「fid」の検索結果

Draft a short, prioritized questionnaire for a collaborating stablecoin team when public evidence cannot resolve important Pharos data or confidence gaps.

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。

Enriches indicators with context from threat-intel sources: planning lookups against reputation, passive DNS, WHOIS, and sandbox/sample databases, scoring confidence, and avoiding operational-security mistakes that tip off adversaries. Activates for requests to enrich IOCs, add threat context, or plan indicator lookups safely.

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Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.

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。

Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels.

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 task needs the judgment of an Elementary School Teacher — interpreting a running record or fluency check against grade-level benchmarks, deciding whether a struggling reader needs a Tier 2 intervention, running a self-contained classroom's daily schedule across multiple subjects, implementing (not writing) an IEP/504 accommodation with fidelity, or writing a parent-conference summary grounded in assessment data.

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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.

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 task needs the judgment of a professional dancer — preparing for a company audition, managing rehearsal load against injury risk, deciding whether to dance through pain or report it, negotiating a casting or return-to-dance plan with a rehearsal director, or critiquing a performance for technical and artistic fidelity to the choreography.

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 task needs the judgment of a Criminal Justice and Law Enforcement Teacher, Postsecondary — grading or defending a crime-trend assignment that must distinguish rate from raw count, mapping a CJ curriculum against ACJS certification standards or a state POST lesson-plan hour requirement, screening a student research protocol involving parolees, probationers, or incarcerated subjects for IRB Subpart C, or setting the liability and confidentiality terms for a ride-along or corrections practicum placement.

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 task needs the judgment of a Court Reporter — producing a realtime or CART feed during a deposition or trial, triaging a QC pass on a draft transcript before certification, drafting or applying an errata sheet under FRCP 30(e), reading back testimony from steno notes on a judge's request, or handling a sealed-record or in-camera confidentiality flag.

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 task needs the judgment of clergy or a pastoral-care leader — triaging a pastoral-counseling disclosure against a referral to licensed mental-health or medical care, mediating a congregational conflict, navigating clergy-privilege/confidentiality boundaries, planning an officiated ceremony with legal requirements, or right-sizing a volunteer program against committed capacity.

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 task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.

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Build comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.

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Multi-agent review resolution with trust-but-verify methodology. Accepts a PR number/URL or a local code-review report file. Extracts review comments, verifies each with parallel agents using confidence scoring (0-100), filters false positives, presents a triage report for approval, then applies fixes and replies. Use when user runs /resolve-review, mentions "resolve PR comments", "address review feedback", "fix PR suggestions", "handle review comments", or "resolve review findings".

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。

Multi-agent code review using the Pragmatic Quality framework. Orchestrates parallel review agents (CLAUDE.md compliance, bug scanning, git history, security) with independent confidence scoring to produce high-signal findings. Use when user runs /code-review, /review:code-review, requests a "code review", "review my changes", "PR review", or mentions "review diff", "review branch".

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Detect AI-generated content using pure text analysis (no external API calls). Analyzes sentence uniformity, vocabulary diversity, repetition patterns, hedging language, paragraph uniformity, and personal experience signals. Returns a 0-100 confidence score plus flagged passages. Use when user says "AI content check", "AI detection", "AI written", "content authenticity", "AI content", "detect AI".

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Circuit simulation workflows — SPICE simulation via ngspice, confidence scoring, RF chain analysis (scikit-rf), power/clock transient and AC analysis. Trigger on: "simulate", "run simulation", "spice", "check stability", "ripple analysis". Load the canonical sim skill from `project-skills/sim/SKILL.md`.

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。

Writing biosketches that build reviewer confidence — personal statement, contributions, and honest positioning.

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。

Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".

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。

Assemble a finished piece from human-written fragments via an orchestrated two-loop pipeline - an assembly loop (assembler + cold reviewer) that sequences the fragments into a draft, then a polish loop (polisher + cold reviewer), then frequency analysis and a catalog-deslop pass with a regression gate. Two modes with opposite fidelity contracts - stitch mode preserves the author's exact wording, narrative mode preserves every idea but may rewrite freely. Use when the user says "mosaic", "assemble my fragments", "stitch these notes into a post", or provides a fragments file to turn into a finished piece. For writing from a topic with research, use article-factory; for prose from scratch, use the prose skill.

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。

Adopt declscope on an existing Go codebase and drive its diagnostics to zero. Read this when introducing declscope to a repository, when choosing its configuration, when running declscope shrink for the first time, or when clearing a declscope baseline. Covers sizing each rule before enabling it, reading the diagnostics as structure, the remedy for each shape, and the measurement traps that produce false confidence. For writing new code in a repository that already runs declscope, use declscope-authoring.

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。

On-Behalf-Of (OBO) Flow for web APIs to call downstream APIs while preserving user identity in MSAL.NET

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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.

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 running a hosted Crewrift policy A/B and judging it with Wilson confidence intervals.

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。

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

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。