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 Zaoqu-Liu/ScienceClaw --skill export-docxgit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote 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/zaoqu-liu/scienceclaw/export-docx)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/export-docx"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/export-docx/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/zaoqu-liu/scienceclaw/export-docx"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/export-docx.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 | $0.00061 | $0.01424 |
| Opus 5 | $0.00030 | $0.00712 |
| Sonnet 5 | $0.00012 | $0.00285 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
export-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 6d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Export to Word (.docx)
Convert a ScienceClaw project's report and figures into a formatted Word document using python-docx.
When to Use
- User says "/export word", "导出 Word", "转 docx", "生成 Word 报告"
- User wants to share findings with collaborators who use Word
- User needs a formatted document for submission or review
Workflow
- Identify the project directory from ACTIVE_PROJECT.md or the most recent project
- Read the main report from
reports/directory (markdown) - Collect figures from
figures/directory - Read METHODS.md if present
- Generate .docx using python-docx with proper formatting
- Save to
reports/<project_name>_report.docx
Code Template
pip install -q python-docx Pillow 2>/dev/null && python3 << 'DOCXEOF'
import os, re, glob
from docx import Document
from docx.shared import Inches, Pt, Cm, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.style import WD_STYLE_TYPE
PROJECT_DIR = os.path.expanduser("PROJECT_DIR_PLACEHOLDER")
REPORT_FILE = "REPORT_FILE_PLACEHOLDER"
OUTPUT_FILE = os.path.join(PROJECT_DIR, "reports", "OUTPUT_NAME_PLACEHOLDER.docx")
doc = Document()
# --- Page setup ---
section = doc.sections[0]
section.top_margin = Cm(2.54)
section.bottom_margin = Cm(2.54)
section.left_margin = Cm(3.18)
section.right_margin = Cm(3.18)
# --- Styles ---
style = doc.styles['Normal']
font = style.font
font.name = 'Times New Roman'
font.size = Pt(11)
font.color.rgb = RGBColor(0x33, 0x33, 0x33)
style.paragraph_format.line_spacing = 1.5
style.paragraph_format.space_after = Pt(6)
for level in range(1, 4):
hs = doc.styles[f'Heading {level}']
hs.font.name = 'Arial'
hs.font.color.rgb = RGBColor(0x3C, 0x54, 0x88)
hs.font.size = Pt(16 - level * 2)
hs.font.bold = True
# --- Read report markdown ---
report_path = os.path.join(PROJECT_DIR, "reports", REPORT_FILE)
with open(report_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
fig_dir = os.path.join(PROJECT_DIR, "figures")
# --- Parse and convert ---
for line in lines:
line = line.rstrip('\n')
# Headings
if line.startswith('### '):
doc.add_heading(line[4:], level=3)
elif line.startswith('## '):
doc.add_heading(line[3:], level=2)
elif line.startswith('# '):
doc.add_heading(line[2:], level=1)
# Figure references
elif '![' in line:
m = re.search(r'!\[.*?\]\((.*?)\)', line)
if m:
img_path = m.group(1)
if not os.path.isabs(img_path):
img_path = os.path.join(PROJECT_DIR, img_path)
if os.path.exists(img_path):
doc.add_picture(img_path, width=Inches(5.5))
last_p = doc.paragraphs[-1]
last_p.alignment = WD_ALIGN_PARAGRAPH.CENTER
# Table rows (simple pipe tables)
elif line.startswith('|') and '---' not in line:
cells = [c.strip() for c in line.split('|')[1:-1]]
if cells:
# Check if this is first row of a table
if not hasattr(doc, '_current_table') or doc._current_table is None:
doc._current_table = doc.add_table(rows=0, cols=len(cells))
doc._current_table.style = 'Table Grid'
row = doc._current_table.add_row()
for i, cell_text in enumerate(cells):
if i < len(row.cells):
row.cells[i].text = cell_text
elif line.startswith('|') and '---' in line:
pass # skip separator rows
else:
# End current table if any
if hasattr(doc, '_current_table'):
doc._current_table = None
# Regular paragraph
if line.strip():
p = doc.add_paragraph()
parts = re.split(r'(\*\*.*?\*\*)', line)
for part in parts:
if part.startswith('**') and part.endswith('**'):
run = p.add_run(part[2:-2])
run.bold = True
else:
p.add_run(part)
# --- Embed remaining figures at the end ---
if os.path.isdir(fig_dir):
pngs = sorted(glob.glob(os.path.join(fig_dir, '*.png')))
if pngs:
doc.add_heading('Figures', level=1)
for png in pngs:
fname = os.path.basename(png)
doc.add_heading(fname.replace('.png', '').replace('_', ' ').title(), level=3)
doc.add_picture(png, width=Inches(5.5))
last_p = doc.paragraphs[-1]
last_p.alignment = WD_ALIGN_PARAGRAPH.CENTER
doc.add_paragraph('')
# --- Append METHODS if present ---
methods_path = os.path.join(PROJECT_DIR, "METHODS.md")
if os.path.exists(methods_path):
doc.add_page_break()
doc.add_heading('Methods', level=1)
with open(methods_path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line and not line.startswith('#'):
doc.add_paragraph(line)
os.makedirs(os.path.dirname(OUTPUT_FILE), exist_ok=True)
doc.save(OUTPUT_FILE)
print(f"Saved: {OUTPUT_FILE}")
DOCXEOF
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.
- 6d ago First seen · 158 lines · 61 tokens per session scan A af99a63d7d7f
export-docx is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 1,424 once invoked, about $0.0003 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
clinical-case-report
Structured medical case presentation for clinical rounds, conferences, and documentation. Generates SOAP-format or narrative case reports with physiologically accurate vitals, labs, and evidence-based plans. Use when the brief mentions "case report", "case presentation", "SOAP note", "clinical case", "ward rounds"…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
nature-experiment-log
A workflow for turning experiment notes, images, audio, or text into structured Markdown laboratory logs with YAML metadata. It can also organize raw attachments and optionally connect the logs to Feishu or Obsidian.
nature-paper2ppt
A workflow for turning a scientific paper, preprint, PDF, article, figure legends, or reading notes into a complete Chinese PowerPoint presentation. It is designed for journal clubs, group meetings, thesis seminars, conferences, defences, and paper-sharing talks.
parsing-ccda-documents
Parses C-CDA / CCD XML clinical documents to extract human-readable section narrative plus coded entries, keyed by section LOINC codes and templateIds. Use before OpenMed processing when ingesting C-CDA R2.1 documents (CCD, Discharge Summary, H&P, Consultation Note) exported from an EHR and you need the narrative…