「did」の検索結果

Use this when the user asks to search memory, "did we already solve this?", "how did we do X last time?", or wants work from previous sessions.

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。

One cost view across Claude Code, Codex and Grok on this machine — spend, tokens, cache hit ratio per provider and model, with unpriced models flagged. Use when asked "what did I spend", "how much did Codex cost", or to compare providers. Local logs only, nothing is sent.

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。

Update Codex's pinned `v8` / `rusty_v8` versions, validate the release-candidate path, and investigate failed V8 canary or artifact builds. Use when asked to bump V8, update `rusty_v8` artifacts, prepare or validate a V8 release candidate, check `v8-canary`, or diagnose why a V8 version update no longer builds.

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。

Periodic architecture survey — walks the module graph and reports ranked deepening candidates (shallow modules, hypothetical seams, logic behind the wrong seam). Survey, not rescue: it finds candidates and hands them to the captain; it never refactors on its own.

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。

WorkorAI talent marketplace skill: candidate job search and employer hiring with white-box match explanations via the WorkorAI MCP server (https://workorai.com/mcp). Use when the user asks to find a job, apply to jobs, respond to employer invitations, or when an employer wants to post jobs, search and evaluate candidates, invite them, and review applicants.

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。

Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).

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。

Prepare a local Python SDK release candidate in a dedicated worktree. Use only when explicitly invoked with a version.

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。

Takes a pile of applications and turns it into a ranked shortlist, scored only against the job's stated rubric, with drafted replies to every candidate, scheduled interviews, and an onboarding checklist for whoever gets hired. Picks up where job-post-builder leaves off and carries the funnel through to first day, including the payroll setup handoff. Runs on Gmail and Google Calendar, deepens with Gusto, DocuSign, and Drive or M365, and works entirely from uploaded resumes with drafted replies the owner sends by hand. Use this whenever the owner has applicants to deal with — including phrasings like "I've got 40 resumes and no time," "who should I interview," "screen these applications," "help me narrow this down," "set up interviews for the top three," or "I need to tell the rest no." Reach for it when the owner mentions applicants, resumes, candidates, or being buried in a hiring inbox.

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。

Writes prospect outreach and follow-up sequences that sound like the owner wrote them, not like an AI did. Learns a voice profile from the owner's own sent mail, grounds every message in something specific about that prospect, builds the follow-up sequence behind it, queues every send for approval, and logs each touch to the CRM. Runs draft-only when no mail connector is available, so the copy can be pasted anywhere. Use this whenever the owner needs to reach out to prospects or customers — including phrasings like "write this cold email," "draft a follow-up," "reach out to these leads," "what do I say to them," "I need a sequence for this list," "nudge the people who didn't reply," or "write it but don't make it sound like a robot." Reach for it whenever outreach copy is the deliverable, even for a single message.

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。

Gets payroll ready to run without anyone getting shorted: pulls timesheets from Gusto, QuickBooks Payroll, or an uploaded spreadsheet, totals regular, overtime, and PTO hours by person, flags every anomaly it finds — missed punches, overtime spikes, rate changes, mid-period hires and terminations, hours off the schedule — and stages the run for the owner to approve line by line before a dollar moves. Anomalies are always raised, never quietly corrected. Once the owner runs it, the journal posts to the ledger or goes to the bookkeeper. Reach for this whenever payroll comes up at all — "run payroll," "payroll is due," "check the timecards," "did everyone clock out," "how many hours did the crew put in," "why is payroll so high this week," "I need to pay the guys Friday" — and use it after cash-flow-snapshot when the owner is worried about whether payroll clears.

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。

Track and manage recruiting pipeline stages. Trigger with "recruiting update", "candidate pipeline", "how many candidates", "hiring status", or when the user discusses sourcing, screening, interviewing, or extending offers.

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。

Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.

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。

Harvest every `ponytail:` comment in the codebase into a debt ledger, so the deliberate shortcuts and deferrals ponytail leaves behind get tracked instead of rotting into "later means never". Use when the user says "ponytail debt", "/ponytail-debt", "what did ponytail defer", "list the shortcuts", "ponytail ledger", or "what did we mark to do later". One-shot report, changes nothing.

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。

Daily outbound-sourcing sweep for {{greenhouse_role}}. Searches LinkedIn for candidates matching {{sourcing_criteria}}, dedupes every match against the role's Greenhouse pipeline, and drafts a personalized outreach email per new candidate to {{recruiter_review_inbox}} — capped at {{daily_cap}} per run. Never sends outreach and never writes to Greenhouse; the recruiter reviews, sends, and owns the ATS.

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。

Daily interview-scheduling sweep for {{projectName}}. Reads {{greenhouse_pipeline}} for candidates ready to schedule and each one's interview plan, checks {{interviewer_calendars}} for panel availability, and drafts a candidate slot proposal plus a calendar invite per interviewer to {{coordinator_review_inbox}}. Never sends a proposal or invite and never makes a hiring decision; a coordinator confirms and sends everything.

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。

Drive an Apache bRPC release end to end - confirm the release slot, draft Release Notes, bump version files, cut the tag, build/sign/checksum the source tarball, stage it to Apache SVN dist/dev, verify the candidate, and draft the VOTE/RESULT/ANNOUNCE mails. Use when cutting, resuming, or verifying a bRPC release ("发版 <version>", "release apache brpc", "准备 RC", "写投票邮件", "验证发布包", "check the release candidate").

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。

Digest of what the user's Superset agents did while they were away, sweeping workspaces, tasks, and agent terminals to report what finished, what needs review, and what's blocked. Use when the user asks "what did my agents do", "what happened while I was away", wants a standup or summary of agent work, or returns after a break.

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。

Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.

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。

Reads a CARLA Leaderboard results.json — per-route driving score, route completion, infraction penalty and the infraction lists — explains how the score was computed for that leaderboard version, and rescores an existing result under the other version's rules (2.0 multiplicative vs 2.1 additive) without re-running anything. Also diffs two runs and merges sharded result files. Use when the user asks "what did I score", "why is my score low", "did my agent pass", "why don't my scores match the leaderboard", or hands over a results.json.

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。

Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.

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。

Econometrics skill for Synthetic Control Method (SCM). Activates when the user asks about: "synthetic control", "SCM", "synthetic counterfactual", "donor pool", "placebo test", "in-space placebo", "in-time placebo", "MSPE ratio", "Abadie Diamond Hainmueller", "augmented synthetic control", "penalized SCM", "synthetic DID", "合成控制", "合成控制法", "捐助池", "安慰剂检验", "合成反事实", "合成DID", "政策评估"

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 asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

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。

SEO drift monitoring — snapshot a site's SEO state and detect regressions over time. Captures a baseline (rankings/positions, indexed page count, titles & meta descriptions, canonical/robots directives, schema presence, key on-page elements) and on later runs diffs against it to surface what changed: ranking drops, pages that fell out of the index, titles/metas that were accidentally overwritten (a CMS/redeploy classic), canonicals or noindex flipped, schema that disappeared. Use this skill when the user wants to monitor SEO over time, catch regressions after a site change / migration / redeploy, set a baseline, diff against a previous state, or asks "what changed on my site's SEO" or "did my redesign break SEO". Trigger on: "SEO drift", "SEO monitoring", "track SEO over time", "did my site change break SEO", "after migration SEO", "SEO regression", "baseline my SEO", "compare SEO to last month", "my titles changed", "pages fell out of the index". For a one-time full audit use /seo-analysis.

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 one route and return one adaptive one-to-four-workgroup bundle candidate as reply-only evidence.

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。