A full Instagram account audit that collects recent account and post data, then combines several focused analyses into one report and an overall score from 0 to 100.

Use it to review recent posts, reach, saves, shares, impressions, follower growth, content quality, and format compliance, provided the required Instagram data is available.

It replaces separate checks of content, reach, growth, and formatting with one report based on data retrieved from Instagram.

ig-audit — Skill for Claude Code by nicojunk

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add nicojunk/claude-ig --skill ig-audit

Clone the repo

git clone --depth 1 https://github.com/nicojunk/claude-ig

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-audit/github.svg)](https://agentmods.dev/skills/nicojunk/claude-ig/ig-audit)

Your own site

<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-audit"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-audit/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-audit"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,506 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00045	$0.01506
Opus 5	$0.00023	$0.00753
Sonnet 5	$0.00009	$0.00301
Haiku 4.5	$0.00005	$0.00151

Measured 9d ago against content hash 4f912130b918, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

ig-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ig-audit/SKILL.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

IG Audit -- Full Instagram content audit with parallel agent delegation

Key references:

references/scoring-system.md -- metric definitions, scoring thresholds
references/account-baseline.md -- current benchmarks, historical averages
references/format-specs.md -- format requirements, dimension specs

Phase 1: Data Collection

Fetch live data via Instagram MCP tools:

Account info: get_user_info for follower count, bio, media count.
Recent posts: get_user_media for last 20-50 posts (aim for 30 minimum).
Post insights: get_post_insights for each post (reach, saves, shares, impressions).
Account insights: get_user_insights for follower growth, reach trends.

Bundle API calls in parallel where possible. Store all raw data in a structured format before proceeding.

CRITICAL: All analysis must be based on actual API data. If API calls fail, report the failure and reduce scope accordingly. Never fabricate data.

Phase 2: Agent Delegation

Spawn 6 analysis agents in parallel, each focused on a specific audit dimension. Each agent receives the collected data and its specific analysis brief.

Agent 1: Content Analysis (ig-content)

Content pillar distribution (what topics are covered, frequency)
Caption quality (length, hook presence, CTA inclusion)
Content type mix (educational, entertaining, promotional, personal)
Topic gaps and oversaturation

Agent 2: Engagement Analysis (ig-engagement)

Engagement rate by format, topic, and posting time
Save rate and share rate trends
Comment sentiment overview (positive, neutral, negative, questions)
DM generation potential (share rate as proxy)

Agent 3: Creative Analysis (ig-creative)

Visual consistency (brand colors, fonts, style)
Cover/thumbnail quality assessment
Video production quality indicators (completion rate as proxy)
Hook effectiveness by category

Agent 4: Growth Analysis (ig-growth)

Follower growth rate and trajectory
Reach-to-follower ratio trend
Viral content identification (reach >> follower count)
Audience growth sources (if available from insights)

Read the full file on GitHub · 188 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

9d ago First seen · 188 lines · 45 tokens per session scan A 4f912130b918

Subscribe to this mod's changes

ig-audit is a skill published in the GitHub repository nicojunk/claude-ig (11 stars, last pushed 2d ago), licensed MIT. It adds 45 tokens to every session and 1,506 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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