arclightning
Skill1 starsChanged 19 days agoApache-2.0View on GitHub
Pick up, work, hand off, and complete tasks tracked by Arc Lightning (`arcl`). Use in a project with an `.arcl/` directory, when asked what to work on next, to start or finish an arcl task, or to leave a resume note for the next session.
The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 2 are distinctive, and 7 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
7
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
When the arcl-mcp server is connected, call its tools instead of the commands.
- Very specific: line 20
Every --json command writes one object to standard output:
- Very specific: line 43
Start it. arcl --json task start <id> [task_start] moves the task to in progress.
- Very specific: line 46
Do not pass --force unless the person asks.
- Very specific: line 47
Read the context. arcl --json context <id> [context] returns the task with its ancestors, spec, plan, phase, blockers, dependents, …
- Distinctive: line 51
Do the work the task describes.
- Very specific: line 63
Use --evidence-file - and --note-file - to pass longer Markdown on standard input.
- Distinctive: line 68
When you find work outside the task, capture it instead of doing it:
- Very specific: line 80
Run arcl <command> --help for every flag, and arcl check to validate the project.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Arc Lightning
Arc Lightning tracks a project's tasks in .arcl/. Drive it through the arcl
CLI with --json, and read fields from the envelope rather than from terminal
text. arcl decides readiness and which transitions are allowed. Ask it
instead of working that out yourself.
When the arcl-mcp server is connected, call its tools instead of the
commands. Each step below names the tool in brackets. A tool returns the same
envelope as its command, as structured content. Its input schema lists the
fields it takes, which do not always match the command's options.
Read the envelope
Every --json command writes one object to standard output:
{ "format_version": <n>, "data": { "...": "..." } }The version depends on the command. Check it before reading data. A failure goes to standard error
as {"format_version": 1, "error": {"code", "exit_code", "message"}}, and the
command exits with that nonzero exit_code. A failed tool call returns the
same object with isError set. The
automation guide
lists the version each command returns, the readiness reason kinds, and their
ID fields.
Work the loop
- Find work. First resume your own parked work:
arcl --json list --kind task --status parked[list] lists tasks a session handed off. Otherwisearcl --json ready[ready] returnsdata.tasksin order, andarcl --json nextreturns the first asdata.task. An empty list means nothing can start. The tools return at mostlimitrecords and adata.next_cursorto pass ascursorwhile more remain. - Start it.
arcl --json task start <id>[task_start] moves the task to in progress. If it refuses withinvalid_request, runarcl --json explain <id>[explain] and readdata.readiness.reasons. Do not pass--forceunless the person asks. The tool has no force. - Read the context.
arcl --json context <id>[context] returns the task with its ancestors, spec, plan, phase, blockers, dependents, readiness, and earlier evidence.data.task.handoffholds the last resume note, anddata.task.bodyholds the acceptance criteria. - Do the work the task describes.
- Finish or hand off:
- Done:
arcl --json task complete <id> --evidence "<proof>"[task_complete], where the evidence says what shows the work is done.data.unblockedlists the tasks this made ready. Start the next one from there or go back to step 1. - Not done:
arcl --json task handoff <id> --note "<note>"[task_handoff], where the note says where the work stands and what is next. This parks the task. The next session finds it in step 1, runsarcl --json task unpark <id>[task_unpark], then starts it again and reads the note from the context.
- Done:
Use --evidence-file - and --note-file - to pass longer Markdown on
standard input.
Record new work
When you find work outside the task, capture it instead of doing it:
arcl --json capture create "<title>" --body "<what and why>"The tool is capture_create. The server also has capture_promote, show,
spec_create, plan_create, task_create, task_update and note_create,
for arcl capture promote, arcl show, arcl spec create,
arcl plan create, arcl task create, arcl task update and
arcl note create.
Run arcl <command> --help for every flag, and arcl check to validate the
project.
Does it work?
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No reviews yet
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- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

