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Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.

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Explains DSP math concepts to developers who need the theory behind an algorithm. Use whenever the user asks how a signal-processing concept works, wants intuition behind a formula, or hits a math-shaped bug. Trigger on phrases like "how does a Fourier transform work", "explain z-transforms", "what is convolution", "why does my biquad ring", "what is windowing for", "what causes aliasing", or "how do I convert gain to dB". Also trigger when the user pastes DSP code and asks why it behaves a certain way.

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Systematic checklist for diagnosing audio artifacts. Use when the user describes a sound quality problem — clicks, pops, crackling, silence, DC offset, aliasing, phase issues, or latency problems. Trigger on phrases like "why do I hear clicks?", "my output has DC offset", "there's aliasing in my oscillator", "phase is wrong in mono", "dropout when CPU spikes". Pairs with audio-dsp-review (realtime safety) and audio-numerics-review (numeric correctness) — use those to fix once the cause is found.

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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Structured 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.

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Use when a task needs the judgment of an Electromechanical Equipment Assembler — verifying shaft/coupling alignment directly rather than trusting an initial functional test that only confirms the device runs, applying fastener torque to spec with a calibrated tool rather than by feel, routing wire to account for the equipment's full range of motion rather than its static assembled position, or distinguishing electrical from mechanical root cause when a device malfunctions.

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Use when a task needs the judgment of a detective/criminal investigator — developing and stress-testing a case theory against alternate explanations, reconciling conflicting timestamps (DVR, cell tower, witness-reported) into a defensible timeline, structuring a suspect/witness interview using a non-accusatorial method, drafting a probable-cause affidavit, or evaluating whether a confession or eyewitness identification is being treated with the right level of certainty.

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Use when a task needs the judgment of a Compliance Officer — deciding whether a suspected violation triggers a mandatory regulatory filing or disclosure, designing or risk-rating a compliance monitoring program, running a violation investigation to root cause, briefing a board/audit committee on compliance findings, or responding to a regulatory exam finding.

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Use when a task needs the judgment of a Coroner or medicolegal death investigator — determining whether a death is reportable/jurisdictional, distinguishing cause of death from manner of death, estimating a postmortem interval from scene findings, deciding whether an autopsy or toxicology screen is warranted, or completing a death certificate under scrutiny.

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Use when a task needs the judgment of a board-certified Clinical Neuropsychologist — building a neuropsychological battery to localize a brain-behavior deficit, adjudicating performance validity before interpreting any cognitive score, distinguishing a dementia subtype or mTBI sequela from a psychiatric or malingering cause, or writing a forensic, pre-surgical (epilepsy/tumor), or serial-retest report.

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Use when a task needs the judgment of a Broadcast Program Director — diagnosing a ratings-book decline into cume vs. TSL causes, deciding whether a format flip is warranted versus a clock/rotation fix, setting spot-load and stopset structure against sales pressure, or clearing content through standards-and-practices and FCC compliance review.

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Use when a task needs the judgment of a Bookkeeping, Accounting, and Auditing Clerk — reconciling a bank or credit card account, resolving an AP three-way-match discrepancy before payment, coding an expense to the correct account and department, tracing a general ledger discrepancy to root cause, or running a month-end close checklist.

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Use when a task needs the judgment of a licensed barber — diagnosing a fade/taper request against a client's actual grain and density, sequencing a shop's chair time and rebooking, deciding straight-razor vs. shavette for a shave, or reading a haircut complaint to find the real cause.

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Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

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Audit financial reconciliation workflows -- evaluate automated transaction matching engines, intercompany balance reconciliation, suspense and clearing account health, variance root-cause investigation, and close calendar integration.

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Analyze a workplace safety incident tracking system for incident classification accuracy, root cause analysis depth, OSHA 300 log recordkeeping compliance, trend analysis capabilities, and leading indicator identification..

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Analyze an incident response program for playbook coverage, MTTR optimization opportunities, evidence collection readiness, root cause analysis quality, and post-incident review effectiveness. Triggers: building a SOC, assessing IR maturity, optimizing detection-to-recovery timelines.

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Analyze franchise inventory management for par level optimization, waste tracking and root cause analysis, and theoretical vs. actual usage variance.

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Analyze manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system analysis.

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Capture learnings from a campaign, launch, sprint, or initiative using Five Whys (Taiichi Ohno, Toyota Production System) for root cause analysis and Start-Stop-Continue for action planning. Append reusable insights to knowledge/learnings.md so future runs of the OS get smarter. Use when the user asks for a retro, retrospective, post-mortem, "what did we learn", "campaign wrap-up", "lessons learned", or after a campaign or launch ends. Critical feedback loop that makes the OS compound over time. For the numbers review itself, see kpi-review. For planning the next campaign, see campaign-brief.

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Analyze budget vs. actual variances, identify root causes, and recommend corrective actions. Use when an accountant or finance professional says "explain this variance", "budget vs. actual analysis", "why did we miss budget", "favorable / unfavorable variance", "price volume variance", "management reporting commentary", "explain the P&L variance to the CFO", "what drove the cost overrun", "revenue shortfall analysis", or needs to write the narrative behind a financial result. Also trigger when someone shares a table of actuals vs. budget and needs to explain the gaps - even if they don't use the word "variance".

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**WORKFLOW SKILL** — Register and generalize agent errors into reusable lessons. USE FOR: when the user corrects the agent, capturing what went wrong, analyzing root cause, generalizing the error into a lesson that prevents recurrence. Use to register errors, analyze mistakes, create lessons, generalize corrections. DO NOT USE FOR: general debugging, writing tests, code review.

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Run dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — AGENTS.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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