Agent skill

Grind

by atgreen in atgreen/openldk

OpenLDK-local autonomous work loop. An agent skill from atgreen/openldk.

GPL-3.0Auto-check passed

Install Grind

skills CLI
$ npx skills add atgreen/openldk --skill grind -a claude-code

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

GitHub CLI
$ gh skill install atgreen/openldk grind --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/atgreen/openldk.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/grind .claude/skills/grind && 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
grind
GitHub stars
271
Token cost
~2k tokens
SKILL.md length
967 words
Files
1
Skills in repo
1
Repo updated
First seen
Licence
GPL-3.0

At a glance

OpenLDK-local autonomous work loop. An agent skill from atgreen/openldk.

  • Works in 7 steps: Orient (every run) → Choose the next task — selection rubric → Anti-rat-hole guardrails → …
  • The user says /grind
  • SKILL.md covers 0. Orient (every run), 1. Choose the next task —…, 2. Anti-rat-hole guardrails and 3. Lisp/OpenLDK discipline…, plus 3 more sections
  • Calls make, javac and git

What it does

Grind is an agent skill from atgreen/openldk. OpenLDK-local autonomous work loop. Survey the Beads queue, pick the highest-value next task (prioritizing correctness and progress toward running real Java on the CLOS/JIT runtime), reprioritize the queue accordingly, then execute it end-to-end with the project's build + regression-gate discipline. Trigger when the user says "/grind", "grind", "pick the next thing and do it", "keep making progress", or wants an autonomous session that decides and works without hand-holding.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Java. The repository describes itself as: A JIT Compiler and Runtime for Java in Common Lisp. The licence is GPL-3.0.

When your agent uses it

  • The user says /grind
  • Pick the next thing and do it
  • Keep making progress
  • Wants an autonomous session that decides and works without hand-holding

Example prompts

  • “s build + regression-gate discipline. Trigger when the user says”
  • “pick the next thing and do it”
  • “keep making progress”
  • “/grind”

Workflow steps

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

  1. Orient (every run)
  2. Choose the next task — selection rubric
  3. Anti-rat-hole guardrails
  4. Lisp/OpenLDK discipline (non-negotiable)
  5. Reprioritize the queue
  6. Execute end-to-end
  7. Loop or hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 23da184. 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:

    • make
    • javac
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Grind loads about 2k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 967 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~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 atgreen/openldk at commit 23da184, republished under its GPL-3.0 licence (© atgreen). 967 words, ~1,982 tokens.

Download SKILL.mdSave it as .claude/skills/grind/SKILL.md (or your agent's skills folder).
name
grind
description
OpenLDK-local autonomous work loop. Survey the Beads queue, pick the highest-value next task (prioritizing correctness and progress toward running real Java on the CLOS/JIT runtime), reprioritize the queue accordingly, then execute it end-to-end with the project's build + regression-gate discipline. Trigger when the user says "/grind", "grind", "pick the next thing and do it", "keep making progress", or wants an autonomous session that decides and works without hand-holding.

grind — the OpenLDK progress loop

You are advancing OpenLDK: a JIT compiler and runtime for Java (JDK 25) implemented in Common Lisp (SBCL). It translates Java bytecode to Lisp, compiles that to native code, maps Java classes to CLOS classes, and runs on the real headless OpenJDK 25 runtime libraries (jimage/jrt classpath, field-backed Class metadata, invokedynamic/record/typeSwitch support). The converging goal: run real, unmodified Java programs correctly — up to and including javac itself (the javacl image).

/grind = decide what to work on next, reprioritize the queue so that decision is legible, then do the work to completion. One good increment, committed and closed, beats a half-finished heroic epic. Prefer correctness and demonstrable progress toward running real Java over breadth.

0. Orient (every run)

bash
bd prime            # workflow + memories (if context is stale)
bd ready            # claimable work
bd stats            # open/blocked/in-progress shape
bd memories java    # persistent knowledge (build gotchas, native rules, gate)

Skim the recalled Beads memories — especially the build gotcha (JAVA_HOME) and the regression-baseline notes — before touching anything. Pull the relevant src/*.lisp file on demand for the subsystem you touch; do not read all of src/. The runtime core is large:

  • src/openldk.lisp — main entry, class loading, classpath, bytecode→Lisp
  • src/native/*.lisp — native JDK method implementations (very large)
  • src/global-state.lisp — global state / special vars
  • src/monitor.lisp, src/fiber.lisp — monitors, virtual-thread fibers

1. Choose the next task — selection rubric

Score candidates in this order and pick the highest that is tractable now:

  1. Correctness first. A bug that produces wrong results (silent miscompiles, wrong values, spurious exceptions, aborts, hangs) outranks any feature or perf work. Wrong answers poison everything built on top.
  2. On the critical path to running real Java. Prefer work that lets more real, unmodified Java run correctly on JDK 25 — class loading, reflection, invokedynamic/lambda, records, the javacl/javac self-host path.
  3. Serves the engine goal. Bytecode→CLOS fidelity, JIT correctness, native JDK method coverage, monitor/thread/fiber semantics. Value work that makes the engine real, not incidental surface.
  4. Tractable and verifiable. A clear done-signal and a way to observe it end-to-end (a Java program that now prints the right value, a test that flips, a gate that ratchets up). Favor a well-scoped bug or a decomposable slice over an open-ended epic.

Deprioritize: cosmetic cleanups, speculative features, anything with no line to running-real-Java, and tracking/rollup epics — don't "work" a rollup; decompose it into a concrete child and do that.

When the top candidate is a large epic, carve off the smallest child that delivers real, verifiable value and do that this run.

2. Anti-rat-hole guardrails

  • Time-box the investigation. If after a bounded dig the task reveals itself as an architecture epic (multi-subsystem, no clear done-signal), stop: write findings + a decomposition as Beads, pick a smaller adjacent win, and proceed. Don't sink the session into a bottomless path.
  • Chase a done-signal, not a rabbit. Every task needs an observable "it works now" (a Java class that returns the right value, a testsuite test that flips PASS, a gate that ratchets). If you can't state it, you're rat-holing.
  • Commit validated increments. Don't stockpile a giant uncommitted change. Land each proven step; the next session/agent benefits.
  • A decision that's genuinely the maintainer's (a semantics policy, a big irreversible direction) — surface it briefly and pick the safe default or ask, rather than guessing and building the wrong thing at length.
Show full SKILL.md (472 more words)Show less

3. Lisp/OpenLDK discipline (non-negotiable)

  • Build with the right JDK, every time. The shell env exports a stale JAVA_HOME (openjdk@21, no lib/modules) that silently breaks the build. Always build explicitly and with pipefail so make's exit code isn't masked:

    bash
    set -o pipefail
    JAVA_HOME=/usr/lib/jvm/java-25-openjdk make openldk 2>&1 | tail -40

    The real binary is ./openldk (rebuilt by the target above). make check-jdk fails fast if JAVA_HOME isn't a JDK 25.

  • Balance parens with the pipe-symbol hazard in mind. |java/lang/Foo| symbols contain literal parens inside the pipes; reader conditionals (#+sb-fiber) skip one form but parens must still balance. A naive paren checker will lie — account for |...| and reader conditionals.

  • Follow the native-method naming rule (see the recalled Beads memory) when adding a src/native/*.lisp method that shadows a JDK-provided native.

  • Don't run heavy openldk work concurrently with the regression gate. The testsuite uses a 40s per-test timeout; a competing load causes spurious thread/exit-test failures (Thread.interrupted, System.exit, Process_*). Run the gate alone.

4. Reprioritize the queue

Make the decision legible by aligning priorities with the rubric — before you start coding:

  • Raise correctness bugs and critical-path/goal work that are currently underranked; lower speculative or off-goal items. bd update <id> --priority N.
  • Leave a one-line bd comment on anything you re-rank, saying why (e.g. "raise: wrong-result bug on the reflection path" / "lower: cosmetic, off critical path"). Keep churn minimal — reprioritize to reflect the plan, not to reshuffle the whole board.
  • File newly discovered work as Beads immediately (never a // TODO or a mental note): bd create "…" -t bug|task -p N. Model blockers with bd dep add.

5. Execute end-to-end

bash
bd update <id> --claim
  1. Implement the smallest correct change. Match surrounding Lisp idiom.

  2. Rebuild the real binary (§3) and drive the affected flow — the /verify discipline, not just tests. Wrong-result bugs demand you see the right value now: compile a tiny Java repro and run it:

    bash
    JAVA_HOME=/usr/lib/jvm/java-25-openjdk /usr/lib/jvm/java-25-openjdk/bin/javac Repro.java
    ./openldk -cp . Repro        # or: LDK_CLASSPATH=. ./openldk Repro
  3. Run the fast regression gate and confirm no dropped pass / new failure:

    bash
    JAVA_HOME=/usr/lib/jvm/java-25-openjdk make check-regression

    Use make check for the full mauve suite when the change warrants it. If you legitimately increase PASS count, consider ratcheting testsuite/baseline.sum and say so.

  4. Commit with an imperative subject citing the bead id (match the repo's ldk-xxx: … subject style); end the message with: Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

  5. bd close <id> with the commit hash and what was verified; bd sync. git push only when the user asks.

6. Loop or hand off

Report: what you picked and why (the rubric line it satisfied), the change, how you verified it (the Java repro output + gate result), what you re-ranked, and any new Beads filed. Then pick the next task (repeat) until told to stop or the queue has no tractable correctness/goal work left — at which point say so plainly rather than inventing busywork.

© atgreen, GPL-3.0. 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/grind of atgreen/openldk.

Open the folder on GitHubat commit 23da184

Compare with similar skills

Grind 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.

Grind compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Grind this skillatgreen/openldk271—~2kAutomated safety check: PassGPL-3.0
Java SDK E2E Test with Replay Snapshotgithub/copilot-sdk11k—~1.8kAutomated safety check: PassMIT
Uitestmicrosoft/vscode-java-dependency199—~1.3kAutomated safety check: PassMIT
Android Audio E2Ehyochan/react-native-nitro-sound961—~814Automated safety check: PassMIT
Edt MCP Build TestDitriXNew/EDT-MCP295—~1.4kAutomated safety check: PassAGPL-3.0
Edt MCP E2E TestingDitriXNew/EDT-MCP295—~1.3kAutomated safety check: PassAGPL-3.0

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Works with

Questions about Grind

What does Grind do?

OpenLDK-local autonomous work loop. An agent skill from atgreen/openldk. Grind is an agent skill from atgreen/openldk. OpenLDK-local autonomous work loop.

When should I use Grind?

Grind fits situations like: the user says /grind; pick the next thing and do it; keep making progress; wants an autonomous session that decides and works without hand-holding.

How do I install Grind in Claude Code?

Run `npx skills add atgreen/openldk --skill grind -a claude-code`. Or copy the skill folder (.claude/skills/grind in atgreen/openldk) into .claude/skills/grind in your project. Claude Code loads it when a task matches its description.

How do I install Grind in Codex?

Run `npx skills add atgreen/openldk --skill grind -a codex`. Or copy the skill folder (.claude/skills/grind in atgreen/openldk) into .agents/skills/grind in your project. Codex loads it when a task matches its description.

Can I use Grind 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 atgreen/openldk --skill grind -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/grind, .gemini/skills/grind, .github/skills/grind and .opencode/skills/grind in your project.

What does Grind need to run?

Going by SKILL.md and its folder, Grind needs the command-line tools its instructions call (make, javac and git).

Does Grind access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Grind 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 Grind use?

Grind is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Grind use?

About 2k tokens (SKILL.md is roughly 7.9k 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 Grind?

Skills that share tags, products or a category with Grind: Java SDK E2E Test with Replay Snapshot (github/copilot-sdk, 11k stars), Uitest (microsoft/vscode-java-dependency, 199 stars), Android Audio E2E (hyochan/react-native-nitro-sound, 961 stars) and Edt MCP Build Test (DitriXNew/EDT-MCP, 295 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Grind?

atgreen (a GitHub user) maintains it in atgreen/openldk, which has 271 GitHub stars. The repository was last updated on August 27, 2026.

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