CustomerNewsletter: Skill for Claude Code

.github/skills/editorial-review/SKILL.md

editorial-review is a skill for Claude Code, Codex from briancl2/CustomerNewsletter. It costs 63 tokens per session (1,226 once invoked), scanned A, original, MIT.

A newsletter editing step that applies a human reviewer’s corrections to an existing newsletter and related editorial records.

In plain words
What is it for?
Use it after a reviewer has supplied corrections to update the newsletter, editorial intelligence, and LEARNINGS.md.
Why use it?
It removes the need to make the same corrections separately in the newsletter, guidance files, and learning log.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

This is briancl2/CustomerNewsletter's own configuration. It tells Claude Code and Codex how to work on CustomerNewsletter itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything CustomerNewsletter configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is - [Editorial Intelligence](../../../reference/editorial-intelligence.md) - Theme detection, selection weights, treatment patterns.

Reuse

Borrowing it

Nothing to install: this file belongs to briancl2/CustomerNewsletter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/briancl2/CustomerNewsletter/main/.github/skills/editorial-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/briancl2/CustomerNewsletter

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for editorial-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/briancl2/customernewsletter/editorial-review/github.svg)](https://agentmods.dev/skills/briancl2/customernewsletter/editorial-review)
Your own site
<a href="https://agentmods.dev/skills/briancl2/customernewsletter/editorial-review"><img src="https://agentmods.dev/badge/skills/briancl2/customernewsletter/editorial-review/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.

agentmods 80×15 button for editorial-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/briancl2/customernewsletter/editorial-review"><img src="https://agentmods.dev/badge/skills/briancl2/customernewsletter/editorial-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,226 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00063 $0.01226
Opus 5 $0.00032 $0.00613
Sonnet 5 $0.00013 $0.00245
Haiku 4.5 $0.00006 $0.00123

Measured 11d ago against content hash 74ab55533eeb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

editorial-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 11d 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.

.github/skills/editorial-review/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Editorial Review Loop

Apply human editorial corrections to a Phase 4 newsletter and produce an updated version.

Quick Start

  1. Read the corrections file at workspace/YYYY-MM_editorial_corrections.md
  2. Read the current newsletter at output/YYYY-MM_month_newsletter.md
  3. Read reference/editorial-intelligence.md for calibrated rules
  4. Apply each correction to the newsletter
  5. Update intelligence files with new patterns
  6. Validate the updated newsletter
  7. Capture learnings in LEARNINGS.md

Inputs

  • Corrections File: workspace/YYYY-MM_editorial_corrections.md (required)
  • Current Newsletter: output/YYYY-MM_month_newsletter.md (required)
  • Editorial Intelligence: reference/editorial-intelligence.md (context)
  • Source Intelligence: reference/source-intelligence/meta-analysis.md (context)

Output

  • Updated Newsletter: output/YYYY-MM_month_newsletter.md (overwrite in place)
  • Updated Intelligence: Changes to reference/editorial-intelligence.md if corrections reveal new patterns
  • New Learnings: Appended to LEARNINGS.md

Corrections File Format

The human writes corrections to workspace/YYYY-MM_editorial_corrections.md. The required structure, sections, and per-item table format are defined in the single authoritative spec: correction-format.md. Use that document as the source of truth for how to structure and label all corrections.

When running this skill, assume the corrections file strictly follows references/correction-format.md and treat any deviations as format errors to be surfaced back to the human editor.

  • Item name, what to do with it (remove / compress / bundle)

Bundling Corrections

[Items that should be combined or separated] | Bundle | Items | Treatment |

Depth Corrections

[Items that need more or less detail]

  • Item name: expand / compress, what detail to add/remove

Tone/Framing Corrections

[Narrative or framing changes needed]


## Core Workflow

### Step 1: Parse Corrections

Read the corrections file and categorize each correction:

| Type | Action |
|------|--------|
| **Theme correction** | Rewrite lead section with new theme |
| **Missing item** | Add item with source URL and appropriate detail |
| **Wrong item** | Remove, compress, or bundle as specified |
| **Bundling correction** | Combine items or split them per instructions |
| **Depth correction** | Expand or compress specific items |
| **Tone/framing** | Rewrite affected sections |

### Step 2: Apply Corrections to Newsletter

For each correction, modify the newsletter in place:

1. **Theme changes**: Replace the lead section title, framing paragraph, and reorganize lead items
2. **Additions**: Insert new items in the appropriate section, following the content-format-spec
3. **Removals**: Delete the item entirely or compress into a bundle
4. **Bundling**: Merge items into a single bullet with sub-items, or split a bundle into separate bullets
5. **Depth changes**: Expand with sub-bullets or compress to a single line
6. **Tone changes**: Rewrite the affected paragraph(s)

### Step 3: Validate Updated Newsletter

Run validation:
```bash
bash .github/skills/newsletter-validation/scripts/validate_newsletter.sh output/YYYY-MM_month_newsletter.md

Check:

  • All corrections applied (count corrections vs changes made)
  • Validation passes (0 errors)
  • Line count in expected range (100-170 lines)
  • No new forbidden patterns introduced
  • All new items have source URLs

Step 4: Update Intelligence Files

If corrections reveal new editorial patterns, update:

  1. reference/editorial-intelligence.md: Add new theme triggers, adjust selection weights, add expansion/compression rules
  2. reference/source-intelligence/meta-analysis.md: Note new source gaps or patterns
  3. Selection criteria: If the correction shows a weight was miscalibrated

Only update when the correction reveals a PATTERN, not a one-off preference.

Read the full file on GitHub · 134 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 11d ago First seen · 134 lines · 63 tokens per session scan A 74ab55533eeb

Subscribe to this mod's changes

editorial-review is a skill published in the GitHub repository briancl2/CustomerNewsletter (11 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,226 once invoked, about $0.0003 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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