AC knowledge-agent
Build a knowledge consultant Agent on OpenClaw — turn your expertise into a 24/7 AI assistant that serves clients via Feishu groups. Use when: (1) Creating a domain-specific consulting Agent from your knowledge base, (2) Setting up AGENTS.md / SOUL.md / IDENTITY.md / MEMORY.md for a client-facing Agent, (3) Configuring Feishu group delivery with no-@ reply, (4) Designing knowledge layering strategy (what goes in AGENTS.md vs memory vs knowledge/), (5) Setting safety constraints for paid consulting scenarios, (6) Installing search skills for real-time information retrieval. Also trigger for: '知识分身', '咨询Agent', '付费咨询', '知识封装', 'consulting bot', 'knowledge agent', '培训机器人', or any question about turning expertise into an automated consulting service via OpenClaw. Based on production experience running paid consulting Agents (2026-04).
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, 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: 13. 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 64/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2357 tokens
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)
- +3Description length 842: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 18 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.