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Get Started Free →Send HTTP requests with automatic configuration and authentication. Use when testing APIs, webhooks, or any HTTP endpoint.
.claude/skills/dicklesworthstone-send-request/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 3% | 0% |
Sends HTTP requests with:
.pi-super-curl/config.json)@endpoint-name{{uuid}}, {{uuidv7}}, {{env.VAR}}, {{timestamp}}$VAR syntax for secrets in configRun the send-request.cjs script with request parameters:
bash# First, find the script path (use -L to follow symlinks) SCRIPT=$(find -L ~/.pi/agent/skills -name "send-request.cjs" 2>/dev/null | head -1) # Then use it node "$SCRIPT" <METHOD> "<URL>" [options] 2>&1
Parameters:
METHOD: GET, POST, PUT, PATCH, DELETEURL: Full URL or @endpoint-name from config--body '{"key": "value"}': Request body (JSON, supports templates)--header 'Name: Value': Custom header (repeatable)--save: Save response to ~/Desktop/api-responses/--stream: Stream SSE responsesExamples:
bash# Simple GET request node "$SCRIPT" GET "https://httpbin.org/get" 2>&1 # POST with JSON body and templates node "$SCRIPT" POST "@chat" --body '{"id": "{{uuidv7}}", "user": "{{env.USER_ID}}"}' 2>&1 # Named endpoint from config node "$SCRIPT" GET "@health" 2>&1 # With custom header node "$SCRIPT" GET "https://api.example.com/data" --header "X-Custom: value" 2>&1
The script reads .pi-super-curl/config.json from the current directory (walking up) or home directory:
json{ "baseUrl": "$API_BASE_URL", "envFile": ".env", "auth": { "type": "jwt", "secret": "$JWT_SECRET", "algorithm": "HS256", "expiresIn": 3600, "payload": { "user_id": "{{env.USER_ID}}", "role": "authenticated" } }, "headers": { "Content-Type": "application/json", "X-Org-Id": "{{env.ORG_ID}}" }, "endpoints": [ { "name": "health", "url": "/health", "method": "GET" }, { "name": "chat", "url": "/api/chat", "method": "POST", "defaultBody": { "chat_id": "{{uuidv7}}", "workspace_id": "{{env.WORKSPACE_ID}}" } } ] }
| Template | Description | |----------|-------------| | {{uuid}}, {{uuidv4}} | Random UUID v4 | | {{uuidv7}} | Time-ordered UUID v7 | | {{timestamp}} | Unix timestamp (seconds) | | {{timestamp_ms}} | Unix timestamp (ms) | | {{date}} | ISO date string | | {{env.VAR}} or {{$VAR}} | Environment variables |
Two syntaxes for different contexts:
| Syntax | Use in | Example | |--------|--------|---------| | $VAR | baseUrl, auth.secret, auth.token | "baseUrl": "$API_URL" | | {{env.VAR}} | URLs, headers, body, JWT payload | "user_id": "{{env.USER_ID}}" |
json{"type": "bearer", "token": "$MY_API_TOKEN"}
json{ "type": "jwt", "secret": "$JWT_SECRET", "algorithm": "HS256", "expiresIn": 3600, "payload": { "user_id": "{{env.USER_ID}}", "email": "{{env.EMAIL}}", "role": "authenticated" } }
json{"type": "api-key", "token": "$API_KEY", "header": "X-API-Key"}
json{"type": "basic", "username": "$USER", "password": "$PASS"}
The script outputs:
[INFO] lines to stderr (method, URL, timing)/tmp/generation-output.txt (for /scurl-log)[INFO] Request completed successfully on success[ERROR] on failure| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,579 | 5,013 | -24% | 1 | 1 | 0% | 894 | 1,434 | +60% | 0 | 0 | — |
case-02 | fail→fail | 9,525 | 7,457 | -22% | 1 | 1 | 0% | 2,109 | 1,768 | -16% | 0 | 0 | — |
case-03 | fail→fail | 9,630 | 7,687 | -20% | 1 | 1 | 0% | 1,676 | 1,637 | -2% | 0 | 0 | — |
case-04 | fail→pass | 10,188 | 4,079 | -60% | 1 | 1 | 0% | 1,566 | 1,960 | +25% | 0 | 0 | — |
case-05 | fail→pass | 6,220 | 3,345 | -46% | 1 | 1 | 0% | 1,211 | 1,717 | +42% | 0 | 0 | — |
case-06 | fail→pass | 8,646 | 2,482 | -71% | 1 | 1 | 0% | 1,433 | 1,660 | +16% | 0 | 0 | — |
case-07 | fail→pass | 7,506 | 1,711 | -77% | 1 | 1 | 0% | 1,214 | 1,536 | +27% | 0 | 0 | — |
case-08 | pass→pass | 19,248 | 3,758 | -80% | 1 | 1 | 0% | 2,320 | 1,843 | -21% | 0 | 0 | — |
case-09 | fail→pass | 11,027 | 2,715 | -75% | 1 | 1 | 0% | 1,638 | 1,679 | +3% | 0 | 0 | — |
case-10 | fail→pass | 8,043 | 2,472 | -69% | 1 | 1 | 0% | 1,275 | 1,736 | +36% | 0 | 0 | — |
case-11 | pass→pass | 3,593 | 2,086 | -42% | 1 | 1 | 0% | 368 | 1,488 | +304% | 0 | 0 | — |
case-12 | pass→pass | 5,610 | 4,916 | -12% | 1 | 1 | 0% | 1,042 | 1,891 | +81% | 0 | 0 | — |
case-13 | pass→pass | 15,893 | 2,313 | -85% | 1 | 1 | 0% | 2,464 | 1,614 | -34% | 0 | 0 | — |
case-14 | fail→pass | 8,747 | 1,699 | -81% | 1 | 1 | 0% | 1,301 | 1,457 | +12% | 0 | 0 | — |
case-15 | fail→pass | 19,995 | 2,114 | -89% | 1 | 1 | 0% | 3,274 | 1,526 | -53% | 0 | 0 | — |
case-16 | fail→pass | 5,513 | 2,901 | -47% | 1 | 1 | 0% | 983 | 1,634 | +66% | 0 | 0 | — |
case-17 | pass→pass | 5,727 | 2,804 | -51% | 1 | 1 | 0% | 1,036 | 1,554 | +50% | 0 | 0 | — |
case-18 | pass→pass | 7,799 | 3,800 | -51% | 1 | 1 | 0% | 1,264 | 1,682 | +33% | 0 | 0 | — |
case-19 | pass→pass | 11,623 | 2,190 | -81% | 1 | 1 | 0% | 2,302 | 1,581 | -31% | 0 | 0 | — |
case-20 | pass→pass | 5,785 | 5,414 | -6% | 1 | 1 | 0% | 1,004 | 2,037 | +103% | 0 | 0 | — |
case-21 | pass→fail | 7,413 | 7,916 | +7% | 1 | 1 | 0% | 1,702 | 2,893 | +70% | 0 | 0 | — |
case-22 | pass→pass | 5,147 | 4,904 | -5% | 1 | 1 | 0% | 834 | 2,038 | +144% | 0 | 0 | — |
case-23 | pass→pass | 5,575 | 3,894 | -30% | 1 | 1 | 0% | 955 | 1,908 | +100% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 20 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +35 percentage points is the difference between those two pass rates over the 20 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.