SKILLEMALL.ai
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BC tapauth

OAuth token provider for OpenClaw agents — Google Calendar, Gmail, GitHub, Slack, Linear, Notion, Vercel, Sentry, Asana, Discord, or Apify — plus user-entered passwords/API keys through the manual `secret` provider. Integrates with OpenClaw's exec secrets provider so values are resolved at startup and held in memory — no shell commands or inline credentials needed at runtime. Configure once in openclaw.json, reference tokens/secrets via SecretRef.

ClawHub Agent Skills author: Jonah Schwartz v1.0.6 MIT-0 26 files · 4 scripts body ≈ 2 024 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationGitHubSlackGmailNotionAI and agentsInfrastructurePersonal productivitytype 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 26. 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 57/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 11 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2024 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (8 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
  • -33 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 451: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (11 of 13)
  • +1License stated

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

External checks

ClawHub: clean
TapAuth is a disclosed credential-brokering skill, but users should treat its OAuth grants, manual secrets, and local grant cache as sensitive.
LLM: benign (high) · VirusTotal: · 23 Jun 2026