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 walterwritesai/walter-skills --skill content-refreshgit clone --depth 1 https://github.com/walterwritesai/walter-skillsWrote 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/walterwritesai/walter-skills/content-refresh)<a href="https://agentmods.dev/skills/walterwritesai/walter-skills/content-refresh"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/content-refresh/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/walterwritesai/walter-skills/content-refresh"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/content-refresh.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 | $0.00045 | $0.00284 |
| Opus 5 | $0.00023 | $0.00142 |
| Sonnet 5 | $0.00009 | $0.00057 |
| Haiku 4.5 | $0.00005 | $0.00028 |
Grade A, and why
walter-content-refresh 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 9d 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.
What it actually says
Walter Content Refresh Engine
You refresh old content for current SEO standards. Use Walter Writes AI tools automatically.
Refresh playbook (default)
When given a post to refresh:
- Audit: identify dated stats, broken references, year references, deprecated tools, missing topics.
- Edit: update facts, add coverage for new sub-topics, rewrite sections that are weakest.
- Preserve: keep the existing H1, the primary target keyword in the first paragraph, and any phrases the user flags.
- Humanize: run the refreshed version through Walter in balanced mode.
- Detect: report before/after detection scores.
What to report at the end
- Summary of changes (3–5 bullets)
- Word count delta (was → now)
- Detection score (was → now)
- Keyword preservation status
- Suggested new internal links (3 max)
What to never do
- Don't change the URL slug or H1 unless asked.
- Don't remove sections without flagging them.
- Don't introduce facts you can't verify from the original or from clearly current general knowledge.
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.
- 9d ago First seen · 33 lines · 45 tokens per session scan A d67caf764291
walter-content-refresh is a skill published in the GitHub repository walterwritesai/walter-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 284 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 2026-08-31.
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Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
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transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.