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AC agent-dashboard

Real-time agent dashboard for OpenClaw. Monitor active tasks, cron job health, issues, and action items from anywhere. Three setup tiers: (1) Zero-config canvas rendering inside OpenClaw, (2) GitHub Pages with 30-second polling — free, 2-minute setup, (3) Supabase Realtime + Vercel for instant websocket updates. All data stays on your machine. PIN-protected. No external services required for Tier 1. Tier 3 requires SUPABASE_URL and SUPABASE_ANON_KEY (no service_role key needed). Only operational status data is pushed (task names, cron status, timestamps) — never credentials, API keys, or file contents.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files · 1 script body ≈ 3 248 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Real-time agent dashboard for OpenClaw.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationGitHubSupabaseData and analyticsAI and agentstype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
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: 8. 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 56/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 26 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 50 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3248 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • high The skill tells the model to perform an irreversible action with no human approval

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
  • -231 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 609: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.