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Audit a week the way Arnold Bennett's 'How to Live on 24 Hours a Day' (1908) prescribes — time as the one income that cannot be increased, the day-within-the-day, and starting with 90 minutes, not a life overhaul. Use when someone says 'I have no time', 'work eats everything', 'I want to learn X but can't fit it', or asks for a time audit or evening routine. Produces a time ledger, one reclaimed 'inner day' block, and Bennett's own warnings about overreach.

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Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

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Assess whether and how someone can safely stay in their own home as they age — the home hazards, the support gaps, and the modifications and services that make it work. Use when asked can my parent stay in their home safely, aging in place assessment, is it safe for them to live alone, or what do we need for them to stay home. Produces a room-by-room safety read (fall hazards, accessibility), an honest look at the daily-living and support gaps, the modifications and services that could close them, warning signs that home may no longer be safe, and how to raise it respectfully — helping a family make a clear-eyed, dignity-preserving decision. Not medical advice.

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Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.

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Search Ashby public job boards (no key needed) and read/write the Ashby ATS: candidates, jobs, applications. Trigger phrases: ashby, job board, candidates, hiring, job search.

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Read and manage Calendly: list scheduled events, event types, invitees, and availability schedules; cancel bookings. Trigger phrases: calendly, my meetings, scheduled events, cancel booking.

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Scan application source, agent skills, and MCP components; run penetration tests against scoped web test deployments, validate findings, and configure scan gates. Use oma-qa for broad quality reviews and domain skills for remediation.

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Detect Insecure Direct Object Reference (IDOR) vulnerabilities in a codebase using a three-phase approach: recon (find candidates), batched verify (check authorization in parallel subagents, 3 candidates each), and merge (consolidate batch results). Checks endpoints for missing ownership or authorization checks on user-supplied identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/idor-results.md. Use when asked to find IDOR or authorization bypass bugs.

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Detect hardcoded sensitive data (API keys, access tokens, private keys, passwords, etc.) in publicly accessible code — frontend JavaScript, mobile apps, client-side bundles, and HTML templates. Uses a three-phase approach: recon (find secret candidates), batched verify (confirm real secrets in public code paths, 3 candidates each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/hardcodedsecrets-results.md. Use when asked to find hardcoded secrets, leaked API keys, or exposed credentials.

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Scan a Kotlin or Compose Multiplatform diff for known footgun shapes and open the matching trap to confirm each hit. Use before committing, when reviewing a Kotlin change, or to audit a codebase.

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CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.

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Use when positioning an American Anthropologist (AA) manuscript against the literature so it reads as a four-field contribution. AA readers span sociocultural, archaeology, biological, and linguistic anthropology, so the paper must engage the conversations they expect and decenter a US/Anglophone-only canon. Stakes the contribution and citational politics; it does not write the lit review.

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Use when deciding whether an anthropology project fits American Anthropologist (AA) and which section to target. AA is the four-field AAA flagship, so the test is significance across anthropology (sociocultural, archaeology, biological, linguistic), not single-subfield novelty. Helps frame the question and pick a section; it does not collect data or conduct fieldwork.

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Use when calibrating expectations for the fast, decisive American Economic Review: Insights (AER: Insights) review process — conditional-accept-or-reject decisions, the single-round model, and pre-empting the objections a short paper cannot afford. Explains the process and stress-tests the paper; it does not write the response letter (see aeri-rebuttal).

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Use when building or trimming the exhibits of an American Economic Review: Insights (AER: Insights) short-format manuscript to the hard five-exhibit budget, one page each, no significance asterisks. Designs the minimum exhibit set; it does not run the analysis behind them.

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Security audit of Solidity code while you develop. Trigger on "audit", "check this contract", "review for security", "loop mode", "run the auditor in loop mode", "run 3 passes". Modes - default (full repo) or a specific filename. Loop mode runs several passes in one scan, each pass told what the earlier ones found, and ends in one combined report; it remembers findings between scans in a ledger.

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Subscribe to and scan industry newsletters for buying signals, competitor mentions, ICP pain-point language, and market shifts. Parses incoming newsletter emails via AgentMail, matches against keyword campaigns, and delivers a weekly digest of actionable signals. Use when a marketing team wants to turn newsletter subscriptions into an ongoing intelligence feed without manual reading.

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Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Tests can run via LinkedIn organic posts, cold email subject line splits, or both. Pure reasoning for variant generation and analysis — the user deploys the tests through their own tools. Use when a team can't decide between messaging angles and needs data, not opinions.

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Verify that every interactive control on a page can take keyboard focus and that its primary action fires from the keyboard rather than only the mouse, and say plainly that Tab order cannot be verified today. Use when a component was hand-tested with a mouse, a custom dropdown/modal/combobox was added, or an accessibility report flags an unreachable control.

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Test-driven development for behaviour a unit test cannot reach, by writing the expectation against the running app before writing the code. Declare the consequence first, watch it fail, implement, watch it pass. Use when building a user-facing feature, when the user asks for TDD on UI or full-stack work, when a unit test cannot express the outcome that matters, or when you want a red-green loop that runs against the real app instead of mocks.

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Use when asked to run Design Error Detection (quick defect scan), find design errors in a Simulink model, perform root cause analysis on DED findings, fix division-by-zero, overflow, dead logic or out-of-bounds defects detected by SLDV, or diagnose why missing coverage cannot be achieved (dead logic blocking coverage objectives). Do NOT use for requirement verification, test generation, Inf/NaN detection, active logic analysis, or coverage measurement.

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Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

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Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.

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Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

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。