Smart Contract Entry Point Analyzer
trailofbits/skills
Maps the state-changing entry points of a smart contract codebase and sorts them by access level, producing a structured audit report that leaves out read-only functions.
Canonical Creator entry point for DisCo skill distillation. An agent skill from VectorSpaceLab/AREX-Skill.
Apache-2.0Auto-check passed
Install Distill ML Knowledge
$ npx skills add VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill distill-ml-knowledge --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .claude/skills/distill-ml-knowledge && 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 "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .claude/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledgeType 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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill distill-ml-knowledge --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .agents/skills/distill-ml-knowledge && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .agents/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill distill-ml-knowledge --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .cursor/skills/distill-ml-knowledge && 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 "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .cursor/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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/VectorSpaceLab/AREX-Skill.git --path cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge--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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill distill-ml-knowledge --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .gemini/skills/distill-ml-knowledge && 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 "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .gemini/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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 VectorSpaceLab/AREX-Skill distill-ml-knowledgeInstalls 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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .github/skills/distill-ml-knowledge && 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 "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .github/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill distill-ml-knowledge --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge .opencode/skills/distill-ml-knowledge && 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 "distill-ml-knowledge" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge into .opencode/skills/distill-ml-knowledge/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distill-ml-knowledge", 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
distill-ml-knowledge- GitHub stars
- 330
- Token cost
- ~1.6k tokens
- SKILL.md length
- 640 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Canonical Creator entry point for DisCo skill distillation. An agent skill from VectorSpaceLab/AREX-Skill.
- Works in 4 steps: Scope the capabilities Q and define… → Ground Q in retained evidence X. For… → Construct a candidate graph G_tilde =… → …
- Turning a repository
- SKILL.md covers Identify The Anchor, Scope, Ground, Construct, Verify, Creator Construction Strategy and Handoffs And Deployment
- Runs JavaScript scripts from its folder
What it does
Distill ML Knowledge is an agent skill from VectorSpaceLab/AREX-Skill. Canonical Creator entry point for DisCo skill distillation. Use when turning a repository, paper, tutorial, dataset, benchmark, research note, task, or other source material into a verified operating skill graph. Identify the anchor, scope capabilities, ground them in evidence, construct a candidate graph, verify it into an accepted graph, and record the construction.
Its SKILL.md is about 1.6k 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/construction-strategy-and-adequacy.md`, `references/direct-construction-and-handoff.md` and `references/task-and-construction-contract.md`).
The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Turning a repository
- Other source material into a verified operating skill graph
Example prompts
- “/distill-ml-knowledge”
Requirements
- Node.js
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Scope the capabilities Q and define applicability, non-goals, candidate
- Ground Q in retained evidence X. For task-agnostic distillation,
- Construct a candidate graph G_tilde = (S_tilde, L_tilde). Use tool
- Verify the candidate graph with static, source-support, executable,
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. 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 1 file in scripts/ (JavaScript), which the agent can run.
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
Distill ML Knowledge loads about 1.6k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 640 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 VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 640 words, ~1,601 tokens.
Download SKILL.mdSave it as
.claude/skills/distill-ml-knowledge/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- distill-ml-knowledge
- description
- Canonical Creator entry point for DisCo skill distillation. Use when turning a repository, paper, tutorial, dataset, benchmark, research note, task, or other source material into a verified operating skill graph. Identify the anchor, scope capabilities, ground them in evidence, construct a candidate graph, verify it into an accepted graph, and record the construction.
- metadata.disco-role
- meta
Distill ML Knowledge
Use this skill as the canonical Creator entry point for DisCo skill distillation. Convert a distillation anchor into a verified operating skill graph that a later Researcher can load. The four paper-aligned stages are scope, ground, construct, and verify. This skill does not execute the downstream research or software task in the current Creator session.
Identify The Anchor
Read task-and-construction-contract.md and create one anchor record before material exploration:
# Distillation Anchor
- anchor kind: source | task
- anchor value:
- source anchor: repository | paper | tutorial | dataset | benchmark | other
- task anchor: `tau = (q, D, E, g)`
- source material: provided | discovered during grounding | mixed
- version/access/trust boundary:
- intended future use:
- unknowns and assumptions:Use a source anchor for task-agnostic distillation. It may be a repository,
paper, tutorial, dataset, benchmark, or comparable source and does not require a
made-up downstream task. Use a task anchor for task-oriented distillation. The
task-oriented D, E, and g values are blocking when routing or verification
would change without them; ask for clarification before the affected action.
Scope, Ground, Construct, Verify
- Scope the capabilities
Qand define applicability, non-goals, candidate skill boundaries, graph entry points, and verification targets. For a task-agnostic anchor, begin with source understanding and capability identification. For a task-oriented anchor, begin with task decomposition and capability gap analysis. - Ground
Qin retained evidenceX. For task-agnostic distillation, extract knowledge from the source anchor. For task-oriented distillation, discover permitted source material for the capability gaps, then select and record evidence. Preserve provenance, versions, exclusions, conflicts, inaccessible material, and assumptions. - Construct a candidate graph
G_tilde = (S_tilde, L_tilde). Use tool encapsulation and skill packaging for source-oriented work, or skill generation for task-oriented work. Each root and sub-skill needs a clear responsibility, progressive-disclosure route, evidence boundary, checks, and recovery behavior. - Verify the candidate graph with static, source-support, executable,
graph/link, and applicable task-level or representative-use checks. Exercise
failure recovery and repair affected skills, links, evidence mappings, or
fixtures. The result is accepted graph
Gplus construction recordR, or a candidate with explicit unverified blockers. Task-agnostic runs use source-supported representative workflows and do not invent a task-level outcome trial when no downstream task exists.
Show full SKILL.md (322 more words)Show less
Creator Construction Strategy
After a lightweight scope/preflight, read
construction-strategy-and-adequacy.md
and record exactly one Creator construction strategy. This is implementation
orchestration recorded in R, not a third distillation form:
reuse-existing: invoke one adequate visible workflow or a bounded composition, preserving its verification, deployment, recovery, handoff, and specialized importer contract.direct: execute the four stages for the current anchor and produce the operating graph now.design-reusable: pass an evidence-backed recurring construction gap todesign-meta-skill, which designs a future Creator workflow. It does not directly produce the current Researcher operating graph.
Record the layered routing fields below. Do not use the construction strategy to pretend that the anchor form is a task form:
# Routing Record
- anchor kind: source | task
- distillation form: task-agnostic | task-oriented
- anchor `z`:
- scoped capabilities `Q`:
- required evidence and verification:
- construction strategy: direct | reuse-existing | design-reusable
- reuse mode: single | compose | not-applicable
- selected visible contracts:
- uncovered recurring construction gap:
- recurrence evidence:
- construction constraints and approval state:
- decision revision:Apply auto by preferring an adequate existing workflow, then direct for a
concrete task-conditioned need or design-reusable only for a verified
recurring construction gap. A reusable preference tries reuse-existing
first and must surface a conflict rather than silently falling back to a
one-off direct run.
Handoffs And Deployment
For direct, read direct-construction-and-handoff.md
and obtain approval of the exact construction specification after grounding.
For reuse-existing, pass the anchor, Q, X, constraints, and ownership to
the selected workflow without bypassing its verification or importer. For
design-reusable, pass the complete routing handoff to
../design-meta-skill/SKILL.md; that skill must not repeat strategy selection.
After an accepted operating graph is verified, decide project or managed scope
separately. Default task-bound or uncertain graphs to
<project-dir>/.agents/skills/; use ~/.disco/agent/skills/ only for
self-contained, provenance-backed graphs with representative reuse evidence.
Keep every root and sub-skill in one scope, show exact destinations and
collisions, obtain import approval, and invoke the selected specialized or
generic locked importer once with all top-level roots.
Write researcher-handoff.md outside live skill roots with the anchor kind,
distillation form, Q summary, X provenance scope, accepted or unverified
G, R path, construction strategy, selected scope, exact imported paths,
entry point, verification evidence, and unresolved limits. Do not load the
resulting operating graph or execute the downstream task in this Creator run.
© VectorSpaceLab, 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 4 other files (scripts, references) in cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/construction-strategy-and-adequacy.md
- references/direct-construction-and-handoff.md
- references/task-and-construction-contract.md
- scripts/import_operating_skill_graph.mjs
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Distill ML Knowledge 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 |
|---|---|---|---|---|---|---|
| Distill ML Knowledge this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Smart Contract Entry Point Analyzertrailofbits/skills | 7.4k | 1 repos | ~2.4k | Automated safety check: Notes | CC-BY-SA-4.0 | |
| HyperFrames Video Entry Pointheygen-com/hyperframes | 59k | 3 repos | ~5.2k | Automated safety check: Pass | Apache-2.0 | |
| Paperclip Distillpaperclipai/paperclip | 99k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Skill Creatoropenclaw/openclaw | 392k | — | ~576 | Automated safety check: Pass | Apache-2.0 | |
| Rules Distillationaffaan-m/ECC | 276k | 2 repos | ~2.3k | Automated safety check: Pass | MIT |
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Questions about Distill ML Knowledge
What does Distill ML Knowledge do?
Canonical Creator entry point for DisCo skill distillation. An agent skill from VectorSpaceLab/AREX-Skill. Distill ML Knowledge is an agent skill from VectorSpaceLab/AREX-Skill. Canonical Creator entry point for DisCo skill distillation.
When should I use Distill ML Knowledge?
Distill ML Knowledge fits situations like: turning a repository; other source material into a verified operating skill graph.
How do I install Distill ML Knowledge in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a claude-code`. Or copy the skill folder (cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge in VectorSpaceLab/AREX-Skill) into .claude/skills/distill-ml-knowledge in your project. Claude Code loads it when a task matches its description.
How do I install Distill ML Knowledge in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a codex`. Or copy the skill folder (cli/packages/coding-agent/src/disco/skills/distill-ml-knowledge in VectorSpaceLab/AREX-Skill) into .agents/skills/distill-ml-knowledge in your project. Codex loads it when a task matches its description.
Can I use Distill ML Knowledge 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 VectorSpaceLab/AREX-Skill --skill distill-ml-knowledge -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/distill-ml-knowledge, .gemini/skills/distill-ml-knowledge, .github/skills/distill-ml-knowledge and .opencode/skills/distill-ml-knowledge in your project.
What does Distill ML Knowledge need to run?
Going by SKILL.md and its folder, Distill ML Knowledge needs JavaScript for the scripts in its folder. Our summary lists: Node.js.
Does Distill ML Knowledge 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 Distill ML Knowledge 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 Distill ML Knowledge use?
Distill ML Knowledge is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Distill ML Knowledge use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 4k tokens, read only when the agent opens those files.
What are the alternatives to Distill ML Knowledge?
Skills that share tags, products or a category with Distill ML Knowledge: Smart Contract Entry Point Analyzer (trailofbits/skills, 7.4k stars), HyperFrames Video Entry Point (heygen-com/hyperframes, 59k stars), Paperclip Distill (paperclipai/paperclip, 99k stars) and Skill Creator (openclaw/openclaw, 392k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Distill ML Knowledge?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.