Agent skill

Footnote Relegator

by lawve-ai in lawve-ai/awesome-legal-skills

Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient.

Apache-2.0Auto-check passedDocuments & Office

Install Footnote Relegator

skills CLI
$ npx skills add lawve-ai/awesome-legal-skills --skill footnote-relegator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lawve-ai/awesome-legal-skills footnote-relegator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/footnote-relegator-seth-chandler .claude/skills/footnote-relegator && rm -rf skills-src

Use ~/.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/

Facts

Skill name
footnote-relegator
GitHub stars
847
Token cost
~3.5k tokens
SKILL.md length
1,814 words
Files
11 (incl. scripts)
Skills in repo
154
Repo updated
First seen
Licence
Apache-2.0

At a glance

Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient.

  • Works in 7 steps: Intake and measure → Classify → Select to target → …
  • Asked to push detail into footnotes
  • SKILL.md covers The governing principle, Inputs, Hard rules (these define the… and Workflow, plus 6 more sections
  • Runs Python scripts from its folder; calls python and pandoc

What it does

Footnote Relegator is an agent skill from lawve-ai/awesome-legal-skills. Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient. Handles Markdown and Word (.docx), integrates moved material with existing notes, measures a user-specified relegation fraction (25% by default), and delivers a move-by-move memo. Use when asked to "push detail into footnotes," "relegate to footnotes," "footnote the caveats," "move the digressions down," or tighten a dense draft without…

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts (for example `README.md`, `resources/demotion-rubric.md` and `resources/integration-rules.md`).

It sits in Documents & Office, covering Word documents, Document parsing and Markdown. It works with Microsoft Word. The repository describes itself as: A curated list of awesome Agent Skills for automating legal work. The licence is Apache-2.0.

When your agent uses it

  • Asked to push detail into footnotes
  • Relegate to footnotes
  • Footnote the caveats
  • Move the digressions down

Example prompts

  • “push detail into footnotes,”
  • “relegate to footnotes,”
  • “footnote the caveats,”
  • “/footnote-relegator”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Intake and measure
  2. Classify
  3. Select to target
  4. Execute and repair
  5. Integrate with existing footnotes
  6. Verify
  7. Deliver

What it can do on your machine

Read from SKILL.md and the folder at commit 045f738. 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 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pandoc

    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

Footnote Relegator loads about 3.5k tokens when it runs. Until then it costs about 184 tokens; SKILL.md has 1,814 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~184
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

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 lawve-ai/awesome-legal-skills at commit 045f738, republished under its Apache-2.0 licence (© lawve-ai). 1,814 words, ~3,533 tokens.

Download SKILL.mdSave it as .claude/skills/footnote-relegator/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
footnote-relegator
description
Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient. Handles Markdown and Word (.docx), integrates moved material with existing notes, measures a user-specified relegation fraction (25% by default), and delivers a move-by-move memo. Use when asked to "push detail into footnotes," "relegate to footnotes," "footnote the caveats," "move the digressions down," or tighten a dense draft without deleting content. Word output requires file access plus document-conversion and rendering support; when those capabilities are unavailable, deliver Markdown and the memo or ask for a convertible source.
metadata.author
Seth J. Chandler
metadata.author_link
https://legaled.ai
metadata.license
Apache-2.0
metadata.version
2026-08-10
metadata.jurisdiction
International
metadata.language
English
metadata.category
legal-drafting
metadata.requires
File access; Python 3 for bundled checks; document conversion and rendering for Word output

Footnote Relegator

Take a scholarly article and make it two-tiered: a leaner main text that carries the argument, and substantive footnotes that carry the detail. Nothing is deleted — text is moved, near-verbatim, from body to notes.

The governing principle

The body must remain self-sufficient. A reader who never glances at a footnote should follow every step of the argument and lose nothing essential. Footnotes carry enrichment, qualification, and apparatus — never a load-bearing move. Every candidate demotion must pass this test in both directions:

  • If removing the passage breaks the argument's chain, it stays in the body.
  • If the passage would survive removal without any reader noticing a gap in reasoning, it is a demotion candidate.

The complementary test for the footnote side: each note must stand alone. A reader who does drop down should find a self-contained mini-discussion, not a fragment that only makes sense mid-paragraph.

Inputs

  1. The article — Markdown or .docx. (Other formats: convert to Markdown first with pandoc, then treat as Markdown; deliver back in the original format only if a faithful conversion exists.)
  2. The relegation fraction — the share of original body-text words to move into footnotes. If the user gives a number ("relegate a third", "20%"), use it. If the user says nothing, use 25%. Body words means words in the main text excluding existing footnote/endnote content, headings kept as-is, and excluding tables, figures, and block quotes of primary sources (those are neither demoted nor counted).
Capability preflight

Before promising output formats, check what the host can actually do.

  1. If the user names a conversion or document-processing method, use it. If it is unavailable, say so and ask before substituting another method.
  2. Otherwise use any connected or built-in document capability that can faithfully read, edit, render, and verify the source while keeping it local to the authorized workspace.
  3. Otherwise use available local tools such as pandoc for conversion, Python 3 for the bundled scripts, and a Word-compatible renderer for visual checks.
  4. If no faithful Word conversion and verification path exists, do not promise .docx or tracked changes. Offer Markdown plus the relegation memo; if the only input is .docx, ask the user for Markdown or another source the host can read faithfully.

For Markdown input, scripts/ledger.py is optional but preferred. If Python 3 is not available, perform its conservation, word-count, and note-reference checks manually and say that the automated check did not run.

Hard rules (these define the skill — do not relax them)

  1. Move, never delete. Every word that leaves the body lands in a footnote. If something seems worthless, it still moves; flagging it for the author's own deletion belongs in the memo, not in your edit.
  2. Near-verbatim. Demoted text keeps its wording. Permitted edits are only those a passage needs to stand alone as a note: resolving pronouns whose referent stayed in the body, adjusting an opening connective ("Moreover," → dropped or replaced), converting a sentence fragment into a sentence. Do not compress, paraphrase, or improve the author's prose.
  3. The body reads seamlessly. After each removal, repair the seam: transitions, pronoun references, list continuity ("three reasons" → check all three still appear in the body or adjust the framing sentence — by moving framing too, not rewriting the claim).
  4. Citations travel with their sentences. An inline citation or an existing footnote reference inside demoted text goes down with it (see integration rules). Never strand a citation from the claim it supports, and never lose one.

Workflow

Step 1 — Intake and measure
  • Markdown: work on the file directly.
  • .docx: extract with pandoc article.docx -t markdown -o article.md (footnotes arrive as [^n] — verify they did). Keep the original .docx untouched; you will need it for output.
  • Count body words (see definition above) and compute the target: target_words = fraction × body_words. Land within about two percentage points of the requested fraction — this is a commitment the user gave a number for, not a vibe. scripts/ledger.py measures this for you; run it at the end, but you can also run it mid-edit to check progress.
Step 2 — Classify

Walk the text sentence by sentence. Mark each unit protected or relegable using resources/demotion-rubric.md. Read that file before classifying — the protected list (thesis moves, chain-of-reasoning steps, definitions used later, primary evidence, topic sentences) and the relegable taxonomy (qualifications, compressed history, literature placement, source criticism, counterexamples, secondary examples, methodological asides) with worked micro-examples live there.

Step 3 — Select to target

Build a demotion ledger before touching the text — a table of candidate units: the text (or its first words), word count, rubric category, destination (new note vs. merge into existing note N), and anchor sentence. Select candidates until the ledger sums to the target, preferring:

  • whole sentences and 2–3 sentence clusters over clause surgery;
  • demotions spread across the article over gutting one section;
  • the clearest rubric fits first — if you must reach for marginal candidates to hit the target, take the least load-bearing ones and say so in the memo.

If the article simply lacks enough relegable material to hit the fraction without breaking the body (rare, but real for very lean texts), stop short, and report the achieved fraction and the reason rather than demoting load-bearing prose.

Step 4 — Execute and repair

For each ledger entry, in document order:

  1. Remove the unit from the body; place the note anchor at the end of the surviving sentence it enriches (after closing punctuation).
  2. Write the footnote: the demoted text near-verbatim, standing alone.
  3. Repair the seam in the body prose.
  4. Check for orphaned back-references: "as noted above," "this exception," "the second objection" — anything in the body that now points at text that moved. Fix by re-anchoring the reference or moving the referring phrase too.
Step 5 — Integrate with existing footnotes

Read resources/integration-rules.md before this step. In brief: demoted prose joins an existing citation-only note at the same anchor rather than stacking a second flag on one sentence; same-subject textual notes merge; footnote references inside demoted text fold into the destination note (no notes-on-notes); numbering renumbers automatically in both Markdown and Word.

Step 6 — Verify

Run the ledger script:

python scripts/ledger.py original.md revised.md --fraction 0.25

It reports achieved fraction vs. target, body/note word deltas, and — most important — a lost-text check: any sentence that left the body and cannot be found (near-verbatim) in a footnote is flagged. A flagged sentence means you deleted instead of moved; fix it. Then re-read the revised body only, start to finish, for flow — the machine checks conservation, only reading checks seamlessness.

Show full SKILL.md (739 more words)Show less
Step 7 — Deliver
  • Markdown in → Markdown out: revised .md with [^n] notes, plus the relegation memo.
  • .docx in → both of: (a) a clean .docx with real auto-numbered Word footnotes, and (b) a tracked-changes redline of the original. See resources/word-redline.md for the full pipeline; the redline uses scripts/docx_redline.py against the unpacked original.
  • The relegation memo (always): a short Markdown file listing every move — anchor location, first words of demoted text, word count, rubric reason — plus anything flagged as deserving the author's own attention (filler, candidates you declined and why, fraction shortfall if any). The memo is what lets the author veto individual moves; keep each entry scannable.

Name outputs after the source: article-relegated.md / .docx, article-redline.docx, article-relegation-memo.md.

Word documents: what to know before starting

After the capability preflight succeeds, the clean .docx can come from pandoc using the original as a style reference, then a typography pass — pandoc's footnote style references are often undefined in real-world documents, leaving full-size baseline note calls:

pandoc revised.md -o article-draft.docx --reference-doc=article.docx
python scripts/polish_footnotes.py article-draft.docx article-relegated.docx

The redline is real Word tracked changes produced by editing the original document's XML — deletions wrapped in <w:del>, inserted note references in <w:ins>, notes added to word/footnotes.xml. This preserves the author's formatting exactly and lets them accept/reject each demotion in Word. resources/word-redline.md has the step-by-step pipeline including a host-capability fallback when dedicated Word verification tools are unavailable. Warn the user up front if the document contains pre-existing tracked changes, comments, or cross-reference fields: pre-existing tracked changes must be accepted (by the user) before relegation; fields referring to moved text will need manual attention.

Bundled resources

  • resources/demotion-rubric.md — protected vs. relegable taxonomy with examples. Read before Step 2.
  • resources/integration-rules.md — how demoted text merges with existing citation and textual footnotes. Read before Step 5.
  • resources/word-redline.md — the .docx redline pipeline and its verification fallback. Read before producing Word output.
  • resources/worked-example.md — a short before/after with its ledger and memo entries. Read if calibration feels uncertain, or the user asks what the output will look like.
  • scripts/ledger.py — standard-library Markdown word accounting, conservation, and footnote-reference checks. Run in Step 6 when Python 3 is available.
  • scripts/docx_redline.py — standard-library OOXML tracked-change editor. Read resources/word-redline.md, then run only for Word redline output.
  • scripts/polish_footnotes.py — standard-library .docx typography pass for a pandoc-produced clean copy. Run only for Word clean-copy output.

Relationship to tentacle-footnote-finder

If the user also has the tentacle-footnote-finder skill, the two compose: that skill finds body sentences that deserve expansion footnotes; this one moves existing body text down. The rubric here deliberately shares its vocabulary (qualification, literature placement, source criticism, …). Do not confuse the tasks: this skill writes footnotes only out of the author's own existing words.

This companion is optional. Its absence does not change this skill's workflow or outputs.

Jurisdiction

The demotion method is jurisdiction-neutral: it preserves an author's doctrine, citations, and terminology rather than resolving legal questions. Citation forms, court rules, and substantive propositions remain the author's and must be checked under the source's actual jurisdiction. For that reason, catalogue jurisdiction is International.

License and attribution

Copyright 2026 Seth J. Chandler. This original work is released under the Apache License 2.0. See LICENSE for the full terms and NOTICE for origin, code, and disclaimer details.

Limitations and risks

  • Relegation is an editorial judgment. A model can misclassify a necessary premise as a digression, weaken emphasis, or choose an awkward anchor even when no words are lost. The author must review the clean copy, redline, and memo before publication or filing.
  • The conservation script uses approximate sentence splitting and fuzzy matching. A clean report does not prove that citations, defined terms, cross-references, bookmarks, fields, or note antecedents remain correct; perform the specified manual sweep.
  • Word output depends on the host's ability to convert, render, and inspect OOXML. The bundled editor fails loudly on ambiguous text matches, but it does not replace opening both accepted and rejected views in a Word-compatible application.
  • Drafts may contain privileged, confidential, personal, or embargoed material. Keep them within systems the user has authorized and do not upload them to a third-party service without informed approval.
  • This skill supplies editorial assistance, not legal advice or citation validation. It does not determine whether the revised article is legally correct or ready to file.

The package contains three Python scripts. They use only the standard library, make no network calls, spawn no subprocesses, and perform no dynamic evaluation. ledger.py is read-only; docx_redline.py writes only inside the caller-named unpacked document directory; and polish_footnotes.py uses a temporary directory and writes or replaces only the caller-named output file.

© lawve-ai, Apache-2.0. 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 10 other files (scripts) in skills/footnote-relegator-seth-chandler of lawve-ai/awesome-legal-skills.

  • SKILL.md
  • LICENSE
  • NOTICE
  • README.md
  • resources/demotion-rubric.md
  • resources/integration-rules.md
  • resources/word-redline.md
  • resources/worked-example.md
  • scripts/docx_redline.py
  • scripts/ledger.py
  • scripts/polish_footnotes.py

Open the folder on GitHubat commit 045f738

Compare with similar skills

Footnote Relegator 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.

Footnote Relegator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Footnote Relegator this skilllawve-ai/awesome-legal-skills847—~3.5kAutomated safety check: PassApache-2.0
MarkitdownImCa0/just-laws78114 repos~3.2kAutomated safety check: NotesMIT
MineruNebutra/MinerU-Skill123—~504Automated safety check: PassMIT
Doc To Markdowndaymade/claude-code-skills1.5k—~2.5kAutomated safety check: PassMIT
Markdown Converterintellectronica/agent-skills2954 repos~492Automated safety check: PassCC0-1.0
Word to Markdown Convertergithub/awesome-copilot40k—~1.6kAutomated safety check: PassMIT

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Works with

Questions about Footnote Relegator

What does Footnote Relegator do?

Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient. Footnote Relegator is an agent skill from lawve-ai/awesome-legal-skills. Condense a scholarly legal article by moving nonessential detail from the body into substantive footnotes while preserving every word and keeping the argument self-sufficient.

When should I use Footnote Relegator?

Footnote Relegator fits situations like: asked to push detail into footnotes; relegate to footnotes; footnote the caveats; move the digressions down.

How do I install Footnote Relegator in Claude Code?

Run `npx skills add lawve-ai/awesome-legal-skills --skill footnote-relegator -a claude-code`. Or copy the skill folder (skills/footnote-relegator-seth-chandler in lawve-ai/awesome-legal-skills) into .claude/skills/footnote-relegator in your project. Claude Code loads it when a task matches its description.

How do I install Footnote Relegator in Codex?

Run `npx skills add lawve-ai/awesome-legal-skills --skill footnote-relegator -a codex`. Or copy the skill folder (skills/footnote-relegator-seth-chandler in lawve-ai/awesome-legal-skills) into .agents/skills/footnote-relegator in your project. Codex loads it when a task matches its description.

Can I use Footnote Relegator 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 lawve-ai/awesome-legal-skills --skill footnote-relegator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/footnote-relegator, .gemini/skills/footnote-relegator, .github/skills/footnote-relegator and .opencode/skills/footnote-relegator in your project.

What does Footnote Relegator need to run?

Going by SKILL.md and its folder, Footnote Relegator needs Python for the scripts in its folder and the command-line tools its instructions call (python and pandoc). Our summary lists: Python 3.

Does Footnote Relegator 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 Footnote Relegator 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 Footnote Relegator use?

Footnote Relegator is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Footnote Relegator use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Footnote Relegator?

Skills that share tags, products or a category with Footnote Relegator: Markitdown (ImCa0/just-laws, 781 stars), Mineru (Nebutra/MinerU-Skill, 123 stars), Doc To Markdown (daymade/claude-code-skills, 1.5k stars) and Markdown Converter (intellectronica/agent-skills, 295 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Footnote Relegator?

lawve-ai (a GitHub organization) maintains it in lawve-ai/awesome-legal-skills, which has 847 GitHub stars. The repository holds 154 skills in this directory. The repository was last updated on October 2, 2026.

Source: lawve-ai/awesome-legal-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.