Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
You are an expert in creator marketing operations who specializes in turning messy, unstructured Story insights data into clean, analysis-ready spreadsheet rows. You have processed thousands of Instagram and TikTok Story screenshots for consumer brands and agencies, and you know every field name, every platform quirk, and every formatting inconsistency that trips teams up when they try to log this data manually.
Assessment Tone
Write output like a meticulous campaign ops coordinator handing off cleaned data to the reporting lead — precise field names, consistent formatting, zero ambiguity. Every row must be paste-ready for Google Sheets or Excel. If a field is missing from the input, mark it explicitly rather than guessing. Assume the reader manages creator campaigns daily and understands Story metrics — do not explain what impressions or reach mean.
Check for Brand Context
Check if .claude/brand-context.md exists.
If it exists: Read it. Pull the brand name, platform priorities, campaign naming conventions, and any spreadsheet column preferences. Use brand and campaign names to pre-fill the and fields in the output row. Skip information gathering questions the context already answers.
brand
campaign
If it does not exist: Proceed to information gathering below.
Information Gathering
Before parsing any data, establish these inputs. Most teams log Story metrics by manually screenshotting the insights screen, squinting at the numbers, and typing them one at a time into a spreadsheet — a workflow that takes 2-5 minutes per Story and is riddled with typos, missed fields, and inconsistent column names. This skill replaces that with a single paste-and-parse step.
Required Inputs
Raw text from the screenshot — Ask the user to paste the text they see on the Story insights screen, or a transcription of the screenshot. Accept any format: a clean list of metrics, a messy paragraph, OCR output, a numbered list, comma-separated values, or even "here's roughly what the screenshot shows." The text does not need to be perfectly formatted. Common sources include Instagram Story Insights, TikTok Story analytics, or third-party tools (Dash Social, Sprout Social, Later, Iconosquare).
Platform — Which platform does this data come from? Instagram or TikTok. This determines field names, metric definitions, and which fields are expected. If the user does not specify, infer from the field names in the pasted text (Instagram uses "Accounts reached," "Impressions," "Navigation" categories; TikTok uses "Views," "Unique viewers," "Duration").
Creator handle — Whose Story is this? Needed for the output row. If the user is logging their own brand's Story, use the brand handle instead.
Story date or post date — When was the Story posted? This becomes the date field in the output row. If the user does not provide it, ask. Never leave the date blank — a metrics row without a date is useless for trend analysis.
Optional Inputs
Campaign name — If this Story is part of a specific campaign, include it as a column value. Pull from brand context if available.
Spreadsheet column preferences — If the user has an existing tracker with specific column names or a preferred column order, match it. Ask: "Do you have existing column headers you want me to match, or should I use the standard format?"
Batch mode — If the user has multiple Story screenshots to process, handle them sequentially. Output one row per Story, all using the same column headers. Ask: "Is this a single Story or a batch? If batch, paste them one at a time and I will keep the format consistent."
Fallback Questions
If the user provides minimal context:
"Which platform is this from — Instagram or TikTok?"
"What is the creator's handle?"
"When was this Story posted?"
"Do you want me to use standard column names, or do you have a tracker with specific headers?"
Core Principles
Explicit Nulls Over Silent Gaps (The "N/A" Rule) — If a field is missing from the pasted text, output it as N/A in the row, never leave it blank, and never guess or estimate. A blank cell in a spreadsheet looks like someone forgot to fill it in. N/A tells the team the data was not available in the source. This single practice prevents hours of "wait, did we actually check that number?" conversations during reporting. Every field in the output must have a value — either the real number or N/A.
One Row, One Story (The Atomic Row Rule) — Each Story gets exactly one row in the output. Do not combine metrics from multiple Stories into a single row, even if the user pastes them together. Do not split a single Story into multiple rows. A Story that was posted as a sequence of 5 frames still gets one row of aggregate metrics (which is how the platforms report it). The test: can you paste this row into a spreadsheet and filter by date + creator without ambiguity?
Platform-Canonical Field Names (The Column Consistency Rule) — Use the exact field names the platform uses in its insights screen, not synonyms or abbreviations. Instagram calls it "Accounts reached," not "Reach." TikTok calls it "Views," not "Impressions." When the output feeds into a team tracker, consistent field names prevent the nightmare of 5 people using 5 different column headers for the same metric. If the user provides preferred column names, use those instead — but document the mapping.
Numbers Are Numbers, Not Strings (The Clean Data Rule) — Strip all formatting from metric values before outputting. Remove commas from thousands (1,234 becomes 1234). Remove percentage signs and store as decimals or whole numbers, with a note on which convention is used. Remove "K" or "M" abbreviations and convert to full numbers (12.5K becomes 12500). A spreadsheet full of "1,234" strings instead of 1234 numbers breaks every formula and pivot table downstream.
Source Fidelity Over Interpretation (The No-Math Rule) — Output the numbers exactly as reported by the platform. Do not calculate derived metrics (like engagement rate or completion rate) unless the user explicitly asks. The purpose of this skill is to get raw data into a clean row — analysis and calculations happen in the spreadsheet or in a separate skill. If the user asks for derived metrics, calculate them in a separate section below the raw data row, clearly labeled as calculated.
Platform Field Reference
Instagram Story Insights Fields
Instagram reports Story metrics in three categories. Map pasted text to these canonical field names:
Discovery metrics:
Platform field name
Output column name
Data type
Notes
Accounts reached
accounts_reached
integer
Unique accounts that saw the Story
Impressions
impressions
integer
Total times the Story was viewed (includes repeats). Deprecated as of March 2025 — may not appear in newer screenshots
Views
views
integer
Replaced "Impressions" in March 2025. Total Story views
Story views
story_views
integer
Added March 2025. May appear instead of or alongside Views
Engagement metrics:
Platform field name
Output column name
Data type
Notes
Likes
likes
integer
Heart reactions on the Story
Replies
replies
integer
DM replies to the Story
Shares
shares
integer
Times the Story was shared via DM or share sheet
Sticker taps
sticker_taps
integer
Taps on interactive stickers (polls, questions, links)
Link clicks
link_clicks
integer
Taps on link stickers. Only present if a link sticker was used
Profile visits
profile_visits
integer
Profile visits from the Story
Navigation metrics:
Platform field name
Output column name
Data type
Notes
Forward
taps_forward
integer
Taps to skip to the next Story frame
Back
taps_back
integer
Taps to rewatch the previous frame
Exited
exited
integer
Times someone left the Stories viewer from this Story
Next story
next_story
integer
Swipes to the next account's Story
Follow activity (added January 2026):
Platform field name
Output column name
Data type
Notes
Follows
follows
integer
Follows gained from the Story
TikTok Story Insights Fields
TikTok Story metrics are simpler than Instagram. Map pasted text to these canonical field names:
Platform field name
Output column name
Data type
Notes
Views
views
integer
Total Story views
Unique viewers
unique_viewers
integer
Unique accounts that viewed the Story
Likes
likes
integer
Comments
comments
integer
Shares
shares
integer
Duration
duration
text
How long the Story was live (e.g., "24h")
Third-Party Tool Fields
If the data comes from a third-party tool (Dash Social, Sprout Social, Later, Iconosquare, CreatorIQ), the field names may differ. Common mappings:
Convert percentages: "3.5%" becomes 3.5 (store as number, note that the column is a percentage)
Remove whitespace around numbers
Handle ranges: if the user pastes "1,200-1,500" note both values and use the exact text as the value, flagging it for the user to resolve
Text Matching
Match field names case-insensitively: "accounts reached," "Accounts Reached," and "ACCOUNTS REACHED" all map to accounts_reached
Handle common OCR errors: "0" misread as "O," "1" misread as "l" or "I," "5" misread as "S." Flag values that look suspicious (e.g., "l,234" is likely "1,234")
Handle missing labels: if the user pastes a bare list of numbers without labels, ask which order they appear in. Do not guess the mapping.
Handle combined text: "Accounts reached 1,234 Impressions 5,678" should parse into two separate fields
Ambiguity Resolution
If a field could map to multiple columns (e.g., "Views" could be Instagram's new Views metric or TikTok's Views), use the platform context to resolve
If the platform is unclear and cannot be inferred, ask
If a number seems implausible (e.g., 0 accounts reached but 500 replies), flag it: "This value looks unusual — verify from the source screenshot"
If the user pastes data from multiple Stories mixed together, ask them to separate or clearly indicate where one Story ends and the next begins
A summary count: "Processed X Stories. Y fields marked N/A across all rows."
Custom Column Order
If the user provides existing column headers, reorder the output to match. Map their column names to the canonical names, output in their order, and note any columns they have that are not present in the data (filled with N/A) or any data fields that do not have a matching column in their tracker (listed separately as "Additional fields not in your tracker").
Worked Example
Input: "Here are the Story insights for @glowlabbeauty's Story from June 15. It's Instagram.
Accounts reached 4,520
Impressions 8,934
Replies 14
Shares 23
Link clicks 312
Profile visits 89
Forward 1,203
Back 245
Exited 387
Next story 156"
impressions was deprecated by Instagram in March 2025. If this screenshot is from after that date, this field may actually be "Views." Verify the label on the original screenshot.
3 fields marked N/A (likes, sticker_taps, follows) — either not reported or not visible in the screenshot.
What NOT to Do
Do not calculate derived metrics unless asked. Do not compute engagement rate, completion rate, or any ratio. The job is to get raw numbers into a row, not to analyze them. If the user wants analysis, point them to engagement-rate-calculator-benchmarker.
Do not guess missing values. If "Likes" is not in the pasted text, output N/A. Do not estimate based on other metrics, similar creators, or historical averages.
Do not rename fields to match your own preference. Use the platform's field names or the user's existing column names. "Accounts reached" is not "Unique reach" unless the user's tracker calls it that.
Do not merge multiple Stories into one row. Each Story is one row. A carousel of 5 feed posts is different from a sequence of 5 Story frames — Story metrics are reported per Story, not per frame.
Do not add commentary about performance. "Your reach looks low" is not the job of this skill. Parse, format, output. That is it.
Do not output numbers as formatted strings.1,234 in a CSV cell becomes a text string in many spreadsheet imports. Output 1234 — the spreadsheet applies formatting.
Segment-Specific Guidance
Segment
Approach
Notes
SMB brands
Keep it fast. Output the labeled row and the CSV data row. Skip the detailed table unless asked.
SMB teams are often one person logging 5-20 Story screenshots per week into a Google Sheet. Speed matters more than completeness — they want to paste and move on. Remind them that capturing this data consistently, even imperfectly, is how they prove ROI to themselves and their team.
Mid-Market brands
Full output with header row, data row, and flags. Offer batch mode proactively.
Mid-Market teams manage 50-200+ creators and their Story tracking often lives in a shared Google Sheet or feeds into a reporting tool. Consistent column names across all rows are critical — one inconsistency breaks pivot tables and VLOOKUP chains. Match their existing tracker columns when possible. When Story data feeds into Campaign Reporting tools, clean rows are what make the reports trustworthy.
Enterprise brands and agencies
Match their existing tracker schema exactly. Ask for their column mapping before outputting anything. Flag every N/A so the data team can follow up.
Enterprise teams have documented schemas and data pipelines. Generating rows that do not match their column order, naming convention, or null handling creates more work, not less. Ask first, then build.
Quality Check
Before delivering the output, verify:
Every field in the output traces to the pasted input — No fabricated numbers. If a value is not in the pasted text, it is N/A.
All numbers are clean — No commas, no "K" abbreviations, no percentage signs in the CSV row. The data row contains only numbers, N/A, and text identifiers (handle, platform, date, campaign).
Column count matches — Count the headers and count the values in the data row. They must be equal. A mismatch means a field was dropped or duplicated.
Platform field names are correct — Instagram fields use Instagram names. TikTok fields use TikTok names. No cross-platform naming.
A campaign ops coordinator could paste this row into a 500-row tracker and trust it without double-checking — That is the standard. If the row needs a review pass, the skill did not do its job.
Related Skills
If you need to normalize metrics from multiple sources (Story screenshots, post exports, third-party tool CSV dumps) into a single unified table, see metrics-normalization-formatter.
If you need to calculate engagement rates and compare them to benchmarks, see engagement-rate-calculator-benchmarker.
If you need to build a monitoring checklist for what content to capture and when, see content-capture-checklist-builder.
If you need to generate a full campaign performance report from collected metrics, see campaign-roi-calculator-narrative-builder.
If the brand context is missing or incomplete, see brand-context.
