Reverse-engineer viral content

This cookbook pulls one post apart so your team can see how it was built. You don’t run it; your agent does.

Run it

Paste this to your agent, filled in:

Fetch https://tryklint.ai/cookbooks/reverse-engineer-viral-content.md
and execute the cookbook it contains with my context:

- post: [the post URL or shortcode, e.g. instagram.com/reel/DOG123abc]
- compare: [optional: "against the account's recent record" is the default]

The URL carries everything your agent needs. Everything below this line addresses the agent executing it.

Context for the agent

Klint is the social context API: it searches and extracts Instagram posts and accounts, returned with transcripts, on-screen text, and creative structure already analyzed. Base URL https://api.tryklint.ai. Every request needs Authorization: Bearer $KLINT_API_KEY; never print or echo the key. This page is the source of truth for this procedure; work from the fetched copy, not memory. The full documentation index is at https://tryklint.ai/llms.txt, and the whole API contract is at https://tryklint.ai/for-agents/coding-agents.md. Last updated July 18, 2026.

Inputs

Input	What it is	Required	Source
`post`	The post to decompose: URL, shortcode, or Klint ID	yes	operator
`compare`	Baseline comparison scope	no; default the account’s recent record	operator
`KLINT_API_KEY`	API key	yes	operator’s environment

Objectives

Produce exactly one artifact: a teardown of the post in the Output format below, with every observation drawn from your own calls. Nothing else counts as done.

Procedure

Extract the post

GET https://api.tryklint.ai/v1/instagram/posts/{shortcode-or-id}. A shortcode from the URL works directly. A 404 with type not-tracked means the post is outside what Klint has observed. Report that to the operator and stop. Don’t decompose from memory.

Read the decomposition

The full post carries the parts. Work through them in order:

The hook: its construction, devices, and which modalities it operates in
The key messages and the proof offered for them
The offer and call to action
The narrative structure
The full transcript and on-screen text
The metrics, each with the timestamp of when it was observed

Put the numbers in the account's context

Raw counts mislead across accounts. Two comparisons, both handle-scoped and filters-only (filters-only searches may sort):

{
  "post": {
    "filters": {
      "posted_at": { "gte": "[90 days ago, RFC 3339]" }
    },
    "author": {
      "filters": { "handle": { "in": ["[author handle]"] } }
    }
  },
  "sort": { "by": "views", "order": "desc" },
  "limit": 20
}

Establish where this post ranks in the account’s own window. If the post carries view_ratio_to_account_baseline, quote it; it needs account history to exist, so its absence is not a signal.

Find the execution's neighbors

One semantic sweep built from the post’s promise, to see whether this execution stands alone or belongs to a pattern:

{
  "post": {
    "query": "[the post's core promise, in plain words]",
    "filters": {
      "posted_at": { "gte": "[6 months ago]" }
    }
  },
  "limit": 15
}

Note which neighbors predate the post and which followed it, by posted_at.

Verify before you report

Check against the Objectives. Every section of the teardown quotes fields from your own responses. The ranking claim cites the comparison sweep. Neighbors carry their dates. Describe what is observable. Never explain why it went viral; state what it did and how that differed from the account’s record.

Output format

Use exactly this structure:

# Teardown: @[handle], [shortcode]

## The hook
[Construction, devices, modalities. Quote the opening line or visual.]

## The message
[Key messages and their proof. The offer and CTA, if present.]

## The build
[Format, narrative structure, length, notable creative choices.]

## The numbers
[Metrics with observed-at dates. Rank within the account's last 90 days.
Baseline ratio if published.]

## The neighbors
[Same-promise posts found, with dates: predecessors and followers.]

## Scope
[What was unavailable: null fields, missing baseline, tracking caveats.]

Boundaries

The teardown describes what was published and how the numbers moved. Causal claims (“it worked because”) and predictions (“this formula will work”) are out.
If enrichment fields are null, say so in Scope; never fill gaps from your own viewing or memory of the post.
Neighbors are matches within what Klint tracks. The earliest observed neighbor is not necessarily the originator.

Get Started

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Cookbooks

For Agents

Reverse-engineer viral content

Run it

Context for the agent

Inputs

Objectives

Procedure

Output format

Boundaries

​Run it

​Context for the agent

​Inputs

​Objectives

​Procedure

​Output format

​Boundaries

Run it

Context for the agent

Inputs

Objectives

Procedure

Output format

Boundaries