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AD long-running-tasks

Orchestrate multi-phase background development using coding agents. Use when: (1) a project has multiple sequential tasks that should run autonomously without human intervention between steps, (2) the user wants continuous background work with crash recovery, stall detection, and progress reporting, (3) the user mentions "long-running tasks", "autonomous development", "background orchestration", or "multi-phase project". Not for: single one-shot tasks, interactive pairing, or work requiring human review between every step.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 547 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Orchestrate multi-phase background development using coding agents.

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationData and analyticsPersonal productivitySoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
98
Quality 40%
92
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Dangerous commands cmd-background-process references/orchestrator-cron.md:74
      Starts a background / autostarted process
      cd [PROJECT_PATH] && nohup [AGENT_COMMAND] '[TASK_PROMPT]' > /tmp/lrt-[PROJECT_SLUG]-worker.log 2>&1 &
    • low Dangerous commands cmd-background-process SKILL.md:94
      Starts a background / autostarted process
      cd /path/to/project && nohup <agent-command> '<task prompt>' > /tmp/lrt-<project>-worker.log 2>&1 &

    Files scanned: 4. 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 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 21 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1547 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 528: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.