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.
npx skills add nicojunk/claude-ig --skill ig-analyzegit clone --depth 1 https://github.com/nicojunk/claude-igWrote 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.
[](https://agentmods.dev/skills/nicojunk/claude-ig/ig-analyze)<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-analyze"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-analyze/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.
<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-analyze"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00037 | $0.01279 |
| Opus 5 | $0.00018 | $0.00639 |
| Sonnet 5 | $0.00007 | $0.00256 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
ig-analyze 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IG Analyze -- API-driven Instagram performance analysis
Key references:
references/account-baseline.md-- current benchmarks, follower count, historical averagesreferences/scoring-system.md-- metric definitions, scoring thresholds, weighted formulas
CRITICAL RULE: This skill MUST use live API data. Never guess, estimate, or fabricate metrics. If an API call fails, report the failure. Do not fill in placeholder numbers.
Phase 1: Data Collection
Pull data using Instagram MCP tools in this order:
-
Account overview:
mcp__claude_ai_Instagram_MCP__get_user_infoCapture: follower count, following count, media count, bio, profile pic.
-
Recent media:
mcp__claude_ai_Instagram_MCP__get_user_mediaFetch the most recent 25 posts. For each, capture: id, type (carousel/reel/image), timestamp, caption, like count, comment count.
-
Post-level insights (for each post):
mcp__claude_ai_Instagram_MCP__get_post_insightsCapture: reach, impressions, saves, shares, video views (if reel), profile visits.
-
Account-level insights:
mcp__claude_ai_Instagram_MCP__get_user_insightsCapture: reach, impressions, follower growth, profile visits over the requested period.
Bundle independent API calls in parallel where possible. Do not make serial calls when parallel is available.
Phase 2: Metric Calculation
Calculate the following metrics from the collected data:
Per-Post Metrics
| Metric | Formula |
|---|---|
| Engagement Rate | (likes + comments + saves + shares) / reach * 100 |
| Save Rate | saves / reach * 100 |
| DM-Send Rate | shares / reach * 100 |
| Completion Rate (Reels) | average watch time / duration * 100 |
| Reach-to-Follower Ratio | reach / follower_count * 100 |
Account-Level Metrics
| Metric | Formula |
|---|---|
| Average Engagement Rate | mean of per-post engagement rates |
| Save Rate Trend | save rate comparison: last 7 posts vs prior 7 |
| Reach Growth | (current period reach - prior period reach) / prior * 100 |
| Content Mix | % reels vs % carousels vs % single images |
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 · 157 lines · 37 tokens per session scan A 9484b8b053d6
ig-analyze is a skill published in the GitHub repository nicojunk/claude-ig (11 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 1,279 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…