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概要と使いどころ
Detect missing glyphs, icons, and HD variants by comparing registries against glyph mapping files, icon directories, and manifests. Reports gaps for skills, agents, and teams across all palettes.
日本語の概要は準備中です。原文の説明を表示しています。
Shared conventions for every SDLC development-phase architect agent: hard rules, code quality bar, workflow steps (superpowers invocation, spec reading, codebase exploration, verification), and the report/compact-summary contract. Architects load this skill first, then apply their stack-specific instructions on top.
日本語の概要は準備中です。原文の説明を表示しています。
Create or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
日本語の概要は準備中です。原文の説明を表示しています。
Produce a fresh, read-only status report for a Senpai research track from GitHub, W&B, and the current agent's local controller state.
日本語の概要は準備中です。原文の説明を表示しています。
Audit recent tool use across local Senpai OpenHands root and recursively delegated conversations without exposing tool arguments. Use for provider comparisons, tool-loop diagnosis, status-poll analysis, error rates, and latency reports.
日本語の概要は準備中です。原文の説明を表示しています。
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.
日本語の概要は準備中です。原文の説明を表示しています。
Use when writing an SELinux or AppArmor policy, a seccomp-bpf filter, enabling CET, PAC, or BTI, or triaging a kernel CVE. Not for KASAN report analysis: use kernel-debugging.
日本語の概要は準備中です。原文の説明を表示しています。
Use when collecting sampling profiles with perf record, reading perf report or perf annotate, measuring PMU counters with perf stat -e, or using PAPI instrumentation to interpret IPC, miss rates, MPKI, or memory bandwidth on Linux. Not for SVG rendering: use flamegraphs.
日本語の概要は準備中です。原文の説明を表示しています。
Use when reducing header bloat and compilation cascades with Include What You Use, interpreting IWYU reports, mapping files, forward declarations, or CMake integration.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to be walked through code, not handed a report: "walk through this", "guided code walk", or "explain this codebase". Not for tasks that require source or remote-system changes.
日本語の概要は準備中です。原文の説明を表示しています。
Use when reading TSan race reports, debugging deadlocks with GDB thread inspection, analyzing Helgrind lock-order violations, or reviewing std::atomic and happens-before usage.
日本語の概要は準備中です。原文の説明を表示しています。
Use when enrolling a project in OSS-Fuzz, running its helper workflow locally, or reproducing an OSS-Fuzz report. Not for remote, credential, publish, deploy, or irreversible changes.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user invokes backlog triage for a GitHub repo's open issues and PRs. Not for proactive triage, non-GitHub trackers, or single bug issues: use github-bug-report-triage.
日本語の概要は準備中です。原文の説明を表示しています。
Use when asked to set up and run coverage-guided fuzzing of Ruby code or C extensions with Ruzzy, producing crash reports or clean campaign summaries. Not for C/C++ fuzzing: use libfuzzer or libfuzzer.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a query needs direct SMT solving, an unsat core needs debugging, or another tool reports a solver timeout or unknown. Not for deciding what to prove: use proof-driven.
日本語の概要は準備中です。原文の説明を表示しています。
Use when asked to audit typography across a codebase. Produces a file:line report with concrete fixes ordered by impact and flags unverifiable rules. Not for building a type system or token scale.
日本語の概要は準備中です。原文の説明を表示しています。
Use when asked to optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a human explicitly asks for a full repository agent-compatibility pass returning a scored report with prioritized fixes. Not for tasks that require source or remote-system changes.
日本語の概要は準備中です。原文の説明を表示しています。
Select, run, and report proportional Proto UI validation for a completed change. Use before review, handoff, commit, or pull request updates, and after resolving validation failures. Cover focused evidence first, then affected graph, types, docs, packaging, consumers, release, or repository checks according to risk.
日本語の概要は準備中です。原文の説明を表示しています。
Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).
日本語の概要は準備中です。原文の説明を表示しています。
Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.
日本語の概要は準備中です。原文の説明を表示しています。
Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose and fix RFDB data disappearing after compaction, where rfdb-server reports a tiny node count (e.g. "15 nodes") on startup despite manifest_index.json showing hundreds of thousands of nodes and segment files existing on disk. Use when: (1) rfdb-server logs "Default database: N nodes" with N orders of magnitude smaller than recent analysis output, (2) /api/stats returns only the most recent commit's nodes, (3) the issue appears AFTER a compaction event (manifest with l1_node_segments populated and node_segments: []), (4) subsequent commits dropped L1 references. Root cause: ManifestStore::create_manifest() initializes l1_node_segments/l1_edge_segments to Vec::new() instead of cloning from current manifest, so any commit AFTER compaction silently orphans all L1 data even though segment .seg files still exist on disk. Compaction injects L1 fields explicitly after create_manifest, but regular commits do not.
日本語の概要は準備中です。原文の説明を表示しています。