Borrowing it
Nothing to install: this file belongs to vvselijah/Claudegram. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vvselijah/Claudegram/main/.claude/skills/plan-my-content/SKILL.mdgit clone --depth 1 https://github.com/vvselijah/ClaudegramWrote 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/vvselijah/claudegram/plan-my-content)<a href="https://agentmods.dev/skills/vvselijah/claudegram/plan-my-content"><img src="https://agentmods.dev/badge/skills/vvselijah/claudegram/plan-my-content/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/vvselijah/claudegram/plan-my-content"><img src="https://agentmods.dev/badge/skills/vvselijah/claudegram/plan-my-content.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 | $0.00087 | $0.00709 |
| Opus 5 | $0.00044 | $0.00354 |
| Sonnet 5 | $0.00017 | $0.00142 |
| Haiku 4.5 | $0.00009 | $0.00071 |
Grade A, and why
plan-my-content 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan my content (data-grounded)
Use the user's actual numbers, never generic advice. Workflow:
1. Refresh, then load the data
- Run
python refresh.pyfirst (unless they just did) so the numbers are current. - Read
data/stats.mdfor the quick read, then parsedata/data.js(window.DASHBOARD_DATA = {…};) for detail. Key fields per post:insights.views / reach / saved / shares / total_interactions,ig_reels_avg_watch_time,reels_skip_rate(0..1),like_count,comments_count,category,media_product_type,timestamp. Account-level:daily(reach, views split by follower/non-follower),online_followers(best times),demographics(who follows them).
2. Score by what 2026 actually rewards (NOT likes / follower count)
Rank content by these, per post, normalized to reach where it makes sense:
- Watch-time / retention (
ig_reels_avg_watch_time) — the #1 reels lever. - Skip-rate (
reels_skip_rate, lower is better) — a strong negative signal. - Sends / shares per reach (
shares / reach) — a share is a recommendation; weighted far above a like. - Saves per reach (
saved / reach) — reference value; drives re-surfacing. - Use likes/comments only as secondary context. A post with big likes but a high skip-rate or low watch-time is NOT a winner — say so plainly.
3. Find the levers
- Under-supplied winners: categories with high views-per-post but a low post count → tell them to make more of that.
- Over-supplied losers: categories they post a lot that under-return → cut back.
- Best time to post: read
online_followers(hour-of-day histogram). - Reach vs follower split: if most
views_non_followers>>views_followers, they're in discovery mode — lean into broad hooks; if reversed, they're preaching to existing followers — push shareable/save-worthy content to break out. - Who they reach vs who follows: compare reached behavior to
demographics.
4. Deliver
Give them, concretely:
- A 2–3 line honest read of what's working and what isn't (with the real numbers).
- The single highest-leverage change.
- 2–3 specific next-post ideas in THEIR voice, each with a hook line, the format (reel/carousel/photo), and why the data supports it.
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 · 49 lines · 87 tokens per session scan A 6a46e8da4f6b
plan-my-content is a skill published in the GitHub repository vvselijah/Claudegram (51 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 709 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 2026-08-30.
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