setup-agent
Skill3 starsChanged 17 days agoMITView on GitHub
Set up and connect a new Coval agent for evaluation. Guides through agent type selection, endpoint configuration, system prompt, and default resource attachment. Use when user says "set up an agent", "connect my agent", "create an agent", "add an agent", or "configure agent endpoint".
3 other projects use a version of this.
Adopted by
Most recently taken up by
- show-karma/skillstheir own version, first seen Oct 13, 2026
- logan-bishop-sgs/sgs-agent-skillstheir own version, first seen Sep 22, 2026
- buildwithdorgham/research-agent-startertheir own version, first seen Aug 2, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- show-karma/skills★ 27same name
- buildwithdorgham/research-agent-starter★ 0same name
- logan-bishop-sgs/sgs-agent-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository33 other files it gives its agents.
- build-dashboardSkill
- coval-calibrate-metricSkill
- coval-compare-runsSkill
- coval-discover-failuresSkill
- coval-eval-auditSkill
- coval-evals-as-codeSkill
- coval-eval-startSkill
- review-llm-annotations-and-improve-promptSkill
- configure-metricsSkill
- migrate-bluejaySkill
- onboardSkill
- coval-resourcesSkill
- design-personaSkill
- personas-from-artifactsSkill
- analyze-accent-reportSkill
- analyze-adversarial-reportSkill
- analyze-audio-quality-reportSkill
- launch-runSkill
- quick-evalSkill
- run-accent-testingSkill
- run-adversarial-testingSkill
- run-audio-quality-testingSkill
- watch-runSkill
- download-audioSkill
- get-resultsSkill
- consult-sofiaSkill
- build-test-suiteSkill
- distill-test-setSkill
- huggingface-importSkill
- configure-trace-metricsSkill
- debug-tracesSkill
- optimize-trace-observabilitySkill
- setup-tracingSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
26 rules. 0 are common, 20 are distinctive, and 6 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
20
Distinctive
This author's own choices. Worth a look.
6
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 11
If $ARGUMENTS contains an agent name or URL, use it to pre-fill the relevant field and skip that question.
- Very specific: line 13
Before configuration writes, inspect the repository for Terraform ownership.
- Distinctive: line 28
If not authenticated, guide the user:
- Distinctive: line 34
If the user doesn't have a Coval account, direct them to https://coval.dev to sign up.
- Distinctive: line 53
Ask: "Would you like to configure an existing agent or create a new one?"
- Distinctive: line 55
If the user picks an existing agent, skip to Phase 3 (System Prompt) to update it, or Phase 4 (Default Resources) if the prompt is already …
- Distinctive: line 61
Ask: "What type of AI agent do you have?"
- Distinctive: line 79
Ask: "What is your agent's phone number?
- Very specific: line 82
Must start with +
- Very specific: line 83
Must contain only digits after the +
- Distinctive: line 84
Must be 10-15 digits total (including country code)
- Distinctive: line 88
Ask: "What is your agent's endpoint URL?"
- Distinctive: line 96
Ask: "What would you like to name this agent?"
- Very specific: line 98
If $ARGUMENTS provided a name, confirm it: "I'll name this agent '<name>' — sound good?"
- Distinctive: line 102
Ask: "Do you have your agent's system prompt?
- Very specific: line 104
If the user provides a prompt, store it as the prompt field.
- Distinctive: line 105
If the user skips, move on — this can be added later.
- Distinctive: line 116
If test sets or metrics exist, present them:
- Distinctive: line 129
Ask: "Would you like to attach default test sets or metrics to this agent?
- Distinctive: line 131
If yes, collect the selected IDs and update the agent:
- Distinctive: line 137
If no resources exist, suggest creating them after setup:
- Distinctive: line 146
If yes, attempt a basic connectivity check and report the result.
- Distinctive: line 163
Ask: "Create this agent? (yes / edit)"
- Distinctive: line 177
If the user provided a system prompt, update the agent:
- Distinctive: line 183
If default resources were selected, attach them:
- Distinctive: line 189
Verify the created agent:
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Setup
Guide the user through connecting and configuring a new Coval agent for evaluation using the coval CLI. Follow the phases below in order, asking questions at each step.
If $ARGUMENTS contains an agent name or URL, use it to pre-fill the relevant field and skip that question.
Before configuration writes, inspect the repository for Terraform ownership. If
these resources are managed as code, use the installed coval-evals-as-code skill
or the repository's declared workflow to edit and plan the change; do not bypass
it with the direct CLI creates/updates below. Read-only inspection and design
guidance still apply. If the skill is absent, continue from the existing Terraform
configuration and public provider docs rather than assuming a sibling skill path.
Phase 0: Preflight
Step 1: Check authentication
coval whoamiIf not authenticated, guide the user:
coval loginThis prompts for an API key. Get one at https://app.coval.dev/settings (Organization > Manage > API Keys).
If the user doesn't have a Coval account, direct them to https://coval.dev to sign up.
Step 2: Inventory existing agents
coval agents list --format jsonIf agents exist, present a numbered list:
Existing agents:
1. My Voice Bot (voice) — +12345678901
2. Support Chat (chat) — https://api.example.com/chat
N. Create a new agentAsk: "Would you like to configure an existing agent or create a new one?"
If the user picks an existing agent, skip to Phase 3 (System Prompt) to update it, or Phase 4 (Default Resources) if the prompt is already set.
If no agents exist, proceed to Phase 1.
Phase 1: Agent Type Selection
Ask: "What type of AI agent do you have?"
Present the options:
voice— Receives inbound phone calls (your agent picks up)outbound-voice— Your agent calls out (agent initiates the call)chat— Text/API endpoint (OpenAI-compatible format)sms— SMS-based agentwebsocket— WebSocket connection (ws:// or wss://)
Load references/connection-types.md for type-specific guidance on required fields, validation, and common pitfalls. Use this to inform the questions in Phase 2.
Phase 2: Connection Configuration
Based on the selected type, collect the required connection details:
For voice / sms agents:
Ask: "What is your agent's phone number? (E.164 format, e.g. +12345678901)"
Validate the input:
- Must start with
+ - Must contain only digits after the
+ - Must be 10-15 digits total (including country code)
For chat / outbound-voice / websocket agents:
Ask: "What is your agent's endpoint URL?"
Validate the input:
chat/outbound-voice: Must be a valid HTTP/HTTPS URLwebsocket: Must be a valid WebSocket URL (ws:// or wss://)
Agent name:
Ask: "What would you like to name this agent?"
If $ARGUMENTS provided a name, confirm it: "I'll name this agent '<name>' — sound good?"
Phase 3: System Prompt (Optional)
Ask: "Do you have your agent's system prompt? Pasting it helps generate better test cases and metrics later."
- If the user provides a prompt, store it as the
promptfield. - If the user skips, move on — this can be added later.
Phase 4: Default Resources (Optional)
Inventory existing resources:
coval test-sets list --format json
coval metrics list --format jsonIf test sets or metrics exist, present them:
Available test sets:
1. Customer Support Evaluation (3 cases)
2. Edge Case Suite (5 cases)
Available metrics:
1. Composite Evaluation
2. Call Resolution
3. Custom: Identity VerificationAsk: "Would you like to attach default test sets or metrics to this agent? These become the agent's default config for future runs."
If yes, collect the selected IDs and update the agent:
coval agents update <agent_id> --test-set-ids <id1>,<id2> --metric-ids <id1>,<id2>If no resources exist, suggest creating them after setup:
- "You can create test sets with
/build-test-suiteand metrics with/configure-metricsafter setup."
Phase 5: Validation
For endpoint-based agents (chat, outbound-voice, websocket):
Suggest testing connectivity: "Want me to verify your endpoint is reachable?"
If yes, attempt a basic connectivity check and report the result.
For phone-based agents (voice, sms):
Confirm the E.164 format is correct and the number looks valid.
Show agent summary before creation:
Agent Summary:
Name: <name>
Type: <type>
<Phone/Endpoint>: <value>
Prompt: <first 80 chars or "Not set">
Defaults: <test sets and metrics or "None">Ask: "Create this agent? (yes / edit)"
Create the agent:
# For voice/sms:
coval agents create --name "<name>" --type <type> --phone-number "<number>" --format json
# For chat/outbound-voice/websocket:
coval agents create --name "<name>" --type <type> --endpoint "<url>" --format jsonCapture agent_id from the JSON response.
If the user provided a system prompt, update the agent:
coval agents update <agent_id> --prompt "<prompt>"If default resources were selected, attach them:
coval agents update <agent_id> --test-set-ids <ids> --metric-ids <ids>Verify the created agent:
coval agents get <agent_id> --format jsonPhase 6: Summary + Next Steps
Present the created agent:
Agent created successfully!
ID: <agent_id>
Name: <name>
Type: <type>
<Phone/Endpoint>: <value>
View in dashboard: https://app.coval.dev/agents/<agent_id>Suggest next steps based on what the user has set up:
- Design a persona for testing:
/design-persona - Build test cases for this agent:
/build-test-suite - Configure metrics to evaluate:
/configure-metrics - Run a full onboarding flow:
/onboard
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

