SKILLEMALL.ai
GitHub ↗ RU Sign in

CC durable-agent-execution-expert

Durable agent execution, Temporal AI workflows, and state replay / Eksekusi agen AI tahan-gagal, workflow Temporal, dan replay state.

The skillemall take

Promises to guide crash-resilient AI agent execution with Temporal, Restate, and Cloudflare Workflows. In reality: one text file, 3187 tokens, no scripts or code samples. Quality scores 72%, process 53%, no critical issues but zero sandbox or model runs. Safety is perfect (100), which doesn't offset the lack of hands-on material. You'll get theory on saga pause/resume and human-in-the-loop patterns, but no working examples for Claude, Cursor, or other platforms listed. Install if you need reference material; skip if you need to actually ship something.

roedyrustam/vibes-plug Agent Skills author: roedyrustam 1 file body ≈ 3 187 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Durable agent execution, Temporal AI workflows, and state replay / Eksekusi agen AI tahan-gagal, workflow Temporal, dan replay state.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCloudflareAI 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
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3187 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 133: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.