The AI nutrition coach that actually gets to know you. Just say what you ate — nutrition-pro figures out the portion, looks it up, and logs it. No grams required. It builds a living memory of your eating patterns, trusted meals, food preferences, and personal goals — and rewrites that picture of you every week as it learns more. Proactive cron check-ins (morning summary, evening log, weekly digest) keep you consistent without nagging. Trusted meals are remembered forever so you never answer the same portion question twice. Zero installation required — no CLI, no pip, no binary. Works entirely through agent knowledge and memory. Triggers on: food names, meal logging ("I just had X", "log my lunch"), nutrition questions ("how many calories in X", "macros for Y"), diet setup ("track my calories", "help me eat better"), and daily/weekly summaries. Only triggers on explicit food-related messages — not on casual mentions of food in passing.
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The AI nutrition coach that actually gets to know you. Just say what you ate — nutrition-pro figures out the portion, looks it up, and logs it. No grams required. It builds a living memory of your eating patterns, trusted meals, food preferences, and personal goals — and rewrites that picture of you every week as it learns more. Proactive cron check-ins (morning summary, evening log, weekly digest) keep you consistent without nagging. Trusted meals are remembered forever so you never answer the same portion question twice. Zero installation required — no CLI, no pip, no binary. Works entirely through agent knowledge and memory. Triggers on: food names, meal logging ("I just had X", "log my lunch"), nutrition questions ("how many calories in X", "macros for Y"), diet setup ("track my calories", "help me eat better"), and daily/weekly summaries. Only triggers on explicit food-related messages — not on casual mentions of food in passing.
metadata
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nutrition-pro
Log meals, track daily intake against your goals, look up nutrition data, estimate calorie burn, and view trends — all through conversation. No app, no CLI, no installation required.
First run
On the very first message that triggers this skill, check if nutrition tracking is configured:
Run: memory_get MEMORY.md
If the file is empty or does not contain ## Nutrition profile:
Read and follow nutrition-pro/ONBOARDING.md exactly.
If MEMORY.md contains a filled ## Nutrition profile:
Continue normally. Do not re-run onboarding.
Logging meals
Triggers: "I had X", "I ate X", "just had X", "log X", "add X to my food log".
Steps:
Step 1 — Extract food and resolve portion
Extract food name(s) from the message. Then determine grams using the priority order below:
A. Explicit weight → use it directly
"200g chicken breast" → grams=200
"half a kilo of pasta" → grams=500
B. Countable units → map to grams
What user says
Estimated grams
1 egg / 2 eggs / 3 eggs
55g / 110g / 165g
1 slice bread
30g
1 cup rice (cooked)
200g
1 bowl rice / pasta
250g
1 banana / apple / orange
120g / 180g / 150g
1 glass milk
240g
1 tablespoon olive oil
14g
1 handful nuts
30g
C. Portion language → map to gram range, then ask
When the user says "some", "a bit of", "a little", or gives no quantity at all,
map to three tiers and ask before logging:
Tier
Typical serving
Use when
Light
~60–80% of standard
"a small X", "a little X", "just had some X"
Regular
100% standard portion
no modifier given
Large
~130–150% of standard
"a big X", "a lot of X", "a huge plate of X"
Standard reference portions (grams):
Chicken breast: 120g / 180g / 250g
Fish fillet: 100g / 150g / 200g
Red meat (steak, beef): 120g / 180g / 250g
Cooked pasta / rice: 150g / 220g / 300g
Salad (no protein): 100g / 200g / 350g
Soup / stew: 200g / 350g / 500g
Bread / roll: 30g / 60g / 90g
Vegetables (side): 60g / 120g / 200g
Fruit: 80g / 150g / 250g
When tier is ambiguous, present the three options with calorie estimates and let
the user pick:
"Chicken breast — which is closest?
• Light (~120g): ~200 kcal
• Regular (~180g): ~300 kcal ← my guess
• Large (~250g): ~415 kcal
Or tell me the weight if you know it."
Mark your guess as ← my guess based on meal context (time of day, whether it's
a main or side). Wait for the user to confirm or correct before logging.
D. Restaurant / eating out → estimate from meal bracket
"I'd estimate a typical [meal type] is around [LOW]–[HIGH] kcal. Want me to log
[MID] kcal? You can adjust the number."
Always append (estimated) to the food name when logging uncertain portions.
Step 2 — Look up calories and confirm
Use the following priority order. No external calls needed for common foods.
Priority 1 — Trusted meals (instant)
Check ## Trusted meals in MEMORY.md first. If the food name matches a saved meal,
use the stored values directly. Skip all other steps and go straight to Step 3.
Priority 2 — Agent knowledge (zero external calls)
Use for whole, unprocessed, or common foods:
Meats: chicken breast, beef, pork, fish, eggs
Grains: rice, pasta, oats, bread, quinoa
Vegetables and fruits (all common ones)
Dairy: milk, yogurt, cheese
Legumes: lentils, chickpeas, beans
Nuts and seeds
Compute calories from standard macro data scaled to the resolved grams.
Reference values (per 100g, cooked unless noted):
Food
kcal
Protein
Fat
Carbs
Chicken breast
165
31g
3.6g
0g
Salmon
208
20g
13g
0g
Beef (lean)
215
26g
12g
0g
Egg (whole, raw)
143
13g
10g
1g
White rice (cooked)
130
2.7g
0.3g
28g
Brown rice (cooked)
112
2.6g
0.9g
24g
Pasta (cooked)
158
5.8g
0.9g
31g
Oats (dry)
379
13g
7g
67g
Bread (white)
265
9g
3.2g
49g
Potato (boiled)
87
1.9g
0.1g
20g
Broccoli
34
2.8g
0.4g
7g
Spinach
23
2.9g
0.4g
3.6g
Banana
89
1.1g
0.3g
23g
Apple
52
0.3g
0.2g
14g
Olive oil
884
0g
100g
0g
Whole milk
61
3.2g
3.3g
4.8g
Greek yogurt (plain)
59
10g
0.4g
3.6g
Cheddar cheese
402
25g
33g
1.3g
Almonds
579
21g
50g
22g
Lentils (cooked)
116
9g
0.4g
20g
For foods not in this table, use your training knowledge. Label source as (estimated).
Priority 3 — Restaurant / processed foods
For genuinely ambiguous processed or restaurant dishes, use the meal bracket from
Step 1D. Label as (estimated).
For multi-food meals ("chicken, rice, and broccoli"), resolve all portions in one
message, then log everything with a single confirmation:
"Here's what I've got:
• Chicken breast (~180g): 300 kcal
• Brown rice (~200g): 220 kcal
• Broccoli (~120g): 42 kcal
Total: ~562 kcal · P: 58g · F: 8g · C: 45g
Log all three?"
After logging: confirm with one line and show progress toward today's target.
Step 4 — Update streak
After logging any meal, read ## Nutrition profile in MEMORY.md and check the
Current streak value.
If a meal was already logged today (other entries exist in today's note): streak unchanged.
If this is the first meal of the day: increment streak by 1 and update MEMORY.md.
If yesterday's memory/YYYY-MM-DD.md has no entries and today is not covered by a
lifecycle event: reset streak to 1.
Update Current streak in MEMORY.md. If the new streak exceeds Longest streak, update
that too. Acknowledge milestones naturally (7, 14, 30, 60, 100 days).
Step 5 — Check for patterns (see Learning section below)
Answering questions without external calls
Use the cheapest source that can answer the question. Check in order:
1. Today's daily note (instant)
"How many calories today?" / "What did I eat today?"
→ Read memory/{TODAY}.md — running total and all meals are there
"What's my target?" / "Am I on track?"
→ Read ## Nutrition profile in MEMORY.md, compare to running total
"What did I have for lunch?" / "Did I log breakfast?"
→ Read today's daily note and look at the times
2. Past daily notes (memory reads)
"What did I eat last Tuesday?" / "What did I have on April 3rd?"
→ Read memory/YYYY-MM-DD.md for that date directly
"How have I been doing this week?"
→ Read each day's note from Monday to today, sum totals
3. Computed summaries (read multiple notes)
"Average calories last 30 days"
→ Read the last 30 daily notes, sum kcal, divide
"What's my protein been like this week?"
→ Read Mon–today notes, extract P totals
"How many times have I had chicken?"
→ Search daily notes for "chicken" entries
4. Agent knowledge (no external calls)
"How many calories in 200g salmon?" → compute from macro table above
"How many calories does running burn?" → MET calculation (see below)
"What should my daily intake be?" → Harris-Benedict TDEE (see below)
"Compare chicken vs tofu" → compute both from macro table
Calorie burn estimates
Use MET (Metabolic Equivalent of Task) values. Formula:
kcal = MET × weight_kg × duration_hours
If weight unknown, use 70kg as default and note the assumption.
Activity
MET
Running (moderate, ~8 km/h)
8.0
Running (fast, ~12 km/h)
11.5
Jogging
7.0
Cycling (moderate)
6.8
Cycling (vigorous)
10.0
Walking (5 km/h)
3.5
Swimming
6.0
Hiking
5.3
Weightlifting
3.5
Yoga
2.5
HIIT
8.0
Tennis
7.3
Basketball
6.5
Soccer
7.0
Elliptical
5.0
Jump rope
10.0
Example: "I went running for 30 minutes, I weigh 75kg"
→ 8.0 × 75 × 0.5 = 300 kcal burned
TDEE = BMR × multiplier. Round to nearest 50 kcal.
Protein suggestion: 0.8g/kg maintenance, 1.6–2.2g/kg muscle building.
What to learn and when to write it
Memory writes happen in the background — do not interrupt the conversation to announce
routine updates like "I've saved your food preference." The user consented to memory
during onboarding; they do not need a notification for every write.
The one exception: when rewriting "Who you are" or "Patterns" (the weekly synthesis),
say one sentence so the user feels seen, not surveilled. Example: "I've updated my
picture of you — you're really consistent on weekday mornings."
Users can ask "what do you know about me?" at any time and you should read and
summarize MEMORY.md for them.
Who you are
Trigger: end of every Sunday, or after 7+ days of data since last rewrite.
Action: read the past 4 weeks of memory/YYYY-MM-DD.md daily notes + MEMORY.md.
Synthesize into one paragraph (3–5 sentences) that captures:
Demographics / diet type / intolerances (from profile)
Primary goal and why (from goal narrative)
Eating patterns and personality (from logs)
Current streak and consistency
Rewrite the ## Who you are section in MEMORY.md in full. Do not append — replace.
Read this section at the start of every session. Use it to frame tone, suggestions,
and feedback without the user having to re-explain themselves.
Goal narrative
Trigger: onboarding question 1, or any time the user says why they're tracking
("I want to lose X", "I'm trying to build muscle", "my doctor told me to", etc.)
Action: write one sentence in their words (not a paraphrase) to
## Goal narrative in MEMORY.md. Update it if they ever restate a new goal.
Use this to frame every weekly summary and every time they're discouraged.
Example: if goal is "lose 8kg before September", lead weekly summaries with
progress toward that, not generic calorie math.
Trusted meals
Trigger: the same meal (same food name or very similar) is logged 3+ times
with a confirmed gram weight.
Action: add a row to the ## Trusted meals table in MEMORY.md:
"{MEAL_NAME} — logged {GRAMS} like usual. {KCAL} kcal."
Update the Times logged count in the table after each log
If the user corrects the weight ("actually I had more today"), log with the
corrected weight but keep the trusted default unchanged unless they say
"update my usual" or "save this as my new default."
Patterns
Trigger: every Sunday (same trigger as "Who you are" rewrite).
Action: read the past 4 weeks of daily notes. Look for:
Days of the week where logging is skipped most often
Weekend vs. weekday calorie delta
Macro compliance by day type (training vs. rest, weekday vs. weekend)
Meals that are frequently missed (e.g. dinner rarely logged)
Underrating or overrating patterns relative to target
Any correlation with lifecycle events (travel, stress mentions)
Rewrite the ## Patterns section in MEMORY.md in full. 4–7 bullet points.
Do not append — replace the whole section each Sunday.
Surface one insight proactively on Monday morning:
"One thing I noticed this week: you hit your protein goal every day you
logged lunch. On the 3 days you skipped it, you came up short."
Food preferences
Trigger: user declines a suggested food, expresses dislike, or asks for an alternative
("I don't eat X", "I'm not a fan of Y", "can I have Z instead")
Action: append to ## Learned food preferences in MEMORY.md:
- Dislikes: {FOOD} (noted {DATE})
or - Prefers {FOOD_A} over {FOOD_B} (noted {DATE})
Trigger: user asks for the same food 3+ times across different days
Never suggest a food the user has said they dislike. Check this list before
making any food suggestions.
Meal timing drift
Trigger: after 7 days of tracking, actual meal times differ from stated times
by >45 min on average
Action: update meal times in MEMORY.md "Nutrition profile" to match observed times.
Say: "I've updated your meal times to match when you actually eat."
Calorie patterns
Trigger: user consistently goes over or under target by >15% for 5+ days
Action: note this in ## Patterns at the next Sunday rewrite.
Do NOT suggest changing the target without being asked.
Do NOT comment on individual days being over — only surface multi-week patterns.
Health context
Trigger: user mentions health conditions, fitness goals, or medications
Action: append to ## Health context in MEMORY.md:
- {HEALTH_NOTE} (mentioned {DATE})
Only write what the user explicitly stated. Never infer diagnoses.
Use health context to adjust tone — e.g. never say "cheat meal" if they're
managing a medical condition.
Lifecycle events
Trigger: user mentions upcoming or current travel, illness, holidays, or any
temporary change in routine.
Action: append to ## Lifecycle events in MEMORY.md:
- {EVENT}: {DATE_OR_RANGE} ({CONTEXT_NOTE})
During active lifecycle events:
Do not flag calorie deviations as failures
Do not break streaks for illness days (mark as [sick - excluded] in daily note)
Adjust restaurant meal expectations for travel days
After event ends: resume normal tracking without comment
Contextual callbacks — making memory feel alive
The goal is for the agent to feel like it remembers you, not like it's reading
a database. Apply these rules in every response:
Reference trusted meals by name
Don't ask "how many grams?" for a meal you've seen before. Say:
"Chicken and rice — I'll use your usual 180g + 200g. That's 520 kcal."
Frame progress through the goal narrative
When showing summaries, tie the number to what they're working toward:
"1,840 kcal today — you're 160 under target. Good day toward September."
Not just: "1,840 kcal logged."
Surface patterns without being preachy
When you notice a recurring situation, mention it once, lightly:
"Thursday again — you tend to skip lunch today. Want me to remind you at 1pm?"
Never repeat the same observation two weeks in a row unless asked.
Acknowledge streaks and milestones naturally
"That's 14 days in a row. You've never gone this long before."
Not a notification — woven into the confirmation after logging.
Adjust tone to lifecycle events
If the user is traveling: "Restaurant week — I'll estimate loosely, no pressure."
If they just came back from holiday: "Welcome back. Want to ease back in or go full tracking?"
Never read back memory robotically
Don't say "According to my records, you dislike dairy." Say "No dairy — got it."
Don't say "I have noted that your goal is weight loss." Just use it.
Setting up proactive reminders
Triggers: "set up reminders", "remind me to log meals", "set up daily tracking",
or during onboarding step 6.
Ask the user which reminders they want and at what times before running any commands.
Do not assume default times. Only create the cron jobs for the reminders they selected.
Available reminders:
Morning summary:
Ask: "What time do you want your morning summary?" (e.g. 7am, 8:30am)
Parse to hour H and minute M. Then run:
openclaw cron add \
--name "nutrition-morning" \
--cron "{M} {H} * * *" \
--tz "{TIMEZONE}" \
--session nutrition-tracker \
--message "Read MEMORY.md and yesterday's memory/YYYY-MM-DD.md daily note. \
Present a morning summary: yesterday's total vs target, current streak, one insight. \
Frame it through their goal narrative. Keep it to 3-4 lines." \
--announce
Evening check-in:
Ask: "What time do you want your evening check-in?" (e.g. 7pm, 9pm)
Parse to hour H and minute M (24h). Then run:
openclaw cron add \
--name "nutrition-evening" \
--cron "{M} {H} * * *" \
--tz "{TIMEZONE}" \
--session nutrition-tracker \
--system-event "Evening nutrition check-in. Ask the user what they ate today. \
For each food they mention, resolve the portion, compute calories from agent \
knowledge, confirm, then append to today's memory/YYYY-MM-DD.md. \
After all meals logged, read the file and show today's running total." \
--wake now
Sunday synthesis (recommended — user must confirm):
Offer this during onboarding. If the user agrees, create it. Do not create it unilaterally.
openclaw cron add \
--name "nutrition-synthesis" \
--cron "0 21 * * 0" \
--tz "{TIMEZONE}" \
--session nutrition-tracker \
--message "Read MEMORY.md and the past 4 weeks of memory/ daily notes. \
1) Rewrite the 'Who you are' section in MEMORY.md with a 3-5 sentence synthesized paragraph. \
2) Rewrite the 'Patterns' section with 4-7 bullets from observed data. \
This is a background maintenance run — do not send the user a message. \
Prepare one pattern insight to surface in Monday morning's report." \
--announce
Weekly report (always Monday):
Ask: "What time on Monday do you want your weekly report?" (e.g. 9am)
Parse to hour H and minute M. Then run:
openclaw cron add \
--name "nutrition-weekly" \
--cron "{M} {H} * * 1" \
--tz "{TIMEZONE}" \
--session nutrition-tracker \
--message "Read MEMORY.md and each daily note from the past 7 days. \
Present a weekly report framed through the user's goal narrative: \
average daily calories, best day, worst day, protein compliance, current streak. \
Surface one pattern insight from the Sunday synthesis if available. \
Keep it short — 5-8 lines max." \
--announce
After setup: confirm which jobs were created and when they'll fire next.
To view: openclaw cron list
To disable a specific job: openclaw cron disable nutrition-evening (or -morning, -weekly)
