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Agent skill

Regenerate GEAR Hash Tables

by nlfiedler in nlfiedler/fastcdc-rs

Regenerates the GEAR and GEAR_LS hash tables in the fastcdc-rs crate from its example programs instead of editing the arrays by hand, then runs the tests.

MITAuto-check passedDevelopment

Install Regenerate GEAR Hash Tables

skills CLI
$ npx skills add nlfiedler/fastcdc-rs --skill regenerate-gear-tables -a claude-code

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

GitHub CLI
$ gh skill install nlfiedler/fastcdc-rs regenerate-gear-tables --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/nlfiedler/fastcdc-rs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/regenerate-gear-tables .claude/skills/regenerate-gear-tables && 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
regenerate-gear-tables
GitHub stars
218
Token cost
~476 tokens
SKILL.md length
204 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

Regenerates the GEAR and GEAR_LS hash tables in the fastcdc-rs crate from its example programs instead of editing the arrays by hand, then runs the tests.

  • Recomputing the GEAR tables after changing the table generation logic
  • Calls cargo
  • Verifying that the embedded tables still match what the generator prints
  • Judging how a table change will affect test expectations and downstream users

What it does

In the FastCDC Rust crate, the v2016 and v2020 implementations each embed a 256-entry GEAR hash table computed from MD5 digests of the byte values, and v2020 adds a left-shifted twin called GEAR_LS. These are generated code, so the agent rebuilds them with the table64 and table64ls example programs through cargo run, then pastes the printed Rust array into the matching const in src/v2016/mod.rs or src/v2020/mod.rs, keeping the same length and formatting.

The two GEAR tables must stay identical unless the generation algorithm is being changed on purpose. A new table moves every chunk cut point, so the hardcoded expected hashes in the test fixtures fail by design and should be updated deliberately, and the change is a semver-relevant break for users who depend on deterministic chunking. Afterward the agent runs cargo test, including the tokio and futures features, to see the full impact.

When your agent uses it

  • Recomputing the GEAR tables after changing the table generation logic
  • Verifying that the embedded tables still match what the generator prints
  • Judging how a table change will affect test expectations and downstream users

Example prompts

  • “Regenerate the GEAR tables and paste them into both version modules.”
  • “I changed examples/table64.rs. Rebuild the tables and tell me which tests will break.”
  • “Verify that the GEAR_LS table in v2020 still matches what table64ls prints.”

Requirements

  • A Rust toolchain with cargo

What it can do on your machine

Read from SKILL.md and the folder at commit b025a70. 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

    Shell commands in SKILL.md call:

    • cargo

    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

Regenerate GEAR Hash Tables loads about 476 tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 204 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~476

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 nlfiedler/fastcdc-rs at commit b025a70, republished under its MIT licence (© nlfiedler). 204 words, ~476 tokens.

Download SKILL.mdSave it as .claude/skills/regenerate-gear-tables/SKILL.md (or your agent's skills folder).
name
regenerate-gear-tables
description
Regenerate the GEAR and GEAR_LS hash tables used by v2016/v2020. Use when asked to regenerate, recompute, or verify the GEAR hash tables, or after any change to the MD5-based table generation logic in examples/table64.rs or examples/table64ls.rs.

Regenerate GEAR hash tables

v2016 and v2020 both embed a 256-entry [u64; 256] GEAR hash table computed from MD5 digests of byte values 0–255. v2020 additionally embeds a left-shifted twin, GEAR_LS, used for its "rolling two bytes each time" optimization.

These tables are generated code, not hand-written — regenerate them with the example programs rather than editing the arrays directly:

shell
# Regenerate the base GEAR table (64-bit)
cargo run --example table64

# Regenerate the left-shifted GEAR_LS table
cargo run --example table64ls

Each program prints the Rust array literal to stdout. Paste the output into the appropriate GEAR / GEAR_LS const in src/v2016/mod.rs and/or src/v2020/mod.rs, replacing the existing array exactly (same length, same formatting conventions as the surrounding file).

Important

  • The tables in v2016 and v2020 are identical for GEAR — only regenerate both if you're intentionally changing the generation algorithm; a mismatch between them is a bug.
  • Changing these tables changes every chunk cut point the crate produces. This breaks the hardcoded expected hashes in the test suite (test/fixtures/SekienAkashita.jpg) by design — update those expected values deliberately, don't chase the failures as bugs. This is also a semver-relevant break for downstream users who depend on deterministic chunking output; call it out clearly if proposing this change.
  • After regenerating, run cargo test, cargo test --features tokio, and cargo test --features futures to see the full blast radius of the change.

© nlfiedler, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/regenerate-gear-tables of nlfiedler/fastcdc-rs.

Open the folder on GitHubat commit b025a70

Compare with similar skills

Regenerate GEAR Hash Tables 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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Release Skillsnexmoe/eve4213 repos~3.3kAutomated safety check: PassNone

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Works with

Categories

Questions about Regenerate GEAR Hash Tables

What does Regenerate GEAR Hash Tables do?

Regenerates the GEAR and GEAR_LS hash tables in the fastcdc-rs crate from its example programs instead of editing the arrays by hand, then runs the tests. In the FastCDC Rust crate, the v2016 and v2020 implementations each embed a 256-entry GEAR hash table computed from MD5 digests of the byte values, and v2020 adds a left-shifted twin called GEAR_LS.rs, keeping the same length and formatting.

When should I use Regenerate GEAR Hash Tables?

Regenerate GEAR Hash Tables fits situations like: recomputing the GEAR tables after changing the table generation logic; verifying that the embedded tables still match what the generator prints; judging how a table change will affect test expectations and downstream users.

How do I install Regenerate GEAR Hash Tables in Claude Code?

Run `npx skills add nlfiedler/fastcdc-rs --skill regenerate-gear-tables -a claude-code`. Or copy the skill folder (.claude/skills/regenerate-gear-tables in nlfiedler/fastcdc-rs) into .claude/skills/regenerate-gear-tables in your project. Claude Code loads it when a task matches its description.

How do I install Regenerate GEAR Hash Tables in Codex?

Run `npx skills add nlfiedler/fastcdc-rs --skill regenerate-gear-tables -a codex`. Or copy the skill folder (.claude/skills/regenerate-gear-tables in nlfiedler/fastcdc-rs) into .agents/skills/regenerate-gear-tables in your project. Codex loads it when a task matches its description.

Can I use Regenerate GEAR Hash Tables 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 nlfiedler/fastcdc-rs --skill regenerate-gear-tables -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/regenerate-gear-tables, .gemini/skills/regenerate-gear-tables, .github/skills/regenerate-gear-tables and .opencode/skills/regenerate-gear-tables in your project.

What does Regenerate GEAR Hash Tables need to run?

Going by SKILL.md and its folder, Regenerate GEAR Hash Tables needs the command-line tools its instructions call (cargo). Our summary lists: A Rust toolchain with cargo.

Does Regenerate GEAR Hash Tables 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 Regenerate GEAR Hash Tables 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 Regenerate GEAR Hash Tables use?

Regenerate GEAR Hash Tables 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 Regenerate GEAR Hash Tables use?

About 476 tokens (SKILL.md is roughly 1.9k 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 Regenerate GEAR Hash Tables?

Skills that share tags, products or a category with Regenerate GEAR Hash Tables: Migrate Core Code to Submodules (tinyhumansai/openhuman, 42k stars), OpenLogi macOS Permissions Triage (AprilNEA/OpenLogi, 23k stars), Rust Best Practices (farm-fe/farm, 5.6k stars) and RTK Rust Design Patterns (rtk-ai/rtk, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Regenerate GEAR Hash Tables?

nlfiedler (a GitHub user) maintains it in nlfiedler/fastcdc-rs, which has 218 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on August 30, 2026.

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