weekly-digest
Skill0 starsChanged 3 months agoMITView on GitHub
Generates a cross-platform weekly analytics digest (TikTok, Instagram, Facebook, YouTube) from real vault data with wins, losses, and next actions. Use when the user asks for a digest, weekly report, scorecard, performance summary, "how did I do this week", or what to do next with their content.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- reatlat/fullstory-claude-plugintheir own version, first seen Oct 30, 2026
- glebis/claude-skillstheir own version, first seen Jul 1, 2026
- JAVICHILEON/claude---Skillstheir own version, first seen Jul 1, 2026
- chengkj99/kj-skillstheir own version, first seen Jun 26, 2026
- aiflaredev/aiflaretheir own version, first seen May 16, 2026
- composio-community/support-skillstheir own version, first seen Apr 10, 2026
Used with
Projects with it also use session-search.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
KISHOR403Skillsession-search
This skill should be used when searching Claude Code session transcripts with semantic understanding. Triggers on queries like "find sessions about X", "when did I work on Y", "search previous conversations". Supports natural language queries with synonym matching.
Also in 3 projects that use this
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- glebis/claude-skills★ 410same name
- reatlat/fullstory-claude-plugin★ 36same name
- composio-community/support-skills★ 18same name
- chengkj99/kj-skills★ 14same name
- aiflaredev/aiflare★ 0same name
- JAVICHILEON/claude---Skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- llms.txtllms.txt
- brand-outreachSkill
- media-kitSkill
- publish-packageSkill
- script-reviewSkill
- socials-connectSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
Call socials-mcp:profile_get (cheap) — niche and goals frame the "do next week" actions.
- Very specific: line 14
Call socials-mcp:digest_data with days: 7.
- Very specific: line 15
Call socials-mcp:top_content (days: 14, limit 10) and socials-mcp:audience_overview for color.
- Distinctive: line 50
Keep the whole digest under ~400 words.
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

