signoz-searching-docs
Look up information in SigNoz documentation. Make sure to use this skill whenever the user asks "how do I", "where in the docs", "what does the docs say about", "find docs for", or otherwise needs reference material on SigNoz instrumentation, OpenTelemetry setup, self-hosted deployment, API endpoints, auth headers, or troubleshooting steps — even if they don't say the word "docs" explicitly. Docs lookup only — for actions inside SigNoz, the agent will pick the matching `signoz-*` action skill.
What this skill does
# SigNoz Docs Use official `signoz.io` documentation and API references only. Ground every answer in fetched docs content and cite the canonical docs URL. ## Access Docs Prefer the SigNoz MCP server tools when available; fall back to direct HTTP fetch. ### Preferred: MCP tools - `signoz:signoz_search_docs` — BM25 search over the indexed docs corpus. Pass the user's natural-language query as `query`. Narrow with `section_slug` when the question maps cleanly to a single docs section (the tool's own schema lists valid slugs — defer to it rather than memorizing). Trust the ranking — the index handles relevance. - `signoz:signoz_fetch_doc` — full markdown for one indexed page. Pass the canonical URL or `/docs/...` path; optionally narrow to a section with `heading`. ### Fallback: direct HTTP fetch If the MCP tools are unavailable, SigNoz docs support `Accept: text/markdown` natively. Discover via the sitemap: ``` GET https://signoz.io/docs/sitemap.md ``` Fetch a specific page: ``` GET https://signoz.io/docs/<path>/ Accept: text/markdown ``` ## Workflow 1. **Identify the domain** from the user's question: instrumentation, OpenTelemetry setup, querying, dashboards, alerts, troubleshooting, deployment, or API docs. 2. **Check the heuristics table below**. If a heuristic matches, read it before answering — heuristics encode product decisions (which path/method fits the user's environment), useful in both paths. 3. **Search and fetch** — pick the path based on tool availability: - **With MCP tools**: call `signoz:signoz_search_docs` with the user's query; pass `section_slug` if the domain maps cleanly to one. Read the top 1-3 results and call `signoz:signoz_fetch_doc` on the chosen URL (use `heading` to narrow if the page is large and the question is sub-section-specific). - **Without MCP tools**: grep `sitemap.md` for candidate pages, rank the best 2-5 by how directly they answer the task, and `GET` the top page(s) with `Accept: text/markdown`. Heuristic coverage is sparse — for topics without a heuristic row, skim the sitemap by section path and prefer setup/troubleshooting/API-reference pages over overviews. - Fetch **one page** for narrow questions; fetch **multiple pages** when the task spans setup + troubleshooting, or method-selection + language guide. Keep the set small. 4. **Answer from the fetched docs** and cite canonical `https://signoz.io/docs/...` URLs. 5. **Handle ambiguity deliberately**: if multiple pages are plausible, prefer the one that completes the task most directly; mention alternates only when they materially change the answer. ## Message Actions On the terminal answer, emit FE-handoff actions per the SigNoz Skills & MCP spec: - **`open_docs`** — include with the canonical URL of the primary cited page. Docs lookups are precisely the case where deep-linking to the source page helps the user read in context and verify the answer. - **`follow_up`** — 1-2 next-step prompts that build on a docs answer. After a setup guide: *"walk me through the first command"* or *"what's a common gotcha here?"*. After a concept page: *"show me a worked example."* - **Do NOT emit `apply_filter`.** Docs answers do not produce a query for an explorer page; emitting `apply_filter` would overwrite the user's working query. Verbatim guardrail: *When answering a SigNoz docs question, include an `open_docs` action on the final message with the canonical URL of the primary cited page.* ## Domain Heuristics Read the matching heuristic file **before** fetching docs. Each file contains decision logic to route the user to the right guide. | Topic | Trigger keywords | Heuristic file | |---|---|---| | Sending Logs | logs, log collection, logging, send logs | [sending-logs.md](./heuristics/sending-logs.md) |
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.