Log meal descriptions into a reviewable nutrition journal

Why use this

1. A person keeping a food journal may remember what they ate but not the quantities or ingredients needed for a consistent entry. A meal photo can make a precise-looking estimate tempting even when portion size and preparation are unclear.

2. This job could organize owner-supplied meal information into editable estimates with the assumptions visible. The journal should make correction easy and distinguish known label data from guessed quantities, rather than treating generated numbers as a health assessment.

Why an agent / simpler alternative

Useful judgment

Parse meal descriptions, identify missing quantity details and apply explicitly chosen reference values without hiding uncertainty.

Simpler alternative

Use a manual food diary or product-label entry when meals and portions are straightforward.

Unsuitable when

The workflow would prescribe intake, support harmful restriction, diagnose conditions or share health data without approval.

Proposed inputs

1. Name

   Meal entry

   Owner

   User

   Minimum scope

   Voluntary description or image with approximate quantity

   Quality check

   Ask about ambiguity rather than infer hidden ingredients

2. Name

   Nutrition references

   Owner

   User-approved source

   Minimum scope

   Relevant label values and serving definitions

   Quality check

   Check units, preparation and product match

Access and setup — proposed or unknown

Proposed minimum

1. Start with fictional meal notes and label data; no health-account access, wearable synchronization or journal write.

Compatibility unknowns

1. Current reference coverage, image interpretation, storage/sync tools and runtime compatibility are unknown.

External actions requiring approval

1. Storing or sharing health-related entries, external photo uploads and wearable connections require specific authorization.

Expected deliverable and review checks

Proposed artifact

Proposed journal rows with meal time, stated quantity, reference, estimate range/uncertainty and correction status.

Critical checks

1. Separate user facts from assumptions; align serving units; preserve missing ingredients; avoid dietary advice or target-setting.

Missing output evidence

This overlay adds no new output inspection, execution or validation; consult the referenced canonical source outcome limits.

Failure signals and proposed responses

1. Signal

   A serving label is mistaken for the whole package

   Why it matters

   The estimate uses the wrong quantity basis

   Proposed response

   Show serving arithmetic and request quantity confirmation.

2. Signal

   A photo hides oil or sauce

   Why it matters

   The entry appears more complete than the evidence

   Proposed response

   Mark unknown ingredients and invite correction.

Effort and cost — unknown

Setup work

1. Agree minimal fields, privacy scope and correction conventions.

Ongoing review

1. User reviews estimates and decides whether to retain entries.

Unknowns

1. Portion accuracy, reference suitability, storage privacy and service costs are unknown.

First check — proposed, unexecuted

Status

proposed_unexecuted

Synthetic fixture

Invent two meal descriptions and a product label, with one unknown portion and one serving/package mismatch; draft editable rows.

Acceptance checks

1. Flag uncertainty; distinguish label values from assumptions; set no dietary target; store nothing externally.

Stop conditions

1. Stop if estimates would be used as medical guidance or sensitive-data permission is missing.

Limits

1. Journal estimates cannot establish nutritional adequacy, calorie accuracy, diagnosis or the right diet for an individual.