content-qa

content-qa is a skill for Claude Code, Codex from Glad-Labs/poindexter. It costs 84 tokens per session (10,306 once invoked), scanned A, original, Apache-2.0.

A quality-assurance pack for checking written content before publication. It includes reviews for topic coverage, contradictions, consistency, writing quality, and the treatment of images or rendered previews.

In plain words
What is it for?
Use it to review drafts, test whether an article answers its topic, check internal consistency, score quality, critique rewrites, and inspect inline images or screenshots.
Why use it?
It catches gaps and inconsistencies that a single proofreading pass may miss. It also provides structured feedback and rewrite checks before content is published.

Skill for Claude CodeCodex

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

Good fit Use it to review drafts, test whether an article answers its topic, check internal consistency, score quality, critique rewrites, and inspect inline images or screenshots.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/glad-labs/poindexter/content-qa
Install

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.

Any agent
npx skills add Glad-Labs/poindexter --skill content-qa
Clone the repo
git clone --depth 1 https://github.com/Glad-Labs/poindexter

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 content-qa

README.md
[![agentmods](https://agentmods.dev/badge/skills/glad-labs/poindexter/content-qa/github.svg)](https://agentmods.dev/skills/glad-labs/poindexter/content-qa)
Your own site
<a href="https://agentmods.dev/skills/glad-labs/poindexter/content-qa"><img src="https://agentmods.dev/badge/skills/glad-labs/poindexter/content-qa/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 content-qa

Your own site · 80×15
<a href="https://agentmods.dev/skills/glad-labs/poindexter/content-qa"><img src="https://agentmods.dev/badge/skills/glad-labs/poindexter/content-qa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,306 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00084 $0.10306
Opus 5.5 $0.00034 $0.04122
Sonnet 5.5 $0.00017 $0.02061
Haiku 4.5 $0.00008 $0.01031

Measured 20d ago against content hash de356fcf10b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

content-qa 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 20d 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.

src/cofounder_agent/skills/content/content-qa/SKILL.md · 876 lines

How it starts

The opening of the file, as written. The whole thing — 876 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content QA skill

The adversarial quality-assurance pack — the QA moat. These prompts drive the multi-model QA stage: topic-delivery and internal-consistency gates, the publication-readiness critic, the aggregate-rewrite pass, writer self-review for contradictions, the self-consistency sampling rail, an LLM quality rubric, and the two vision-QA prompts. The architect routes on the description above; UnifiedPromptManager resolves each template by key (Langfuse override still wins over the bodies below).

Default prompts — basic but functional; production-quality prompt packs ship as a premium add-on.

qa.topic_delivery

You are a strict editor checking whether an article
delivers on its topic. A reader clicking this article expects what the topic
promises. Did the writer deliver?

REQUESTED TOPIC: {topic}

ARTICLE OPENING (first ~1000 words):
{opening}

Check these specific failure modes:

  1. Numeric promises. If the topic says "10 X" or "11 Y" or "5 Z", does the
     body actually list that many? Partial lists (two items then a pivot to
     generalities) FAIL.
  2. Named entities. If the topic names a specific product, person, or
     technology ("Llama 4", "Claude", "indie hackers making $1M+"), does the
     body actually discuss that specific thing? An article titled "Llama 4"
     that only discusses Llama 3.1 FAILS.
  3. Format promise. If the topic implies a guide, tutorial, list, or review,
     does the body deliver that format? A "guide" that's actually an opinion
     piece FAILS.
  4. Angle/thesis. Is the article's thesis actually about the topic, or did
     the writer pivot to a tangential point they preferred?

Respond with ONLY valid JSON:
{{"delivers": true/false, "score": NUMBER 0-100, "reason": "concise — name the specific gap when one exists"}}

Scoring guidance: delivers=true and score 85-100 if the body is a faithful
execution of the topic. delivers=false and score 0-40 if the body is a
bait-and-switch or numeric underdelivery or misnamed version. delivers=true
and score 60-80 if the body is mostly on-topic but weaker than the topic
implies.

Read the full file on GitHub · 876 lines

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. 20d ago Changed · +32 lines de356fcf10b9
  2. 22d ago Changed · +30 lines 136008bbb445
  3. 24d ago Changed · +129 lines 94c5f5eba1b1
  4. 26d ago Changed · +10 lines b73b3915ac64
  5. 27d ago Changed · -76 lines a4c58df51bed
  6. 28d ago Changed · +100 lines 10e03115692f
  7. 1mo ago Changed · +10 lines 3f405dffa027
  8. 1mo ago Changed · +4 lines 8bdcba15dfb1
  9. 1mo ago Changed · +41 lines af2f60b27e2e
  10. 1mo ago First seen · 596 lines · 84 tokens per session scan A cd4cd7d64f04

Subscribe to this mod's changes

content-qa is a skill published in the GitHub repository Glad-Labs/poindexter (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 84 tokens to every session and 10,306 once invoked, about $0.0003 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 2026-08-31.

Related

content-qa compared with skills from other repositories

content-qa next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
content-qaGlad-Labs/poindexterSkill7yesterdayApache-2.0A84Claude Code, Codex
datamodel-code-generatorkoxudaxi/datamodel-code-generatorSkill4.0k6d agoMITA147Claude Code, Codex
linkedin-employee-advocacysergebulaev/linkedin-skillsSkill5.7k4d agoMITA96Claude Code
company-braincaura-ai/cauraSkill552todayApache-2.0A108Claude Code
secops-investigateTracecatHQ/tracecatSkill3.9ktodayAGPL-3.0A116Claude Code, Codex
aws-incident-responseTracecatHQ/tracecatSkill3.9ktodayAGPL-3.0A34Claude Code, Codex
blog-multilingualAgriciDaniel/claude-blogSkill2.7k4d agoMITC130Claude Code