「confidence」の検索結果

Use when the user asks to write a threat / risk / vulnerability assessment, or wants the appropriate template for each type. Distinct structures and section ordering per assessment kind. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Confidence is capped by the weakest load-bearing claim, and at Moderate when the evidence is ungraded.

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Use when you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.

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Analysis of Competing Hypotheses — structured technique for evaluating multiple explanations against evidence. Use when facing ambiguous attribution or multiple plausible scenarios. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Runs on ungraded evidence when asked, labelled as such, unweighted, and capped at Moderate confidence.

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Use when you need CrowdStrike Falcon Intelligence on an indicator (IOC reputation for an IP, domain, hash, or URL — malicious confidence, linked actors, malware families, reports) OR on an adversary (threat-actor profile, origin/target search, MITRE ATT&CK TTPs, finished intel reports). Answers questions like "look up 1.1.1.1", "what TTPs does Charming Kitten use?", "which threat actors operate from Russia?", and "latest report on Mustang Panda". Commonly invoked by the four /*-investigation skills, /ioc-enrichment-workflow, /threat-actor-profiling, and the regional espionage cells. Other agents/skills can chain this for vendor-authoritative actor and finished-intel context. Requires a Falcon Intelligence subscription.

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Use when you need abuse-report history for an IPv4/IPv6 address — confidence score, total reports, distinct reporters, usage type. IP-only. Commonly invoked by /ip-investigation. Retrieval only.

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Use as the default entry point for any CTI request that doesn't name a specific skill. Activates when a user asks to investigate an indicator, profile a threat actor, write an assessment, enrich IOCs, or build detection rules, and when a user asks where a report or article comes from, how reliable it is, to grade a source, check a report, or split a report into claims, or pastes a bare article URL. Routes to the right investigation, provenance, grading or analysis skill, then auto-applies rigor skills (source rating, TLP, confidence, likelihood) on the output.

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IOC export formats and procedures. CSV, STIX 2.1, OpenIOC, MISP. Handles format conversion and packaging, and carries per-claim evidence grades (access level and claim support) from /quality-of-information-check into each format (STIX confidence, MISP attribute tags).

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Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation

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"Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level. Use this skill when the user needs to quantify downside risk, set risk limits, or report regulatory risk measures — even if they say 'worst case loss', 'portfolio risk', or 'how much could we lose'.".

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Facilitate SAFe 6.0 PI Planning events. Structure the 2-day cadence, generate PI Objectives with Business Value scores, build the Program Board for dependency visualization, and run the Confidence Vote. Use before every Program Increment when the organization follows SAFe.

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Deep OSINT reconnaissance using the MITRE ATT&CK Reconnaissance framework. Email harvesting with SMTP verification, subdomain takeover detection, certificate transparency mining, Shodan/Censys intelligence, Wayback Machine historical analysis, social media profiling, cloud storage enumeration, document metadata extraction, DNS history, credential leak checks, and passive infrastructure mapping. Uses theHarvester, amass, dnsrecon, fierce, dnstwist, dmitry, whatweb, wafw00f, exiftool, metagoofil, smtp-user-enum, swaks, waybackurls, subfinder, and crt.sh. Pure reconnaissance — no active exploitation. Produces: confidence-scored findings, infrastructure diagram, employee roster, email pattern confirmation, subdomain takeover candidates. Chains into /pentester for active testing.

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Captures and evolves user preferences with confidence levels. Maintains Voice of the Customer model with exact language, dissatisfaction and aspiration signals. Handles promotion/demotion, global vs project scoping, auto memory system, and Omi wearable data integration.

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

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

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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 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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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".

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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`.

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

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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".

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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.

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