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BC rigs

Use when a person in their own Claude Code or Codex session mentions OpenRig, rigs or the rig command, or wants a team of coding agents (Claude Code, Codex) on their project. That includes installing, setting up, starting or updating OpenRig, seeing, getting back to or re-attaching to their OpenRig agents (including bringing a stopped team back), TUI or operator, starting or sharing a team, and joining this session to one. Operating a running rig from inside it (fleet recovery, seat handover, watchdog wakes, in-rig upgrades) is the openrig-skills skill.

mvschwarz/openrig Agent Skills author: mvschwarz Apache-2.0 1 file body ≈ 3 994 tokens Open the sourcegithub.com↗ analyzed 10 h ago

Use when a person in their own Claude Code or Codex session mentions OpenRig, rigs or the rig command, or wants a team of coding agents (Claude Code, Codex)…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: rigs (mvschwarz/openrig)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 100Steps. 40 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3994 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (33 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 559: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.