http-fetch
http-fetch executes a single HTTP request (GET, POST, PUT, or DELETE) and returns the response body as text without spawning an LLM agent. Use it for lightweight data-fetch steps in meta-skill DAGs where a simple HTTP call is the only operation needed; for multi-page crawling, JavaScript rendering, complex authentication, or custom headers, delegate to a sub-agent with specialized tools instead.
git clone --depth 1 https://github.com/opensquilla/opensquilla /tmp/http-fetch && cp -r /tmp/http-fetch/src/opensquilla/skills/bundled/http-fetch ~/.claude/skills/http-fetchSKILL.md
# http-fetch (sub-skill) Direct shell wrapper for a single HTTP request. Replaces ``sub-agent`` sub-Agent steps that just GET/POST a URL — order-of- magnitude faster (no LLM round-trip, no tool surface, no iteration loop). ## Inputs (``with:``) | key | required | default | notes | |-------------|----------|------------------------|----------------------------------------------------------------| | `url` | yes | — | absolute http(s) URL | | `method` | no | `GET` | `GET` / `POST` / `PUT` / `DELETE` (case-insensitive) | | `body` | no | `''` | request body, piped via stdin (for POST/PUT); empty = no body | | `timeout` | no | `30` | request timeout in seconds | | `max_bytes` | no | `2000000` | response body cap; larger payloads truncated + suffixed `…` | ## Output - Success: response body on stdout (UTF-8 decoded, truncated to ``max_bytes`` if larger; lossy decode replaces invalid bytes). - Non-2xx response: exit 1, stderr ``HTTP <code>: <reason> <body[:200]>``; stdout still carries the body for callers that want to inspect. - Network / DNS / timeout failure: exit 2, stderr cause. ## When NOT to use - Crawling multiple pages → use ``scrapling`` (via ``sub-agent``). - JS-rendered pages → use ``sub-agent`` + browser tools. - OAuth dance / multi-step auth → use ``sub-agent``. - Streaming responses → not supported (we buffer + return). ## Fallback If this skill is unavailable, callers should spawn ``sub-agent`` with a curl/requests task — same result, ~10× the latency and a non-deterministic tool loop.
Submit audio or video for multilingual dubbing, poll status, and download dubbed audio. Use when the user asks for dubbing, 多语言配音, 视频翻译配音, 译制片, or wants a source clip dubbed into another language.
Generate a structured short-video shooting script from a topic. Emits a strict, machine-parseable shot list (3 shots by default) with image prompt + video prompt + voiceover + on-screen text per shot. Trigger when the user asks for a video script, 分镜, 短视频文案, AI视频, 短剧脚本, or wants visual prompts ready for image/video generation.
Use when the user asks to schedule recurring tasks, one-off reminders, timers, or cron-style jobs through the OpenSquilla cron tool.
Multi-round research with explicit methodology, evidence tracking, and citation-tagged synthesis. Trigger on 'deep dive', 'research report', 'literature review', 'investigate X across sources', 'multi-round investigation'. Distinct from the `summarize` skill, which is a single-pass condensation; this skill maintains a state file across iterations, tracks coverage, and produces a long-form report with per-claim citations. Three execution stages: plan (scope into sub-questions), iterate (record evidence per round), compile (synthesize report). The skill itself does not fetch the web — it tells the host agent which fetches to perform via OpenSquilla's existing web tools, and records what comes back.
Read, edit, or create Microsoft Word `.docx` files. Trigger this skill whenever the user mentions a Word document, .docx file, contract, report, brief, memo, or asks to extract text, modify an existing doc, generate one from a brief, or audit tracked changes. Three execution paths: text-and-structure extraction, in-place edit-by-run (preserves styles), and create-from-scratch with python-docx. Falls back to OOXML unzip-and-patch for layout work python-docx cannot reach.
Capture the current git diff (staged, working-tree, or staged file list) as text. Direct shell call for workflows that need repository diffs without an LLM agent loop.
GitHub operations via `gh` CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available), bulk operations across many repos (script with gh api), or when gh auth is not configured.
Query the per-turn DecisionEntry log for skill co-occurrence patterns, meta-skill usage stats, and the router fixture corpus. Returns a JSON summary suitable for downstream LLM consumption. Used by meta-skill-creator's harvest step but also useful standalone for 'which skills did I use most this week?'