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ig-competitor

ig-competitor is a skill for Claude Code from nicojunk/claude-ig. It costs 41 tokens per session (1,288 once invoked), scanned A, original, MIT.

A competitor-research workflow for Instagram accounts that delegates data collection and compares the findings with your own account.

In plain words
What is it for?
Use it to study three to five similar accounts and identify content hooks, formats, and gaps worth considering.
Why use it?
It reduces the manual work of choosing relevant competitors, gathering account information, and spotting differences in content performance or approach.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 23repo +2 2mo ago A scan 41 tokens original MIT

Good fit Use it to study three to five similar accounts and identify content hooks, formats, and gaps worth considering.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nicojunk/claude-ig/ig-competitor
View source ↗ nicojunk/claude-ig
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-competitor
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.

agentmods badge for ig-competitor

README.md
[![agentmods](https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-competitor/github.svg)](https://agentmods.dev/skills/nicojunk/claude-ig/ig-competitor)
Your own site
<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-competitor"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-competitor/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.

agentmods 80×15 button for ig-competitor

Your own site · 80×15
<a href="https://agentmods.dev/skills/nicojunk/claude-ig/ig-competitor"><img src="https://agentmods.dev/badge/skills/nicojunk/claude-ig/ig-competitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,288 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00041 $0.01288
Opus 5.5 $0.00016 $0.00515
Sonnet 5.5 $0.00008 $0.00258
Haiku 4.5 $0.00004 $0.00129

Measured 2mo ago against content hash b2c6a2438b33, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ig-competitor 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 2mo 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-competitor/SKILL.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

IG Competitor -- Competitor research via delegation

Key references:

  • references/competitor-framework.md -- competitor selection criteria, analysis dimensions, comparison matrix
  • references/account-baseline.md -- own account metrics for gap analysis

NOTE: This is a DELEGATION skill. The primary data collection happens via the ig-research skill. This skill orchestrates the research, reads results, and synthesizes recommendations.

Phase 1: Target Selection

Identify which competitor accounts to research:

  1. Ask the user for specific accounts, or suggest based on these criteria:
    • Same niche (fitness, nutrition, health for German-speaking audience)
    • Similar follower count range (0.5x to 5x of own account)
    • Active posting (at least 3 posts/week)
    • High engagement relative to follower count
  2. Select 3-5 target accounts. More than 5 dilutes the analysis.
  3. Load references/competitor-framework.md for the analysis dimensions to apply.
  4. Load references/account-baseline.md for own account metrics (needed for gap analysis).

Document the selected accounts with handles and reasoning for selection.

Phase 2: Delegation

For each target account, invoke the ig-research skill:

  1. Pass the account handle and the analysis dimensions from competitor-framework.md.
  2. The ig-research skill will:
    • Collect public data (post frequency, content types, engagement patterns)
    • Analyze content pillars and topic distribution
    • Document hook patterns and CTA strategies
    • Note visual style and branding approach
  3. Each research run produces a report file. Note the output paths.

If ig-research is unavailable, fall back to manual data collection using available API tools or web research tools.

Phase 3: Report Reading

Read all generated research reports:

  1. Load each competitor report file.
  2. Extract key data points per competitor:
    • Posting frequency and consistency
    • Top-performing content (by visible engagement: likes, comments)
    • Content pillar distribution
    • Hook patterns used (categorize into hook-library.md categories)
    • CTA strategies
    • Audience interaction style (comment responses, story engagement)
    • Affiliate/partnership approach

Read the full file on GitHub · 163 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.

  1. 2mo ago First seen · 163 lines · 41 tokens per session scan A b2c6a2438b33

Subscribe to this mod's changes

ig-competitor is a skill published in the GitHub repository nicojunk/claude-ig (23 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 1,288 once invoked, about $0.0002 per session on Opus 5.5. 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.

Marketing and SEOResearch
Related

ig-competitor compared with skills from other repositories

ig-competitor next to 6 skills from other repositories
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