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 skills add benchflow-ai/skillsbench --skill docxgit 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/docx)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/docx"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/docx.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 | $0.00022 | $0.01732 |
| Opus 5 | $0.00011 | $0.00866 |
| Sonnet 5 | $0.00004 | $0.00346 |
| Haiku 4.5 | $0.00002 | $0.00173 |
Grade A, and why
docx 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 3d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Word Document Manipulation with python-docx
Critical: Split Placeholder Problem
The #1 issue with Word templates: Word often splits placeholder text across multiple XML runs. For example, {{CANDIDATE_NAME}} might be stored as:
- Run 1:
{{CANDI - Run 2:
DATE_NAME}}
This happens due to spell-check, formatting changes, or Word's internal XML structure.
Naive Approach (FAILS on split placeholders)
# DON'T DO THIS - won't find split placeholders
for para in doc.paragraphs:
for run in para.runs:
if '{{NAME}}' in run.text: # Won't match if split!
run.text = run.text.replace('{{NAME}}', value)
Correct Approach: Paragraph-Level Search and Rebuild
import re
def replace_placeholder_robust(paragraph, placeholder, value):
"""Replace placeholder that may be split across runs."""
full_text = paragraph.text
if placeholder not in full_text:
return False
# Find all runs and their positions
runs = paragraph.runs
if not runs:
return False
# Build mapping of character positions to runs
char_to_run = []
for run in runs:
for char in run.text:
char_to_run.append(run)
# Find placeholder position
start_idx = full_text.find(placeholder)
end_idx = start_idx + len(placeholder)
# Get runs that contain the placeholder
if start_idx >= len(char_to_run):
return False
start_run = char_to_run[start_idx]
# Clear all runs and rebuild with replacement
new_text = full_text.replace(placeholder, str(value))
# Preserve first run's formatting, clear others
for i, run in enumerate(runs):
if i == 0:
run.text = new_text
else:
run.text = ''
return True
Best Practice: Regex-Based Full Replacement
import re
from docx import Document
def replace_all_placeholders(doc, data):
"""Replace all {{KEY}} placeholders with values from data dict."""
def replace_in_paragraph(para):
"""Replace placeholders in a single paragraph."""
text = para.text
# Find all placeholders
pattern = r'\{\{([A-Z_]+)\}\}'
matches = re.findall(pattern, text)
if not matches:
return
# Build new text with replacements
new_text = text
for key in matches:
placeholder = '{{' + key + '}}'
if key in data:
new_text = new_text.replace(placeholder, str(data[key]))
# If text changed, rebuild paragraph
if new_text != text:
# Clear all runs, put new text in first run
runs = para.runs
if runs:
runs[0].text = new_text
for run in runs[1:]:
run.text = ''
# Process all paragraphs
for para in doc.paragraphs:
replace_in_paragraph(para)
# Process tables (including nested)
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
for para in cell.paragraphs:
replace_in_paragraph(para)
# Handle nested tables
for nested_table in cell.tables:
for nested_row in nested_table.rows:
for nested_cell in nested_row.cells:
for para in nested_cell.paragraphs:
replace_in_paragraph(para)
# Process headers and footers
for section in doc.sections:
for para in section.header.paragraphs:
replace_in_paragraph(para)
for para in section.footer.paragraphs:
replace_in_paragraph(para)
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.
- 3d ago First seen · 274 lines · 22 tokens per session scan A 65c3442f5195
docx is a skill published in the GitHub repository benchflow-ai/skillsbench (1,747 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 1,732 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.
Other skills, from other repositories
template-processing
Process templates with placeholder substitution and conditional sections.
document-organization-pipeline
End-to-end pipeline for extracting, classifying, and organizing documents by subject.
xlsx-tool-operations
Common operations and patterns for using the xlsx skill tool to manipulate Excel files programmatically.
security-audit-csv-reporting
Generate structured CSV security audit reports from vulnerability data with proper filtering, formatting, and field mapping.
python-docx
Programmatically read, modify, and create Word documents (.docx) with python-docx library.
content-classification
Classify documents by subject using keyword matching and text analysis.