Agent skill

Decompose Mathematical Solution Corpora

by morluto in morluto/jacobian

Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

MITAuto-check passedAgent Workflows

Install Decompose Mathematical Solution Corpora

skills CLI
$ npx skills add morluto/jacobian --skill decompose-mathematical-solution-corpora -a claude-code

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

GitHub CLI
$ gh skill install morluto/jacobian decompose-mathematical-solution-corpora --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/morluto/jacobian.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/decompose-mathematical-solution-corpora .claude/skills/decompose-mathematical-solution-corpora && 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
decompose-mathematical-solution-corpora
GitHub stars
205
Token cost
~1.9k tokens
SKILL.md length
977 words
Files
2 (incl. references)
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

  • Tasks that involve Math and symbolic computation
  • SKILL.md covers Freeze the scope, Group by solution technique, Decompose each representative and Research the method, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Decompose Mathematical Solution Corpora is an agent skill from morluto/jacobian. Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/continuous-moves.md`).

It sits in Agent Workflows, covering Math and symbolic computation. It works with Model Context Protocol and Python. The repository describes itself as: Composable mathematics tools for agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Math and symbolic computation

Example prompts

  • “/decompose-mathematical-solution-corpora”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9dc2aaf. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Decompose Mathematical Solution Corpora loads about 1.9k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 37 tokens; SKILL.md has 977 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from morluto/jacobian at commit 9dc2aaf, republished under its MIT licence (© morluto). 977 words, ~1,924 tokens.

Download SKILL.mdSave it as .claude/skills/decompose-mathematical-solution-corpora/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
decompose-mathematical-solution-corpora
description
Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

Decompose Mathematical Solution Corpora

Extract reusable mathematical moves from a bounded solution corpus without turning the result into a file-by-file summary or a catalog of theorem wrappers. The unit of analysis is a method family: a recurring local mathematical move, its exact carrier, and the global reasoning that lifts it into a proof.

Use audit-mathematical-vocabulary for one bounded mathematical slice, learn-from-math-agent-trajectories for one completed or paused investigation, and audit-public-operation-contracts when a specific operation needs a deep contract review. This skill owns the repository- or corpus-level decomposition that precedes those focused audits.

Freeze the scope

Record the immutable revision of every source repository and the Jacobian revision and catalog used for comparison. Define the included directories, campaigns, or certificate families and any exclusions. Do not claim corpus closure from a partial clone, truncated artifact, generated summary, or unreadable dependency.

Inventory artifact types before reading deeply: papers and notes, Lean or other formal developments, exact Python or native programs, numerical experiments, solver encodings and proofs, prompts and trajectories, certificate bundles, test fixtures, and replay or publication metadata. Use the inventory to find method families, not to produce a chronological summary of every file.

Group by solution technique

Cluster sources by mathematical move rather than conjecture name or language. Examples include exact finite enumeration, dynamic programming, local-lemma witnesses, linear or semidefinite duality, algebraic elimination, interval enclosure, canonicalization, dependent rounding, coding-theoretic profiles, and finite-state transfer arguments.

Select representative sources from each family. Prefer sources that expose the local move, exact hypotheses, boundary behavior, and an independently replayable fixture. Continue sampling within a family until another representative no longer reveals a new carrier, postcondition, representation regime, or failure mode.

Decompose each representative

Describe the exact carrier, local move and stable postcondition, how the surrounding theorem uses it, and the evidence or fixture. Add representation, closure, and discovery details when they affect the proposed operation.

The local move is not automatically an operation. Reject boundaries that merely expose one loop iteration, solver control, callback, proof bookkeeping, or temporary data structure. Also reject the opposite boundary when it bundles the motivating theorem, search strategy, interpretation, and stopping rule. Look for one postcondition that remains meaningful if the surrounding paper or conjecture disappears.

Resolve the result's closure cases before calling it a complete finite value. Record the empty, zero, identity, repeated-root, singular, boundary, or continuum-locus case that applies to the proposed carrier. A result whose maximizers can be all points of a curve, for example, needs an exact locus variant rather than a fictitious complete finite witness list. This is part of the postcondition, not an implementation footnote.

For continuous or analytic sources, use the carrier checks to distinguish an exact local operation from a discretization that loses the source's decisive semantics.

Classify an established technique as a public-operation candidate, native-only function, private kernel, invariant or fixture, or caller reasoning. Technique names may be discovery vocabulary for a public operation without becoming separate operation IDs. Require an independently consumable postcondition before making an intermediate technique separately runnable.

Treat representation as mathematical execution evidence. State whether the carrier is materialized, succinct, generated, or oracle-backed; what expansion the implementation performs; whether that expansion is predictable before execution; and whether a compact representation changes the complexity class or output obligation.

Show full SKILL.md (438 more words)Show less

Research the method

Trace the local move to primary literature or an authoritative formal/library source. Verify the exact hypotheses, conclusion, conventions, algorithmic regime, and representation-sensitive complexity. Distinguish neighboring methods that share vocabulary but prove different guarantees. Use secondary surveys only to discover sources or terminology, then verify the conclusion against the primary source.

Research maintained exact backends and standard algorithms in proportion to the candidate. The question is whether a bounded, typed Jacobian contract is feasible—not whether the corpus's handwritten implementation should be copied. Record uncertainty when the literature supports the theorem but not an admissible exact kernel at the required scale.

Compare with Jacobian

Inspect the current catalog, native API, canonical values, request and result models, tests, admission decisions, and narrowly related issues. Attempt the smallest exact composition before declaring a gap. A manual coordinate change, cheap projection, or theorem-specific assembly normally remains caller work; incompatible values, detached certificates, or hidden expansion may instead identify an interoperability or contract problem.

Give every method family one disposition:

  • existing operation or exact composition;
  • representation or interoperability repair;
  • discovery repair;
  • request/result contract repair;
  • scale or backend improvement;
  • new bounded operation candidate;
  • defining, convention, adversarial, metamorphic, producer-consumer, or stress fixture;
  • reasoning, theorem-specific workflow, or certificate infrastructure; or
  • no supported Jacobian action.

For operation candidates, state the semantic domain, stable postcondition, source representation, controlling work and output quantities, reconstruction or defining invariant, typed incomplete states, and at least one discriminating fixture. Separate the existence of a reusable gap from public-catalog admission.

Route actions without overclaiming

Verify issue ownership narrowly before proposing a new issue. Reinforce the canonical owner when the operation, contract, or scale question is already in scope. Keep distinct semantics separate even when they share a backend. Do not file, comment, edit external systems, or make repository changes without user authorization.

Prefer compact in-thread findings and focused repository actions. Do not create large durable reports, copied source archives, or generated inventories unless the user requests them. Preserve only the small fixtures and evidence needed to replay a conclusion.

Establish closure

Stop when every inventoried method family has a disposition, every proposed operation has been compared with exact current composition and issue ownership, and additional representative sources yield no new local move, representation regime, postcondition, or fixture role. Report the frozen revisions, coverage, important exclusions, and unresolved uncertainties. “No gaps remain” means no unclassified reusable move within that declared scope, not that the corpus or mathematical literature contains nothing else.

Lead the final result with a compact technique-to-disposition matrix, followed by the few highest-value operation, contract, scale, and fixture actions. Keep the proof workflow separate from the atomic mathematical move throughout.

© morluto, 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 1 other file (references) in .agents/skills/decompose-mathematical-solution-corpora of morluto/jacobian.

  • SKILL.md
  • references/continuous-moves.md

Open the folder on GitHubat commit 9dc2aaf

Compare with similar skills

Decompose Mathematical Solution Corpora 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.

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Questions about Decompose Mathematical Solution Corpora

What does Decompose Mathematical Solution Corpora do?

Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory. Decompose Mathematical Solution Corpora is an agent skill from morluto/jacobian. Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

When should I use Decompose Mathematical Solution Corpora?

Decompose Mathematical Solution Corpora fits situations like: tasks that involve Math and symbolic computation.

How do I install Decompose Mathematical Solution Corpora in Claude Code?

Run `npx skills add morluto/jacobian --skill decompose-mathematical-solution-corpora -a claude-code`. Or copy the skill folder (.agents/skills/decompose-mathematical-solution-corpora in morluto/jacobian) into .claude/skills/decompose-mathematical-solution-corpora in your project. Claude Code loads it when a task matches its description.

How do I install Decompose Mathematical Solution Corpora in Codex?

Run `npx skills add morluto/jacobian --skill decompose-mathematical-solution-corpora -a codex`. Or copy the skill folder (.agents/skills/decompose-mathematical-solution-corpora in morluto/jacobian) into .agents/skills/decompose-mathematical-solution-corpora in your project. Codex loads it when a task matches its description.

Can I use Decompose Mathematical Solution Corpora 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 morluto/jacobian --skill decompose-mathematical-solution-corpora -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/decompose-mathematical-solution-corpora, .gemini/skills/decompose-mathematical-solution-corpora, .github/skills/decompose-mathematical-solution-corpora and .opencode/skills/decompose-mathematical-solution-corpora in your project.

What does Decompose Mathematical Solution Corpora need to run?

SKILL.md names no scripts, command-line tools or credentials: Decompose Mathematical Solution Corpora is instructions for the agent only. Our summary lists: Python 3.

Does Decompose Mathematical Solution Corpora 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 Decompose Mathematical Solution Corpora 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. Review the folder before installing.

What licence does Decompose Mathematical Solution Corpora use?

Decompose Mathematical Solution Corpora is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Decompose Mathematical Solution Corpora use?

About 1.9k tokens (SKILL.md is roughly 7.7k 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 293 tokens, read only when the agent opens those files.

What are the alternatives to Decompose Mathematical Solution Corpora?

Skills that share tags, products or a category with Decompose Mathematical Solution Corpora: Setting Up Papergraph (lotchuazzz-crypto/papergraph-mcp, 285 stars), MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Decompose Mathematical Solution Corpora?

morluto (a GitHub user) maintains it in morluto/jacobian, which has 205 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 5, 2026.

Source: morluto/jacobian on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.