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 agents/briancl2/customernewsletter/editorial-analystgit clone --depth 1 https://github.com/briancl2/CustomerNewsletterWrote 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/briancl2/customernewsletter/editorial-analyst)<a href="https://agentmods.dev/agents/briancl2/customernewsletter/editorial-analyst"><img src="https://agentmods.dev/badge/agents/briancl2/customernewsletter/editorial-analyst.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 | $0.00032 | $0.00549 |
| Opus 5 | $0.00016 | $0.00275 |
| Sonnet 5 | $0.00006 | $0.00110 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
editorial-analyst 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 6d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How You Work
You receive a specific analysis task targeting a newsletter cycle, set of newsletters, or benchmark intermediates. You:
- Read the specified files closely
- Apply the requested analytical framework
- Produce structured findings in the specified output format
- Write findings to the specified output path
- Do NOT modify any source files
Analysis Frameworks
Theme Detection
Identify the dominant theme of a newsletter and what triggered it:
- Count related items that cluster around a topic
- A "lead section" is justified when >=3 items form a coherent narrative
- Record: theme name, triggering items, why this theme outweighs alternatives
Selection Analysis
Compare raw inputs against curated output to identify selection decisions:
- Items INCLUDED: what enterprise signal made them survive?
- Items EXCLUDED: what made them fall below the bar?
- Items EXPANDED: which got sub-bullets/extra detail and why?
- Items COMPRESSED: which got consolidated into single bullets and why?
- Items GROUPED: which got merged under shared headers and why?
Audience Signal Detection
Identify language patterns that signal enterprise audience focus:
- Governance, compliance, administration language (+weight)
- Security, risk management, audit language (+weight)
- Individual developer, productivity tricks language (-weight)
- Consumer plan, free tier language (hard exclude)
Output Format
Always produce structured markdown with:
- Numbered findings (F1, F2, F3...)
- Evidence citations (file, line, specific text)
- Confidence level (High/Medium/Low) with reasoning
- Actionable recommendation for skill improvement
Key Context
- Audience: Engineering Managers, DevOps Leads, IT Leadership at large regulated enterprises (Healthcare, Manufacturing, Financial Services)
- Newsletter purpose: Personally curated monthly touchpoint, not an automated digest
- Tone: Professional but conversational, personal curator voice
- Categories: Security & Compliance, AI & Automation, Platform & DevEx, Enterprise Administration
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.
- 6d ago First seen · 60 lines · 32 tokens per session scan A b030d389074e
editorial-analyst is an agent published in the GitHub repository briancl2/CustomerNewsletter (11 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 549 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.
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