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 agentmods add commands/saigonxiii/evc/content-reviewgit clone --depth 1 https://github.com/SaigonXIII/evcWrote 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/commands/saigonxiii/evc/content-review)<a href="https://agentmods.dev/commands/saigonxiii/evc/content-review"><img src="https://agentmods.dev/badge/commands/saigonxiii/evc/content-review.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 | $0.00030 | $0.01024 |
| Opus 5 | $0.00015 | $0.00512 |
| Sonnet 5 | $0.00006 | $0.00205 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
content-review 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 5d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Review
Audit any piece of copy against the brand canon defined in brand/canon-rules.json. Works on LinkedIn posts, deck slides, email copy, website text, proposals, or any other written asset.
Usage
/content-review
[Paste copy here, or provide a file path / Google Doc URL]
Steps
1. Load the brand canon
Read the authoritative rules file:
brand/canon-rules.json
Extract and hold in memory:
banned_phrases[]— the full list with reasonsframing_red_flags[]— phrases that signal lazy or generic marketing languagerequired_language.*— product_name, company_anchor, and any other fields the user has setusps[]— the product's unique selling points (exact wording)defunct_partners[]— names that must never appearaudience— the intended buyer/readerpositioning— the one-line positioning statementcompliance_rules[<jurisdiction>]— for any jurisdiction set inconfig.env → COMPLIANCE_JURISDICTIONS
If any field is empty or missing, note it and proceed with what's available — do not invent rules.
Also read content-engine/config.md if it exists for audience segments and tone guidance.
2. Read the submitted copy
Read the full piece. If it's a file path, read it. If it's a Google Doc URL, use the docs CLI. If pasted inline, use as-is.
3. Run the audit
Check every claim, phrase, and framing decision against the loaded canon. For each issue found, note:
- The exact offending text
- The specific canon rule it violates (quote the rule)
- A suggested replacement that aligns with the canon
Banned phrases (automatic fail)
Match against banned_phrases[] in canon-rules.json. For each hit, cite the reason field verbatim.
Defunct references (automatic fail)
Match against defunct_partners[]. These are names that were once valid but no longer are — they must never appear in new content.
Framing red flags (flag and explain)
Match against framing_red_flags[]. These are lazy/generic phrases ("best in class", "revolutionary", etc.) — the user's canon may extend this list for their category.
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.
- 5d ago First seen · 123 lines · 30 tokens per session scan A 52d9e98e6e4c
content-review is a command published in the GitHub repository SaigonXIII/evc (56 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 1,024 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 commands, from other repositories
review-tool
Research a tool or product for skill-gap, architecture, and verb-fit per the Anatomy framework.
setup
First-run setup — replace the fictional company and authors with yours.
voice-check
Check a draft against the author's voice profile and flag deviations.
content-status
Scan all content directories and report status of drafts, ideas, and published content.
draft-blog
Scaffold a blog post with proper structure, voice, and frontmatter.
review-content
Review content for brand alignment, lead placement, bio consistency, and proof points.