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 seranking-planable/smm-skills --skill cross-client-metrics-overviewgit clone --depth 1 https://github.com/seranking-planable/smm-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/seranking-planable/smm-skills/cross-client-metrics-overview)<a href="https://agentmods.dev/skills/seranking-planable/smm-skills/cross-client-metrics-overview"><img src="https://agentmods.dev/badge/skills/seranking-planable/smm-skills/cross-client-metrics-overview/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/seranking-planable/smm-skills/cross-client-metrics-overview"><img src="https://agentmods.dev/badge/skills/seranking-planable/smm-skills/cross-client-metrics-overview.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 | $0.00124 | $0.01096 |
| Opus 5 | $0.00062 | $0.00548 |
| Sonnet 5 | $0.00025 | $0.00219 |
| Haiku 4.5 | $0.00012 | $0.00110 |
Grade A, and why
cross-client-metrics-overview 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 11d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-client metrics overview
Compare social media performance across multiple Planable workspaces in one view. Built for agency operators and managers who need to see the full picture without opening each workspace individually.
How this skill works
- Identify the workspaces and time period to cover
- Fetch page and post metrics for each workspace
- Produce a ranked, comparative summary
- Flag outliers — best performers, problem accounts, accounts needing attention
Step 1: Gather inputs
- Workspaces to include — specific clients, or all workspaces (default: all)
- Date range — default to previous calendar month if not specified
- Primary metric to rank by — engagement rate (default), impressions, posts published, or audience growth
If the user asks for "all clients", proceed — but if there are more than 15 workspaces, confirm before fetching them all.
Step 2: Fetch data for each workspace
For each workspace:
list_pages(workspaceId) → get all pageIds
get_page_metrics_summary(
workspaceId,
pageIds[],
startDate,
endDate
)
get_post_metrics_summary(
workspaceId,
pageIds[],
startDate,
endDate
)
Store results keyed by workspace name. Handle failures gracefully — if a workspace returns no data, note it rather than stopping the whole report.
Step 3: Build the comparison
At a glance — ranked table
Rank workspaces by the primary metric. Show as a clean list:
Agency performance — [Month Year]
Ranked by engagement rate
1. [Client A] Eng. rate: 4.2% Impressions: 48K Posts: 22
2. [Client B] Eng. rate: 3.8% Impressions: 31K Posts: 18
3. [Client C] Eng. rate: 2.1% Impressions: 62K Posts: 30
...
Include totals row at the bottom: total impressions, total engagement, total posts across all clients.
Highlights and flags
After the table, add a short section with 3–5 specific callouts:
🏆 Top performer — [Client] led on [metric] with [number]. [One sentence on why if inferable — e.g., "Ran 2 campaigns this month."]
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.
- 11d ago First seen · 118 lines · 124 tokens per session scan A b92f7d62b3b0
cross-client-metrics-overview is a skill published in the GitHub repository seranking-planable/smm-skills (3 stars, last pushed 2mo ago), licensed MIT. It adds 124 tokens to every session and 1,096 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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