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instagram-content-benchmark

instagram-content-benchmark

github.com/postplusaiMarketing & growth27525 installsUpdated 1d ago
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About instagram-content-benchmark

A skill for benchmarking Instagram posts and Reels to identify strong examples and recurring content patterns. It helps marketers compare samples and extract hooks, caption structures, formats, and content pillars.

Key features

  1. 01Collect post and Reel samples
  2. 02Normalize results into a comparable dataset
  3. 03Rank by engagement, relevance, recency, and format
  4. 04Create benchmark shortlists
  5. 05Summarize hooks, formats, captions, and hashtags

Use cases

  1. 01Compare content across selected themes
  2. 02Find recurring opening hooks and caption patterns
  3. 03Identify content pillars from benchmark examples

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/postplusai/postplus-skills --skill instagram-content-benchmark

After installing, type/instagram-content-benchmarkin Claude Code to confirm it loaded.

Details

From the project README

Instagram Content Benchmark

Use this skill to benchmark Instagram posts and Reels, find strong examples, and extract reusable hooks, caption structures, formats, and content pillars.

Apply shared rulebook and user-guidance rules from postplus-shared.

Do Not Use When

  • The user wants to evaluate known accounts. Route to instagram-account-research.
  • The user wants to discover creators for outreach. Route to instagram-creator-discovery.
  • The user wants comment language. Route to instagram-audience-voice after a shortlist exists.

Collection Key Routing

Route to the matching hosted collection key by task shape:

  • post or Reel samples from known accounts or URLs,
  • hashtag-derived benchmark pools.

Discover the exact released collection keys and request shapes with postplus research schema --json. Use hosted collection outputs and the workflow below.

Default Workflow

  1. Define seeds: usernames, post URLs, Reel URLs, hashtags, or themes.
  2. Collect the first candidate pool at 10-15 posts or reels per theme.
  3. Normalize post and Reel-like results into one comparable dataset.
  4. Rank by engagement proxy, relevance, recency, and format fit.
  5. Produce a benchmark shortlist.
  6. Summarize repeated hooks, visual formats, caption patterns, and hashtag use.

Treat 10-15 posts or reels per theme as the first-pass boundary. Do not compile a wider first input from broad wording like "as many as possible." Expand only after the first shortlist proves seed quality and the user approves a second pass.

Good Output

Return top benchmark examples, content-type split, repeated opening patterns, value promises, likely content pillars, and recommended next posts to inspect deeper.

Failure Modes

  • Stop if no usable benchmark seeds exist.
  • Stop on unsupported keys, missing auth, unavailable hosted service, or stable network failure.
  • Do not treat benchmark content as permission to contact creators directly; lead work belongs in creator-outreach.

Handoff

  • Need creator pool from benchmark content -> instagram-creator-discovery.
  • Need comment sentiment or audience language -> instagram-audience-voice.
  • Need shot-level video breakdown -> video-analysis.

Public Command Boundary

  • Choose the smallest matching command or workflow from the user input and run it directly.
  • Readiness diagnostics: postplus doctor --skill instagram-content-benchmark.
  • If an owned CLI or script command fails, report the exact error and stop. Do not bypass the failure with metadata-only answers, readiness probing, local payload rewrites, fallback providers, or unpublished tools.
  • Use postplus research schema --collection-key <collectionKey> --json only when constructing or repairing an unknown request shape.
  • Hosted collection runs through the shared research collect verb (owned by instagram-tools), attributing the run to this skill: postplus research collect <collectionKey> --skill instagram-content-benchmark --request <input.json> --output <result.json> (input = the collection parameters).
  • Resume a pending collection: postplus research collect --run-handle <runHandle> --output <result.json>.
  • Keep the first pass bounded; expand only after inspecting the first result.
  • If the CLI returns a quote-confirmation challenge, run postplus quote confirm --json --challenge-file <challenge.json> and retry with the returned token.

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