SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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/benchflow-ai/skillsbench/text-parsernpx skills add benchflow-ai/skillsbench --skill text-parsergit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/text-parser)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/text-parser"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/text-parser.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 | $0.00011 | $0.00248 |
| Opus 5 | $0.00005 | $0.00124 |
| Sonnet 5 | $0.00002 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
text-parser 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- text-parser — 100% identical, 0 lines differ
What it actually says
Text Parser Skill
Overview
Parse structured text files to extract data for filling PDFs.
Key-Value Parsing
def parse_input(text):
"""Parse key-value pairs from text."""
data = {}
for line in text.strip().split('\n'):
if ':' in line:
# Remove leading dash/bullet if present
line = line.lstrip('- ').strip()
key, value = line.split(':', 1)
data[key.strip()] = value.strip()
return data
# Usage
with open("input.txt") as f:
content = f.read()
data = parse_input(content)
# data["Name"] -> "John Smith"
# data["Email"] -> "[email protected]"
Common Input Formats
- Name: John Smith
- Email: [email protected]
- Phone: 555-1234
Or without dashes:
Name: John Smith
Email: [email protected]
Tips
- Read the entire input file first
- Match field names to PDF labels
- Handle special instructions
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.
- 2d ago First seen · 54 lines · 11 tokens per session scan A 42da49f3d8f0
text-parser is a skill published in the GitHub repository benchflow-ai/skillsbench (1,747 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 248 once invoked, about $0.0001 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 2026-09-03.
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