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 indranilbanerjee/neels-plugins --skill eval-contentgit clone --depth 1 https://github.com/indranilbanerjee/neels-pluginsWrote 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/indranilbanerjee/neels-plugins/eval-content)<a href="https://agentmods.dev/skills/indranilbanerjee/neels-plugins/eval-content"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/neels-plugins/eval-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/skills/indranilbanerjee/neels-plugins/eval-content"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/neels-plugins/eval-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 | $0.00032 | $0.01825 |
| Opus 5.5 | $0.00013 | $0.00730 |
| Sonnet 5.5 | $0.00006 | $0.00365 |
| Haiku 4.5 | $0.00003 | $0.00183 |
Grade A, and why
eval-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 2d 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.
This is a copy
89% identical to eval-content — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/digital-marketing-pro:eval-content
Purpose
Comprehensive content evaluation using the full eval pipeline. Runs content through six scoring dimensions — content quality, brand voice, hallucination risk, claim verification, output structure, and readability — to produce a composite score with letter grade, flag specific issues with fix suggestions, and compare against brand quality baselines. This is the go-to command before any content goes to publication, client review, or campaign launch.
Every evaluation is logged to the quality tracker so regression detection, trend analysis, and brand-level quality reporting work continuously. If the brand has custom thresholds or dimension weights configured via /digital-marketing-pro:eval-config, those are applied automatically — otherwise industry-standard defaults are used.
Input Required
The user must provide (or will be prompted for):
- Content to evaluate: The text to score — provided inline, as a pasted block, or as a file path. Supports any marketing content format: blog post, email, ad copy, social post, landing page, press release, content brief, campaign plan, or custom
- Content type (optional): One of
blog_post,email,ad_copy,social_post,landing_page,press_release,content_brief,campaign_plan, orcustom. If omitted, the eval runner auto-detects based on content structure and length. Content type determines which built-in schema is used for structure validation and which readability benchmarks apply - Evidence file (optional): A JSON file containing verifiable claims with source data — required for full claim verification scoring. Format:
[{"claim": "...", "source": "...", "date": "...", "verified": true}]. If not provided, claim verification runs in extraction-only mode and flags all specific claims as "unverified — evidence recommended" - Schema (optional): A custom JSON schema file for structure validation — used when the content type does not match any of the 8 built-in schemas, or when the brand has a custom template that defines required sections, word counts, and formatting rules
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.
- 2d ago First seen · 64 lines · 32 tokens per session scan A 18ea2f067171
eval-content is a skill published in the GitHub repository indranilbanerjee/neels-plugins (6 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 1,825 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to eval-content, differing in 0 lines, and is treated as a copy.
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