ai-context-tools 0.9.0

AI workflow utilities for the AI Context Standard

ai-context-tools

AI workflow utilities for the AI Context Standard.

Version: tracks AI Context Standard version (currently 0.8.8)

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Installation

pip install ai-context-tools

Or for development (from the workspace):

pip install -e C:/Users/takahashi/GitHub/ai-context-tools

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Publishing to PyPI

Uses GitHub Actions with PyPI Trusted Publisher (OIDC — no API token needed).

One-time setup on PyPI: Configure a Trusted Publisher at
https://pypi.org/manage/account/publishing/
(or for a new package not yet on PyPI, use the "pending publisher" form)

Settings to enter:

• PyPI project name: ai-context-tools
• Owner: freesemt
• Repository: ai-context-tools
• Workflow: upload_to_pypi.yml
• Environment: (leave blank)

To publish: Go to Actions → "Manual Upload Python Package to PyPI" → Run workflow.

The workflow builds the package, uploads to PyPI via OIDC, and creates a version tag (e.g. v0.8.2).

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Tools

aic_tools.notebook — Read notebook cell outputs

Bypasses the built-in read_notebook_cell_output tool size limit by reading the .ipynb JSON directly from disk.

When to use (routing rule for AI assistants): After copilot_getNotebookSummary, check each cell's mime types. If a cell has application/vnd.code.notebook.stdout, use this tool — the built-in tool will fail silently with "output too large".

CLI:

python -m aic_tools.notebook <notebook.ipynb> <cell_number> [max_lines]

# Examples:
python -m aic_tools.notebook experiments/08d.ipynb 14
python -m aic_tools.notebook experiments/08d.ipynb 14 0   # all lines

Entry point (after install):

aic-notebook experiments/08d.ipynb 14

Python API:

from aic_tools.notebook import read_cell_output
read_cell_output("experiments/08d.ipynb", 14)
read_cell_output("experiments/08d.ipynb", 14, max_lines=0)  # all lines

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aic_tools.nb_status — Read notebook execution status

Reports which cells have been executed (and in what order) by reading the execution_count field stored in the .ipynb file.

When to use (routing rule for AI assistants):

⚠️ disk vs. live kernel: execution counts are read from the saved .ipynb file. If the file was externally rewritten while open in VS Code (e.g. via json.dump), VS Code reloads from disk and loses unsaved counts. In that case, the kernel variables section of copilot_getNotebookSummary is the authoritative live source — a variable being present there proves the cell ran, regardless of what the execution count says.

Use aic-nb-status for quick offline checks or to cross-check after a suspected external file write.

CLI:

python -m aic_tools.nb_status <notebook.ipynb>
python -m aic_tools.nb_status <notebook.ipynb> --executed-only
python -m aic_tools.nb_status <notebook.ipynb> --json

Entry point (after install):

aic-nb-status experiments/13h.ipynb
aic-nb-status experiments/13h.ipynb --executed-only

Python API:

from aic_tools.nb_status import get_execution_status
rows = get_execution_status("experiments/13h.ipynb")
# Returns list of dicts: cell_number, cell_id, cell_type, execution_count, first_line

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aic_tools.runcell — Execute a notebook cell with fresh outputs

Executes cells 1..N of a notebook via nbclient and prints the target cell's outputs to the terminal with no size limit. The companion to aic_tools.notebook (which reads stale outputs from disk).

When to use (routing rule for AI assistants):

• Use aic_tools.notebook when the cell's last-saved output is enough.
• Use aic_tools.runcell when you need fresh output — e.g. after editing code that the cell depends on, or when verifying a one-line fix without re-running the entire notebook in the GUI.

Install execution dependencies (nbclient, nbformat, ipykernel):

pip install ai-context-tools[run]

CLI:

python -m aic_tools.runcell <notebook.ipynb> <cell_number> \
    [--kernel NAME] [--timeout SEC] [--write] [--max-lines N]

# Examples:
python -m aic_tools.runcell experiments/08d.ipynb 14
python -m aic_tools.runcell experiments/08d.ipynb 14 --write    # save outputs
python -m aic_tools.runcell experiments/08d.ipynb 14 --kernel python3

Entry point (after install):

aic-runcell experiments/08d.ipynb 14

Python API:

from aic_tools.runcell import run_up_to_cell
cell = run_up_to_cell("experiments/08d.ipynb", 14)

Behaviour:

• Executes cells 1 through cell_number in order (markdown cells are skipped by nbclient automatically) so the kernel state is correctly built up.
• Read-only by default — the .ipynb is not modified unless --write is passed.
• Exit code 1 on cell error, file-not-found, or invalid arguments.

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aic_tools.marimo_session — Read marimo notebook cell outputs

Reads the marimo session cache written to __marimo__/session/<notebook_name>.py.json after each execution. Works without a running server — reads from disk.

When to use (routing rule for AI assistants):

Output type	Tool
console (stdout/stderr from print())	aic_tools.marimo_session — always readable as text
text/plain, text/markdown	aic_tools.marimo_session
text/html with embedded image	size summary only; use screenshot_page() to view
Live UI (sliders, buttons)	browser tools — screenshot_page(), run_playwright_code()

CLI:

python -m aic_tools.marimo_session <notebook.py>              # list all cells
python -m aic_tools.marimo_session <notebook.py> <cell_N>     # read cell N (1-based)
python -m aic_tools.marimo_session <notebook.py> <cell_N> 50  # limit to 50 lines

Entry point (after install):

aic-marimo-session experiments/23a_basic_workflow.py
aic-marimo-session experiments/23a_basic_workflow.py 8

Python API:

from aic_tools.marimo_session import list_cells, read_cell_output
list_cells("experiments/23a_basic_workflow.py")
read_cell_output("experiments/23a_basic_workflow.py", 8)
read_cell_output("experiments/23a_basic_workflow.py", 8, max_lines=50)

Note: marimo mcp does not exist in marimo 0.23.8. This tool is the primary offline alternative for AI-readable marimo cell output.

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aic_tools.edit_lines — Line-range based file editing

Solves the "duplicate content" problem where replace_string_in_file fails with "Multiple matches found" errors. Uses line numbers instead of string matching, making edits precise and unambiguous.

When to use (routing rule for AI assistants):

• HTML/XML files with duplicate sections (common in generated/templated files)
• Any file where replace_string_in_file returns "Multiple matches found"
• Large structural changes where line-based editing is clearer
• When you need to replace exact line ranges (e.g., "replace lines 124-358")

What problem does this solve?

VS Code's built-in replace_string_in_file requires exact string matching. If your file has duplicate sections (common in HTML templates, config files, generated documentation), the tool fails with "Multiple matches found".

This tool uses line numbers instead, so you can target the exact section:

# Replace lines 124-358 in index.html with content from replacement.html
python -m aic_tools.edit_lines index.html 124 358 replacement.html

CLI:

python -m aic_tools.edit_lines <file> <start_line> <end_line> <content_file>
python -m aic_tools.edit_lines <file> <start_line> <end_line> --delete
python -m aic_tools.edit_lines <file> <start_line> <end_line> --stdin

# Examples:
python -m aic_tools.edit_lines index.html 124 358 replacement.html
python -m aic_tools.edit_lines config.json 10 20 --delete
echo "new content" | python -m aic_tools.edit_lines file.txt 5 5 --stdin
python -m aic_tools.edit_lines file.txt 1 10 new.txt --no-backup

Entry point (after install):

aic-edit-lines index.html 124 358 replacement.html

Python API:

from aic_tools.edit_lines import edit_lines, delete_lines

# Replace lines 10-20 with new content
edit_lines("index.html", 10, 20, "new content here")

# Replace from file
edit_lines("index.html", 10, 20, content_file="replacement.html")

# Delete lines 10-20
delete_lines("index.html", 10, 20)

# Edit without backup
edit_lines("index.html", 10, 20, "new content", backup=False)

Safety features:

• Creates .bak backup by default (use --no-backup to skip)
• Validates line numbers before editing
• Atomic write (writes to temp file, then renames — no partial writes)

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aic_tools.pdf — Extract text from PDF files

Automatically selects the best PDF library for your document:

• pymupdf (recommended): best quality, handles Japanese & scanned PDFs
• pypdf (fallback): lighter, sufficient for English text PDFs

When to use (routing rule for AI assistants): Just call this tool — it automatically handles:

• Japanese PDFs (uses pymupdf if available)
• English PDFs (pypdf fallback is sufficient)
• Scanned PDFs (pymupdf preferred)
• Encoding issues (automatic selection)

You don't need to check PDF type or choose which library to use.

Install PDF dependencies:

# Recommended (both libraries for full compatibility):
pip install ai-context-tools[pdf]

# Or install individually:
pip install pymupdf  # recommended
pip install pypdf    # fallback

CLI:

python -m aic_tools.pdf <file.pdf>               # Extract all pages
python -m aic_tools.pdf <file.pdf> --page 2      # Extract single page
python -m aic_tools.pdf <file.pdf> --page 2 --max-lines 50

# Examples:
python -m aic_tools.pdf paper.pdf
python -m aic_tools.pdf paper.pdf --page 1
python -m aic_tools.pdf paper.pdf --page 2 --max-lines 20

Entry point (after install):

aic-pdf paper.pdf --page 2

Python API:

from aic_tools.pdf import extract_text

# Extract all pages
text = extract_text("paper.pdf")

# Extract specific page (1-indexed)
text = extract_text("paper.pdf", page=2)

Requirements:

pip install ai-context-tools[pdf]
# or
pip install pypdf

JOSS review note: For JOSS PDFs with line numbering, extract the full page text — line numbers appear at the end of each line in the extracted text (e.g., "Software Design34"), making them easily parsable.

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Versioning

Package version tracks the AI Context Standard version that introduced each tool. 0.8.2 = notebook reader introduced in Standard v0.8.2.

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Relationship to other tools

Tool	Language	Role
ai-context-vscode	TypeScript / VS Code extension	Live notebook cell output reading + VS Code version recording (supersedes vscode-version-recorder)
ai-context-tools (this package)	Python	AI workflow utilities (notebook output reading, etc.)

All tools support the AI Context Standard.

VS Code users: The ai-context-vscode extension reads live cell outputs from the VS Code document model — no save required. This Python package serves as the fallback for terminal-only sessions or non-VS Code editors.

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License

MIT