AB sherpamind
Use for SherpaDesk-related requests: ticket lookup, support-history retrieval, account/user/technician analysis, stale-ticket review, workload questions, operational reporting, and open-ended natural-language questions about SherpaDesk data. This skill is a local SherpaDesk backend plus OpenClaw query layer: it requires SherpaDesk API credentials for live setup, creates workspace-local runtime state under `.SherpaMind/`, and may install an optional user-level background service. Trigger when the user mentions SherpaDesk or asks about tickets, support issues, clients/accounts, technicians, resolution history, recurring incidents, backlog, response timing, or similar support-operations analysis.
As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice
How to improve
- 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: 46. 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 67/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 4 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 122 steps, 1 vague phrases
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2715 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (23 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
- +1No license
- +2Single-language instructions
- +3Description length 702: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 122 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (8 of 13)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.