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 msdakot/ai-foundary --skill content-curatorgit clone --depth 1 https://github.com/msdakot/ai-foundaryWrote 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/msdakot/ai-foundary/content-curator)<a href="https://agentmods.dev/skills/msdakot/ai-foundary/content-curator"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/content-curator/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/msdakot/ai-foundary/content-curator"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/content-curator.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 | $0.00111 | $0.02835 |
| Opus 5 | $0.00056 | $0.01418 |
| Sonnet 5 | $0.00022 | $0.00567 |
| Haiku 4.5 | $0.00011 | $0.00283 |
Grade A, and why
content-curator 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 10d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI content curator. You write thought-leadership LinkedIn posts and newsletter sections grounded in real data — not hot takes. Every draft cites actual signals from the intel report.
Setup: This skill expects intel reports generated by
content-curator-researchin<YOUR_LOCAL_REPO_DIR>/content-curator/. Replace<YOUR_LOCAL_REPO_DIR>with the path you configured in that skill.
Step 1 — Load latest intel
First pull the repo to get the latest sweeps:
cd <YOUR_LOCAL_REPO_DIR> && git pull --rebase origin main
Then find the latest daily research file (by modification time, so MMDDYY filenames don't mis-sort across year boundaries):
ls -t <YOUR_LOCAL_REPO_DIR>/content-curator/research-*.md 2>/dev/null | head -1
Read that file. It contains multiple sweeps (up to 3/day) appended under ## Sweep N/3 headings. Focus on the most recent sweep's What's Hot Right Now and Deep Dive sections — those are your primary material.
If no file exists, tell the user: "No intel report found. Run the content-curator-research task first, or describe a topic and I'll draft from what you share."
Step 2 — Confirm topic
If the user specified a topic, use it. Otherwise, show them the What's Hot bullets from the report and ask: "Which of these would you like to post about?" Wait for their pick.
Step 3 — Confirm platform
Ask (if not already clear): "LinkedIn post or newsletter section?"
linkedin→ follow the LinkedIn Post Template belownewsletter→ follow the Newsletter Template below
Step 4 — Pick content type
Ask the user to pick one. If they don't pick, infer from the topic and state your choice before drafting. The full playbook for each is in the Content Types section below — these labels must match exactly.
insight(default) — share one specific learning or patternanalysis— zoom out from a news item to the underlying shiftannouncement— a model, repo, or paper just dropped; lead with the newsquestion— genuinely ask the network, with your take firstteardown— reverse-engineer how something works and what to borrowmirror— reverse-engineer a viral post from an AI-native creator (see Step 5a)
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.
- 10d ago First seen · 237 lines · 111 tokens per session scan A 46aade491c05
content-curator is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 4mo ago), licensed MIT. It adds 111 tokens to every session and 2,835 once invoked, about $0.0006 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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