Contract Snapshot
LegalQuants/lq-ai
A skill your agent uses when the user wants to compare the same handful of terms across N contracts side-by-side in a grid — what is the term, survival period, carveouts, and governing law in each…
Extract structured data from multiple documents into comparison matrix with citations.
Install Tabular Document Review
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills tabular-document-review --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/legal/tabular-document-review .claude/skills/tabular-document-review && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .claude/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-reviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills tabular-document-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/legal/tabular-document-review .agents/skills/tabular-document-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .agents/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills tabular-document-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/legal/tabular-document-review .cursor/skills/tabular-document-review && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .cursor/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path legal/tabular-document-review--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills tabular-document-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/legal/tabular-document-review .gemini/skills/tabular-document-review && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .gemini/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills tabular-document-reviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/legal/tabular-document-review .github/skills/tabular-document-review && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .github/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill tabular-document-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills tabular-document-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/legal/tabular-document-review .opencode/skills/tabular-document-review && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tabular-document-review" agent skill from https://github.com/borghei/Claude-Skills/tree/main/legal/tabular-document-review into .opencode/skills/tabular-document-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tabular-document-review", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
tabular-document-review- GitHub stars
- 881
- Token cost
- ~3k tokens
- SKILL.md length
- 1,015 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 349
- Repo updated
- First seen
- Licence
- MIT
At a glance
Extract structured data from multiple documents into comparison matrix with citations.
- Works in 2 steps: Document Discovery… → Extraction Aggregator…
- Bulk document review
- SKILL.md covers Overview, Table of Contents, Clarify First and Tools, plus 8 more sections
- Runs Python scripts from its folder; calls python
What it does
Tabular Document Review is an agent skill from borghei/Claude-Skills. Extract structured data from multiple documents into comparison matrix with citations. Use for bulk document review.
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/common_extraction_columns.md`, `references/extraction_methodology.md` and `scripts/document_discovery.py`).
It sits in Legal & Compliance, covering Contract review, Schema markup and Citation management. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Bulk document review
- Tasks that involve Contract review
- Tasks that involve Schema markup
Example prompts
- “/tabular-document-review”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tabular Document Review loads about 3k tokens when it runs, and up to ~9.1k if it reads all its reference files. Until then it costs about 35 tokens; SKILL.md has 1,015 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 1,015 words, ~3,006 tokens.
Download SKILL.mdSave it as
.claude/skills/tabular-document-review/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- tabular-document-review
- description
- Extract structured data from multiple documents into comparison matrix with citations. Use for bulk document review.
- license
- MIT + Commons Clause
- metadata.version
- 1.0.0
- metadata.author
- The Glass Room
- metadata.category
- legal
- metadata.domain
- document-review
- metadata.updated
- 2026-04-10
- metadata.tags
- document-review, extraction, comparison-matrix, contracts, bulk-review
⚠️ EXPERIMENTAL — This skill is provided for educational and informational purposes only. It does NOT constitute legal advice. All responsibility for usage rests with the user. Consult qualified legal professionals before acting on any output.
Tabular Document Review Skill
Overview
Production-ready toolkit for extracting structured data from multiple legal documents into a comparison matrix with citations. Supports user-defined extraction columns, parallel processing with up to 10 agents, confidence scoring, and output in markdown table or structured JSON. Designed for legal teams performing bulk contract review, NDA comparison, employment agreement analysis, and lease review.
Table of Contents
- Tools
- Reference Guides
- Workflows
- Extraction Scenarios
- Troubleshooting
- Success Criteria
- Scope & Limitations
- Anti-Patterns
- Tool Reference
Clarify First
Before the review, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- The extraction columns — define exactly what each cell holds; a vague "Date" matches dozens of dates, while "Effective Date" with guidance does not
- Document set location and formats — drives discovery and how many parallel agents to allocate (ceil(N/10), max 10)
- Document type — contracts, NDAs, employment, leases — selects the pre-defined column set and per-column extraction guidance
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the matrix.
Tools
1. Document Discovery (scripts/document_discovery.py)
Scan a directory for legal documents and generate an inventory manifest.
python scripts/document_discovery.py /path/to/contracts
python scripts/document_discovery.py /path/to/ndas --types pdf,docx --json
python scripts/document_discovery.py /path/to/leases --types pdf,docx,txt,md --min-size 10242. Extraction Aggregator (scripts/extraction_aggregator.py)
Aggregate multiple extraction result JSONs into a unified comparison matrix.
python scripts/extraction_aggregator.py \
--results extraction_1.json extraction_2.json extraction_3.json
python scripts/extraction_aggregator.py \
--results-dir ./extraction_results/ --json
python scripts/extraction_aggregator.py \
--results-dir ./extraction_results/ \
--format markdown \
--output review_matrix.md
python scripts/extraction_aggregator.py \
--results extraction_1.json extraction_2.json \
--columns "Parties,Effective Date,Term,Governing Law"Reference Guides
| Reference | Purpose |
|---|---|
references/extraction_methodology.md | Document extraction best practices, JSON schema, agent prompts |
references/common_extraction_columns.md | Pre-defined column sets for contracts, NDAs, employment, leases |
Workflows
5-Step Document Review Pipeline
| Step | Action | Tool | Output |
|---|---|---|---|
| 1. Gather Requirements | Define document folder, output filename, columns to extract | Manual | Column list, file path |
| 2. Discover Documents | Scan directory for target documents | document_discovery.py | Document manifest JSON |
| 3. Process Documents | Extract values per column with citations (parallel agents) | AI agents (external) | Per-document extraction JSONs |
| 4. Collect Results | Aggregate extraction JSONs into unified matrix | extraction_aggregator.py | Consolidated matrix |
| 5. Generate Output | Export as markdown table or structured JSON | extraction_aggregator.py | Final deliverable |
Parallel Processing Strategy
| Agents | Documents per Agent | Use When |
|---|---|---|
| 1 | All | 1-5 documents |
| 2-3 | ceil(N/agents) | 6-15 documents |
| 4-6 | ceil(N/agents) | 16-40 documents |
| 7-10 | ceil(N/agents) | 41-100 documents |
| 10 (max) | ceil(N/10) | 100+ documents |
Agent Prompt Template
Each agent receives a prompt structured as:
You are reviewing {count} legal documents. For each document, extract the
following columns:
{column_definitions}
For each value extracted:
1. Provide the exact value found
2. Include the page number (PDF) or section/paragraph (DOCX/MD)
3. Rate your confidence: HIGH (exact match), MEDIUM (inferred), LOW (uncertain)
4. If not found, record "NOT FOUND" with confidence LOW
Output as JSON per the extraction schema.Confidence Scoring
| Level | Color Code | Definition |
|---|---|---|
| HIGH | Green | Exact value found with clear citation |
| MEDIUM | Yellow | Value inferred from context; multiple possible interpretations |
| LOW | Red / Not Found | Value uncertain or not found in document |
Output Format
Sheet 1: Document Review
| Document | Parties | Effective Date | Term | Governing Law | ... |
|---|---|---|---|---|---|
| contract_a.pdf | Acme / Beta [p.1] | 2026-01-15 [p.2] | 3 years [p.3] | Delaware [p.12] | ... |
| contract_b.pdf | Gamma / Delta [p.1] | NOT FOUND | 2 years [p.4] | New York [p.10] | ... |
Sheet 2: Summary
| Metric | Value |
|---|---|
| Documents processed | 25 |
| Columns extracted | 8 |
| Average confidence | 87% |
| Not found rate | 12% |
Extraction Scenarios
Contract Review
| Column | What to Extract |
|---|---|
| Parties | All contracting parties with full legal names |
| Effective Date | Contract effective or execution date |
| Term | Duration of the agreement |
| Renewal | Auto-renewal terms and notice period |
| Governing Law | Jurisdiction governing the agreement |
| Liability Cap | Maximum liability amount or formula |
| Indemnification | Indemnification obligations and scope |
| IP Ownership | Intellectual property ownership provisions |
| Termination Rights | Termination triggers and notice requirements |
| Data Protection | Data protection or privacy obligations |
NDA Review
| Column | What to Extract |
|---|---|
| Parties | Disclosing and receiving parties |
| Type | Mutual or one-way |
| Definition Scope | How "confidential information" is defined |
| Exceptions | Standard exceptions to confidentiality |
| Term | Duration of confidentiality obligations |
| Survival | Survival period after termination |
| Return/Destruction | Obligations on termination |
| Remedies | Available remedies for breach |
Show full SKILL.md (427 more words)Show less
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| Discovery finds 0 documents | Wrong path or file types | Verify path exists; check --types matches actual file extensions |
| Extraction JSONs have wrong schema | Agent prompt incomplete | Use the extraction schema from extraction_methodology.md |
| Aggregator shows conflicts | Multiple values for same cell | Review source documents; aggregator marks conflicts for manual review |
| High "NOT FOUND" rate | Columns too specific for document type | Use column definitions from common_extraction_columns.md; broaden definitions |
| Confidence all LOW | Agent unable to locate values | Check column definitions are specific enough; verify document is readable |
| Aggregator crashes on large set | Too many result files loaded at once | Process in batches of 50 results; use --columns to limit output width |
| Markdown table misaligned | Long values or special characters | Use --format json for machine processing; truncate long values |
| Missing citations | Agent did not include page/section references | Reinforce citation requirement in agent prompt; check extraction schema |
Success Criteria
- Extraction Coverage: 90%+ of defined columns populated across all documents
- Confidence Distribution: 70%+ of extractions rated HIGH confidence
- Citation Accuracy: Every extracted value includes verifiable page/section citation
- Processing Speed: 50+ documents processed within 30 minutes using parallel agents
- Matrix Completeness: Final matrix includes all documents and all columns with no orphan rows
Scope & Limitations
This skill covers:
- Document inventory and discovery across PDF, DOCX, TXT, and MD formats
- Aggregation of extraction results from parallel agent processing into unified matrix
- Pre-defined column sets for contracts, NDAs, employment agreements, and leases
- Confidence scoring and conflict detection for extracted values
- Markdown and JSON output formats
This skill does NOT cover:
- Actual document parsing or text extraction (requires external libraries or AI agents)
- OCR processing for scanned documents
- Excel/XLSX output generation (use JSON output and convert externally)
- Automated legal analysis or risk assessment of extracted values
- Document comparison or redlining between versions
Anti-Patterns
| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Vague column definitions | "Date" could match dozens of dates in a contract | Use specific definitions: "Effective Date" with guidance on where to look |
| Skipping document discovery | Unknown document count leads to wrong agent allocation | Always run discovery first; use manifest for pipeline planning |
| Ignoring LOW confidence results | Missing or uncertain data treated as fact | Review all LOW confidence cells manually; flag in final report |
| Processing 100+ docs with 1 agent | Slow, context window overflow, quality degradation | Use parallel processing: ceil(N/10) documents per agent, max 10 agents |
| No citation requirement | Cannot verify extracted values against source | Require page/section citation for every extraction; reject uncited values |
Tool Reference
scripts/document_discovery.py
Scan directory for legal documents and generate inventory manifest.
usage: document_discovery.py [-h] [--json]
[--types TYPES]
[--min-size MIN_SIZE]
[--max-size MAX_SIZE]
directory
positional arguments:
directory Path to directory containing documents
options:
-h, --help Show help message and exit
--json Output in JSON format
--types TYPES Comma-separated file extensions to include
(default: pdf,docx,doc,txt,md,rtf)
--min-size MIN_SIZE Minimum file size in bytes (default: 0)
--max-size MAX_SIZE Maximum file size in bytes (default: no limit)scripts/extraction_aggregator.py
Aggregate extraction results into unified comparison matrix.
usage: extraction_aggregator.py [-h] [--json]
[--results RESULTS [RESULTS ...]]
[--results-dir RESULTS_DIR]
[--format {markdown,json}]
[--columns COLUMNS]
[--output OUTPUT]
options:
-h, --help Show help message and exit
--json Output in JSON format (alias for --format json)
--results One or more extraction result JSON files
--results-dir Directory containing extraction result JSON files
--format Output format: markdown table or JSON (default: markdown)
--columns Comma-separated column names to include (default: all)
--output Write output to file instead of stdout© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in legal/tabular-document-review of borghei/Claude-Skills.
- SKILL.md
- references/common_extraction_columns.md
- references/extraction_methodology.md
- scripts/document_discovery.py
- scripts/extraction_aggregator.py
Open the folder on GitHubat commit 4a698e8
Compare with similar skills
Tabular Document Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tabular Document Review this skillborghei/Claude-Skills | 881 | — | ~3k | Automated safety check: Pass | MIT | |
| Contract SnapshotLegalQuants/lq-ai | 150 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Legal Design Assessmentlawve-ai/awesome-legal-skills | 836 | — | ~3.7k | Automated safety check: Pass | CC-BY-4.0 | |
| Msa SnapshotLegalQuants/lq-ai | 150 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Contract Output Formatterinfometa/workbuddyskills | 344 | — | ~1.6k | Automated safety check: Pass | None | |
| Contract Reviewevolsb/claude-legal-skill | 462 | 1 repos | ~3.6k | Automated safety check: Pass | MIT |
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Questions about Tabular Document Review
What does Tabular Document Review do?
Extract structured data from multiple documents into comparison matrix with citations. Tabular Document Review is an agent skill from borghei/Claude-Skills. Extract structured data from multiple documents into comparison matrix with citations.
When should I use Tabular Document Review?
Tabular Document Review fits situations like: bulk document review; tasks that involve Contract review; tasks that involve Schema markup.
How do I install Tabular Document Review in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill tabular-document-review -a claude-code`. Or copy the skill folder (legal/tabular-document-review in borghei/Claude-Skills) into .claude/skills/tabular-document-review in your project. Claude Code loads it when a task matches its description.
How do I install Tabular Document Review in Codex?
Run `npx skills add borghei/Claude-Skills --skill tabular-document-review -a codex`. Or copy the skill folder (legal/tabular-document-review in borghei/Claude-Skills) into .agents/skills/tabular-document-review in your project. Codex loads it when a task matches its description.
Can I use Tabular Document Review in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill tabular-document-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tabular-document-review, .gemini/skills/tabular-document-review, .github/skills/tabular-document-review and .opencode/skills/tabular-document-review in your project.
What does Tabular Document Review need to run?
Going by SKILL.md and its folder, Tabular Document Review needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Tabular Document Review access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Tabular Document Review safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Tabular Document Review use?
Tabular Document Review is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tabular Document Review use?
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.1k tokens, read only when the agent opens those files.
What are the alternatives to Tabular Document Review?
Skills that share tags, products or a category with Tabular Document Review: Contract Snapshot (LegalQuants/lq-ai, 150 stars), Legal Design Assessment (lawve-ai/awesome-legal-skills, 836 stars), Msa Snapshot (LegalQuants/lq-ai, 150 stars) and Contract Output Formatter (infometa/workbuddyskills, 344 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tabular Document Review?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 881 GitHub stars. The repository holds 349 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.