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 agentmods add skills/legendtkl/agentic-skill-router/skill-014npx skills add legendtkl/agentic-skill-router --skill skill-014git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-014)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-014"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-014.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 | $0.00034 | $0.00307 |
| Opus 5 | $0.00017 | $0.00153 |
| Sonnet 5 | $0.00007 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
skill-014 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 5d 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.
What it actually says
PPTX Data Extraction
Overview
Data embedded in PowerPoint presentations can provide valuable insights and facilitate analysis. This skill covers methods for extracting tables, charts, and other data elements from .pptx files.
Extracting Tables
To extract tables from a PowerPoint slide, use the following method:
from pptx import Presentation
# Load the presentation
prs = Presentation('path-to-file.pptx')
# Extract data from tables
for slide in prs.slides:
for shape in slide.shapes:
if shape.has_table:
table = shape.table
for row in range(len(table.rows)):
for col in range(len(table.columns)):
cell = table.cell(row, col)
print(cell.text)
Extracting Charts
Charts can also be extracted for further data analysis. The following example demonstrates how to get chart data:
for slide in prs.slides:
for shape in slide.shapes:
if shape.has_chart:
chart = shape.chart
data = chart.plots[0].chart_data
print(data)
Conclusion
The ability to extract data from PowerPoint presentations can enhance your analytical capabilities and enable better decision-making based on visual data representations.
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.
- 5d ago First seen · 45 lines · 34 tokens per session scan A 3de9a7e85712
skill-014 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 307 once invoked, about $0.0002 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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