actually-lsp-doctor
Diagnose and fix LSP setup for the current project's detected ecosystems (Rust, TypeScript, Ruby). Use when the SessionStart hook nudged about a missing LSP plugin, when the env isn't ready (no `bundle install`, no `cargo build`, missing server binary), when LSP calls are failing, or when the user invokes `/actually-lsp-doctor` directly. Walks the per-ecosystem state machine, reports what's missing, then runs the fix.
What this skill does
# /actually-lsp-doctor Parse the user's args from the invocation: - `fix` as the first arg means skip the diagnostic report and jump straight to action. - `rust` | `typescript` | `ruby` as an arg narrows focus to that ecosystem. - No args: full diagnostic report followed by interactive fix. ## Step 1: Read project state Read `.claude/actually-lsp.json` in the current project root. If missing, run detection: source `lib/detect.sh` and `lib/ecosystems.sh` from the plugin root (the env var `CLAUDE_PLUGIN_ROOT` points there), call `detect_ecosystems "$PWD"`, and for each detected ecosystem compute the current state per the rules in `CONTEXT.md`. ## Step 2: Diagnose (unless `fix` arg present) Output a per-ecosystem report. For each ecosystem: - State (from `CONTEXT.md`'s six states) - For non-`ready` ecosystems: what's needed to reach `ready` - For `dismissed` ecosystems: note them but don't propose action Keep tone terse. No celebration messaging. ## Step 3: Act For each ecosystem in `no-lsp-plugin`, `server-not-runnable`, or `error`: **`no-lsp-plugin`**: try `claude plugin install <recommended_plugin>@claude-plugins-official` via Bash. The user gets a permission prompt. If denied, output the slash command form (`/plugin install <recommended_plugin>@claude-plugins-official`) and ask the user to run it themselves. **`server-not-runnable`**: run env fixes per ecosystem via Bash. All env fixes auto-run; the user has implicit project consent. Check `readiness_source` in the state file to aim the fix: `binary` means the server binary is missing or non-functional (fix the binary, not the deps); `heuristic`/`probe` means the binary ran but the project env needs work (build deps). - **TypeScript**: `npm install`. - **Ruby**: `bundle install`, plus `gem install ruby-lsp` if `command -v ruby-lsp` is empty. - **Rust**: the binary is the usual culprit, and `cargo build` does NOT fix it. The `rust-analyzer-lsp` plugin launches a bare `rust-analyzer` from PATH, which is often a rustup proxy that is non-functional when the `rust-analyzer` component isn't installed for the active toolchain (`rust-analyzer is unavailable for the active toolchain`). Recover in order: 1. **Binary**: if `rust-analyzer --version` fails or prints `unavailable for the active toolchain` (test it, don't trust `command -v` alone, since the proxy resolves but doesn't run), run `rustup component add rust-analyzer`. This makes the bare `rust-analyzer` the LSP tool launches actually work. If `rustup` is absent, tell the user to install rust-analyzer so a working binary is first on PATH (rustup component, mise, or brew) and stop. 2. **Standard library**: rust-analyzer needs `rust-src` to load std; without it nav can return nothing. If `rustc --print sysroot` has no `lib/rustlib/src`, run `rustup component add rust-src`. 3. **Deps / proc-macros**: `cargo build` so dependency and macro-generated symbols resolve (intra-project nav works without this, but full semantics need it). Then let Step 5 re-probe to confirm the server actually answers. **`error`**: surface the cached `last_error` from the state file. Ask the user how to proceed. ## Step 4: Re-detect Re-run detection (same as Step 1's "if missing" path) and compute the new state per ecosystem. Hold the result in memory; don't write the state file yet. Step 5 may downgrade `ready` ecosystems before persistence. ## Step 5: Probe ready ecosystems via LSP The goal is to ground a `ready` verdict in an actual LSP response, not just env state. Env can say "server should run" while the server fails to answer queries (not indexed yet, wrong workspace root, crashed). For each ecosystem whose computed state is `ready`: 1. **Ensure the `LSP` tool is loaded.** If it isn't available in this session, call `ToolSearch` with query `select:LSP`. If `ToolSearch` returns no match, skip the probe for *every* ecosystem and note "LSP tool unavailable in session" in the report. Do not downgrade any state. This is an environment issue, not an ecosystem failure. 2. **Find a sample source file** under `$PROJECT_DIR`: - rust: `find "$PROJECT_DIR" -maxdepth 4 -type f -name '*.rs' -print -quit` - typescript: `find "$PROJECT_DIR" -maxdepth 4 -type f \( -name '*.ts' -o -name '*.tsx' \) -print -quit` - ruby: `find "$PROJECT_DIR" -maxdepth 4 -type f -name '*.rb' -print -quit` If no file is found, skip the probe for this ecosystem and note "no sample file" in the report. Do not downgrade. 3. **Call `LSP documentSymbol`** against the sample file at `line: 1, character: 1`. 4. **On error or non-array response:** downgrade the ecosystem to `error` and set `last_error` to the stringified LSP response (or a short summary if the response is large). Env said the server should be runnable, so a probe failure is a real LSP failure worth surfacing on the next SessionStart. 5. **On success:** count the symbols in the response array. State stays `ready`. The count is for the report only; nothing extra goes into the state file. ## Step 6: Write state and final report Write the (possibly probe-updated) per-ecosystem state to `.claude/actually-lsp.json` using `write_state` from `lib/state.sh`. Output one status line per ecosystem: - `ready` with successful probe: `<ecosystem>: ready (LSP: N symbols)` - `ready` with skipped probe: `<ecosystem>: ready (LSP: skipped, <reason>)` - `error` from probe failure: `<ecosystem>: error (LSP probe failed: <summary>)` - Other states: `<ecosystem>: <state>` plus the recovery hint from Step 2 Keep tone terse. No celebration messaging.
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.