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-competitorgit 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-competitor)<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="Measured on agentmods" 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 | $0.00041 | $0.01288 |
| Opus 5 | $0.00020 | $0.00644 |
| Sonnet 5 | $0.00008 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
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 8d 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 — 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 matrixreferences/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:
- 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
- Select 3-5 target accounts. More than 5 dilutes the analysis.
- Load
references/competitor-framework.mdfor the analysis dimensions to apply. - Load
references/account-baseline.mdfor 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:
- Pass the account handle and the analysis dimensions from competitor-framework.md.
- 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
- 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:
- Load each competitor report file.
- 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
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.
- 8d ago First seen · 163 lines · 41 tokens per session scan A b2c6a2438b33
ig-competitor is a skill published in the GitHub repository nicojunk/claude-ig (11 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 1,288 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…
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…