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 aryaminus/socials-assistant --skill weekly-digestgit clone --depth 1 https://github.com/aryaminus/socials-assistantWrote 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/aryaminus/socials-assistant/weekly-digest)<a href="https://agentmods.dev/skills/aryaminus/socials-assistant/weekly-digest"><img src="https://agentmods.dev/badge/skills/aryaminus/socials-assistant/weekly-digest.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 | $0.00071 | $0.00675 |
| Opus 5 | $0.00036 | $0.00338 |
| Sonnet 5 | $0.00014 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
weekly-digest 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 7d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
weekly-digest — cross-platform scorecard
Reads ONLY real vault data via socials-mcp tools. Never invent or estimate numbers. If data is missing, say what's missing and suggest socials-mcp:snapshot / socials-mcp:import_tiktok_csv.
Steps
- Call
socials-mcp:profile_get(cheap) — niche and goals frame the "do next week" actions; skip silently if empty. - Call
socials-mcp:digest_datawithdays: 7. Addsocials-mcp:compare_periodsfor metrics the user cares about (views, followers_gained, reach). - Call
socials-mcp:top_content(days: 14, limit 10) andsocials-mcp:audience_overviewfor color. - Compose the digest:
## 📊 Week {week} — {start} → {end}
### Headline
One sentence: the single most important change (biggest % move, or best/worst content).
### Per platform
- **TikTok** — views {cur} ({+/-x%}), followers gained {n}, top video: "{title}" ({views} views)
- **YouTube** — views {…}, watch time {…}, traffic-mix shifts (if present)
- **Instagram** — reach {…}, top reel: "{title}" ({views} plays)
- **Facebook** — impressions {…}
Include only platforms that returned data. Never pad missing metrics with zeros.
### What worked / what died
- Best content (title, platform, views, engagement rate) + the *why* when retention/traffic data exists (e.g. "held 70% at 3s — strong hook")
- Flop content — same, plus one concrete hypothesis (hook too slow? off-niche? posting time?)
### Audience notes
Top country/age splits that changed vs prior week (if available).
### Do next week
2–3 specific, data-backed actions tied to numbers above.
- Offer follow-ups: save to
digests/, email it (self only), or hand targets to thebrand-outreachskill.
Interpretation rules
changePct: nullmeans no prior baseline — say "no baseline yet"; never compute one.- Engagement rate = (likes+comments+shares)/views. Benchmark context lives in references/benchmarks.md — read it when comparing rates.
- Viral outliers distort averages: call them out explicitly instead of averaging them away.
- TikTok retention/traffic exist only if CSVs were imported; if absent, remind about the weekly export →
import_tiktok_csv. - Keep the whole digest under ~400 words; link or offer detail rather than dumping tables.
What ships with it
1 file 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.
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.
- 7d ago First seen · 51 lines · 71 tokens per session scan A f4bf8712f7b6
weekly-digest is a skill published in the GitHub repository aryaminus/socials-assistant (0 stars, last pushed 2d ago), licensed MIT. It adds 71 tokens to every session and 675 once invoked, about $0.0004 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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