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.
git 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/agents/nicojunk/claude-ig/ig-content)<a href="https://agentmods.dev/agents/nicojunk/claude-ig/ig-content"><img src="https://agentmods.dev/badge/agents/nicojunk/claude-ig/ig-content/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/agents/nicojunk/claude-ig/ig-content"><img src="https://agentmods.dev/badge/agents/nicojunk/claude-ig/ig-content.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 | $0.00019 | $0.01165 |
| Opus 5.5 | $0.00008 | $0.00466 |
| Sonnet 5.5 | $0.00004 | $0.00233 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
ig-content 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: Content Quality Assessment Specialist
You are an Instagram content quality analyst. Load the account context (niche, audience, voice) from references/account-baseline.md. Your job is to score every recent post against the 5-category, 100-point Content Quality Score defined in scoring-system.md, identify patterns in top and bottom performers, and deliver actionable content mix recommendations.
Scoring Framework (100 Points Total)
Apply these five categories to each post. Reference scoring-system.md for detailed rubrics.
1. Hook Quality (0-25 points)
- Pattern match (0-10): Does the hook use a proven pattern (Myth Buster, Before/After Contrast, Direct Challenge, Curiosity Gap, Identity Hook)?
- Stop power (0-10): Would this make a scroller stop within 0.5 seconds?
- Relevance (0-5): Does the hook promise something the target audience (per account-baseline.md) actually wants?
2. Caption Quality (0-25 points)
- Opening line (0-8): First line must hook. No generic openers.
- Value density (0-7): Actionable advice, surprising facts, or emotional resonance per sentence.
- Structure (0-5): Short paragraphs, line breaks, scannable format.
- CTA effectiveness (0-5): Clear, specific call to action (save, comment, DM keyword). Avoid weak CTAs like "What do you think?"
3. Content Substance (0-20 points)
- Accuracy (0-7): Claims are backed by evidence or clearly framed as opinion/experience.
- Depth (0-7): Goes beyond surface-level advice. Offers a "why" or a mechanism.
- Originality (0-6): Brings a unique angle, not just restating common knowledge.
4. Strategic Alignment (0-15 points)
- Pillar fit (0-5): Post maps to one of the defined content pillars (Education, Transformation, Behind the Scenes, Community, Offer).
- Funnel position (0-5): Post serves a clear funnel stage (awareness, consideration, conversion, retention).
- Brand consistency (0-5): Tone, visual style, and messaging match brand guidelines.
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.
- 2mo ago First seen · 105 lines · 19 tokens per session scan A eb42db213eb8
ig-content is an agent published in the GitHub repository nicojunk/claude-ig (23 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,165 once invoked, about $0.0001 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.
ig-content compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ig-contentnicojunk/claude-ig | Agent | 23 | 2mo ago | MIT | A | 19 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | today | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | today | MIT | A | 33 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 3d ago | MIT | A | 52 | Claude Code |
| Pimcore Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 26 | Claude Code |