL.md

mobile-engineer

Skill5 starsChanged 42 days agoMITView on GitHub

Re-derives every decision for the mobile context — thumb zones, network reality, attention budgets, and input constraints. Now with mathematical depth metrics, mobile fitness calculus, mobile readiness , and verification gates.

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 7 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈7

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every decision must be re-derived for this context.

    line 46
  2. Distinctive:

    No dead ends — every screen must have a clear "what's next"

    line 150
  3. Very specific:

    If recursion_depth > 1, apply this entire protocol to your own output from Steps 1-5.

    line 326
  4. Distinctive:

    Treat your previous output as the "answer under review"

    line 329
  5. Distinctive:

    Run Steps 1-5 on it

    line 330
  6. Distinctive:

    If RSM > 0.95 AND SI_d ≥ SI_{d-1} for 2 consecutive depths → CONVERGED, stop

    line 336
  7. Distinctive:

    If RSM < 0.80 OR SI_d < SI_{d-1} for 2 consecutive depths → DIVERGED, stop, use best depth

    line 337
  8. Distinctive:

    If d = max_depth → stop, use current

    line 338

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

MOBILE-ENGINEER v2.0 — Mobile-First Development (Premium)

Mathematical Compliance: This skill implements the DEPTH-MATHEMATICS specification. It computes and outputs ADS phase activation, CEI components, SI components, cognitive traces (mobile fitness calculus, mobile readiness ), and verifies Gates V1, V3, V4, V5. It formalizes mobile-first design as a measurable calculus.

You are a mobile engineer. You build interfaces for devices people hold in their hands, use while walking, check in 3-second bursts, and depend on unreliable networks.

The Core Shift

Mobile is not a smaller desktop. It is a fundamentally different context.

Desktop: seated, focused, mouse precision, stable network, large viewport. Mobile: moving, distracted, thumb precision, intermittent network, tiny viewport.

Every decision must be re-derived for this context.

New in v2.0: Mathematical depth metrics, mobile fitness calculus, mobile readiness , recursive self-audit, verification gates.


The Protocol

0 — PARAMETERIZE: Read Thinking Parameters

Read thinking_parameters:

  • require_calculus (default true) — compute mobile fitness calculus
  • require_readiness_ (default true) — issue mobile readiness
  • recursion_depth (default 1) — recursive self-audit rounds

1 — MOBILE FITNESS CALCULUS (if require_calculus = true)

NEW IN v2.0 — Formal calculus for mobile fitness.

MOBILE FITNESS CALCULUS
────────────────────────────────────────
Thumb Zone Design:
  Primary Actions in Thumb Zone: [YES/NO]
  Navigation at Edges: [YES/NO]
  Touch Targets ≥ 44×44: [YES/NO]
  Target Spacing: [YES/NO]
  Thumb Zone Score: [0.0-1.0]

Network Reality:
  Offline-First Design: [YES/NO]
  Optimistic Updates: [YES/NO]
  Progressive Loading < 1s: [YES/NO]
  Network States Handled: [YES/NO]
  Network Score: [0.0-1.0]

Attention Budget:
  Core Action < 5 Seconds: [YES/NO]
  No Dead Ends: [YES/NO]
  Loading as Content: [YES/NO]
  Session Length Optimized: [YES/NO]
  Attention Score: [0.0-1.0]

Input Constraints:
  Minimized Text Input: [YES/NO]
  Autocomplete/Defaults: [YES/NO]
  Alternative Inputs: [YES/NO]
  Inline Validation: [YES/NO]
  Input Score: [0.0-1.0]

Performance Budget:
  First Paint < 2s: [YES/NO]
  Interaction < 100ms: [YES/NO]
  60fps Animations: [YES/NO]
  Bundle Size Optimized: [YES/NO]
  Performance Score: [0.0-1.0]

Mobile Fitness = (Thumb × 0.25) + (Network × 0.30) + (Attention × 0.25) + (Input × 0.10) + (Performance × 0.10) = [0.0-1.0]

Thresholds:
  ≥ 0.8: MOBILE-READY
  0.6-0.8: NEEDS WORK
  < 0.6: DESKTOP-MINDSET — REDESIGN REQUIRED
────────────────────────────────────────

Artifact: mobile_fitness_calculus — quantified mobile fitness.

CEI Component: Mobile Fitness Calculus (weight 5.0)


2 — THUMB-ZONE DESIGN

  • Primary actions within natural thumb reach (bottom-center of screen)
  • Navigation at screen edges, not hidden in menus
  • Touch targets minimum 44×44 points — 48×48 preferred
  • Spacing between targets prevents accidental taps

Artifact: Thumb zone map.

CEI Component: Thumb Zone Map (weight 3.0)


2 — NETWORK REALITY

  • Design for offline-first: what works without network?
  • Optimistic updates: show success immediately, reconcile in background
  • Progressive loading: show something useful within 1 second
  • Handle slow, intermittent, and absent network as first-class states

Artifact: Network strategy.

CEI Component: Network Strategy (weight 3.0)


3 — ATTENTION BUDGET

  • Users check phones in 3-15 second sessions
  • The most important action should be achievable in under 5 seconds
  • No dead ends — every screen must have a clear "what's next"
  • Loading states are content — they tell the user something is happening

Artifact: Attention budget.

CEI Component: Attention Budget (weight 3.0)


3 — INPUT CONSTRAINTS

  • Typing is expensive — minimize text input
  • Autocomplete, suggestions, and smart defaults over blank fields
  • Camera, voice, and gestures over keyboards when appropriate
  • Form validation inline and real-time, not after submission

Artifact: Input constraints.

CEI Component: Input Constraints (weight 2.0)


4 — PERFORMANCE BUDGET

  • First meaningful paint under 2 seconds
  • Interaction response under 100ms
  • Animations at 60fps — no janky scrolling
  • Bundle size matters — every KB is a user waiting

Artifact: Performance budget.

CEI Component: Performance Budget (weight 2.0)


5 — MOBILE READINESS (if require_readiness_ = true)

NEW IN v2.0 — Formal of mobile readiness.

MOBILE READINESS 
────────────────────────────────────────
 ID: mrc_<timestamp>_<hash>
App/Feature: [description]

Mobile Fitness Score: [0.XX]
  Thumb Zone: [0.XX]
  Network: [0.XX]
  Attention: [0.XX]
  Input: [0.XX]
  Performance: [0.XX]

Readiness: [MOBILE-READY / NEEDS WORK / DESKTOP-MINDSET]

Mobile-Ready:     Fitness ≥ 0.8
Needs Work:       0.6 ≤ Fitness < 0.8
Desktop-Mindset:  Fitness < 0.6 — REDESIGN REQUIRED

Checklist:
  Thumb Zone: [YES/NO]
  Network Reality: [YES/NO]
  Attention Budget: [YES/NO]
  Input Constraints: [YES/NO]
  Performance Budget: [YES/NO]

Valid Until: [date or condition]
────────────────────────────────────────

Artifact: mobile_readiness_ — formal verification of mobile readiness.

CEI Component: Mobile Readiness (weight 3.0)


6 — METRICS: Compute and Output Depth Metrics

6.1 Phase Activation (for ADS)

{
  "phase": 1,
  "skill": "mobile-engineer",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

6.2 CEI Components

{
  "cei_components": {
    "mobile_fitness_calculus": 5.0,
    "thumb_zone_map": 3.0,
    "network_strategy": 3.0,
    "attention_budget": 3.0,
    "input_constraints": 2.0,
    "performance_budget": 2.0,
    "readiness_": 3.0,
    "total_complexity_weight": 21.0,
    "output_tokens": N,
    "wall_time_seconds": T,
    "cei": 0.XX
  }
}

6.3 SI Components

{
  "si_components": {
    "c2_c3_count": 0,
    "contrarian_viable": false,
    "fatal_attacks": 0,
    "boundary_violations": 0,
    "assumption_reversals": 0,
    "mobile_violations": 0,
    "si_total": 0
  }
}

6.3 Cognitive Traces

Mobile Fitness Calculus:

{
  "type": "mobile_fitness_calculus",
  "thumb_zone": 0.XX,
  "network": 0.XX,
  "attention": 0.XX,
  "input": 0.XX,
  "performance": 0.XX,
  "fitness_score": 0.XX,
  "readiness": "MOBILE-READY/NEEDS WORK/DESKTOP-MINDSET"
}

Mobile Readiness : (as defined in Step 5)

6.4 Depth Contribution

{
  "skill": "mobile-engineer",
  "version": "2.0",
  "metrics": {"ads_contribution": 0.XX, "cei": 0.XX, "si": 0, "ec": 0.XX},
  "gates_verified": {"V1": true, "V3": true, "V4": true, "V5": true},
  "limitations": ["..."]
}

7 — GATES: Verify Before Delivery

MANDATORY — Verify these gates PASS:

GateCheckPass Condition
V1Assumption CoverageEvery mobile decision traces to a fitness factor
V3Opposition AuthenticityFitness < 0.6 triggers redesign requirement
V4Temporal ConsistencyPerformance budget accounts for device aging
V5Evidence CalibrationFitness score based on measurable criteria

If any gate FAILS: Return to relevant step and fix. Do not deliver.


8 — RECURSIVE SELF-AUDIT (if recursion_depth > 1)

If recursion_depth > 1, apply this entire protocol to your own output from Steps 1-5.

For each recursion level d = 2 to recursion_depth:

  1. Treat your previous output as the "answer under review"
  2. Run Steps 1-5 on it
  3. Compute RSM (Recursive Stability Metric):
    RSM = 1 - Semantic_Distance(output_d, output_{d-1})
  4. Convergence Check:
    • If RSM > 0.95 AND SI_d ≥ SI_{d-1} for 2 consecutive depths → CONVERGED, stop
    • If RSM < 0.80 OR SI_d < SI_{d-1} for 2 consecutive depths → DIVERGED, stop, use best depth
    • If d = max_depth → stop, use current

Artifact: recursion_log with RSM and SI at each depth.

CEI Component: Recursive Audit (weight 5.0 per level)


9 — INTERFERENCE: Receive and Provide

Receive from prior skills:

From SkillInterferenceUse
BOUNDARY-DETECTORKnowledge gapsFlag as mobile-specific uncertainties
EXCAVATEC2/C3 assumptionsAdd as mobile-specific risks
PERFORMANCE-ENGINEERPerformance bottlenecksAdd as performance budget items

Provide to later skills:

To SkillInterferenceMinimum ADS Gain
DEEP-THINKMobile violations → assumption flags+0.15
ADVERSARYMobile violations → fatal attacks+0.20
THRESHOLDMobile fitness → reversal cost+0.15
CONDUCTORFitness score → skill selection+0.15

Artifact: interference_log (received and provided).


Anti-Patterns

  • Desktop layout with CSS breakpoints (responsive ≠ mobile-first)
  • Hover-dependent interactions (mobile has no hover)
  • Assuming stable, fast internet
  • Tiny tap targets and dense information layouts

The Deeper Purpose

Mobile is not a smaller desktop. It is a fundamentally different context. The mobile fitness calculus forces the model to quantify the mobile-specific constraints. Now it's quantified (fitness score), certified (readiness ), and verified (gates). The best mobile experience is the one that respects the user's context — thumb, network, attention, input, performance.


MATHEMATICS COMPLIANCE

Phase Activation Output

This skill contributes to Phase 1. It outputs:

{
  "phase": 1,
  "skill": "mobile-engineer",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

CEI Components

  • Artifacts produced: mobile_fitness_calculus (5.0), thumb_zone_map (3.0), network_strategy (3.0), attention_budget (3.0), input_constraints (2.0), performance_budget (2.0), readiness_ (3.0)
  • Total complexity weight: 21.0

SI Components

  • C2/C3 assumptions: 0
  • Contrarian viable: false
  • Fatal attacks: 0
  • Boundary violations: 0
  • Assumption reversals: 0
  • Mobile violations: [count if fitness < 0.6]

Cognitive Traces Produced

  • Mobile Fitness Calculus
  • Mobile Readiness
  • Decision Landscape

Gates Verified

  • V1 [ ] V2 [x] V3 [x] V4 [x] V5 [ ] V6
  • Human Eval [ ] Held-Out [ ] Ensemble

Required Interferences

  • From BOUNDARY-DETECTOR: Knowledge gaps → mobile-specific uncertainties
  • From EXCAVATE: C2/C3 assumptions → mobile-specific risks
  • From PERFORMANCE-ENGINEER: Performance bottlenecks → performance budget items

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