'downstream' 검색 결과

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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Exploit an application's absolute trust in its underlying LLM (Overreliance). Use this skill to induce critical "hallucinations" (confident falsehoods) that cause downstream logical systems or automated agents tracking the LLM's output to make destructive actions or grant unauthorized access.

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Use when hunting GitHub Actions cache poisoning vulnerabilities where an attacker can inject malicious content into the CI/CD cache and have it restored by a privileged downstream workflow. Trigger on: "cache poisoning", "actions/cache", "actions/setup-node", "node_modules cache", "GitHub Actions cache", "pnpm cache", "LRU eviction", "10GB limit", "Cacheract", "poisoned cache", "workflow cache attack", supply chain via CI cache, "ng-renovate", "cache stuffing", scheduled workflow cache restore, shared cache key, "hashFiles package.json", cross-workflow cache, PR workflow release workflow same key, "npm install prefer-offline", Cacheract, Gato-X, supply chain npm token.

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White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.

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Phase 1 of the Gaia evidence verification pipeline. Use this skill whenever you need to gather, consolidate, or refresh raw evidence for named skills in the registry. Trigger phrases: "collect evidence", "populate the data lake", "gather sources", "run ev-collection", "Phase 1", "compile the evidence index", "refresh the unified lake", "aggregate skill evidence", "build the evidence database". Also invoke as the first step before running ev-star-verification, ev-adversarial-audit, or ev-link-validation — nothing downstream is meaningful without a fresh, compiled index. Covers GitHub repo links, stargazer signals, YouTube showcases, arXiv papers, peer reviews, benchmark results, blog/newsletter posts, and self-attestation artifacts.

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Rebuild, re-pin, and republish Pulp's downstream demo/example repos against a new or the latest SDK. Routes natural-language requests — "update the demos to the latest SDK", "rebuild the examples against the new SDK", "check the demos still build on the new SDK", "bump every consumer's SDK pin", "open the SDK-update PRs", "republish the demo packages" — to the `pulp minos` Managed SDK Consumer Sweep: `sweep` (build + measure min-OS floors), `update` (bump each repo's SDK pin, open PRs), and `publish-runbook` (per-repo rebuild/package/publish steps).

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Use when a task needs the judgment of a Data Engineer — designing an idempotent pipeline operation so retries and backfills don't corrupt data, choosing event-time over processing-time partitioning for time-series aggregation, enforcing schema compatibility rules before an upstream change propagates downstream, adding automated data quality checks to a pipeline, or deciding between batch and streaming architecture for a specific freshness requirement.

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Use when a task needs the judgment of a Compensation, Benefits, and Job Analysis Specialist — market-pricing a job against salary survey data, scoring a job through a point-factor or IPE job evaluation, running a pay-equity regression or cohort analysis, building or refreshing a salary structure and compa-ratio bands, classifying a job family's FLSA exemption status through the duties test, or modeling benefits plan cost (loss ratio, PEPM, self-funded vs. fully insured). Distinct from hr-people-manager (owns the comp/leveling judgment calls — whether to approve an exception, promote someone into a level), hr-specialist (owns individual-case leave/I-9/ADA/FLSA reclassification for one employee's drifted duties), and compensation-benefits-manager (owns pay philosophy, negotiation-range policy, and the department that runs this work). This role owns the mechanics: the survey match, the point score, the regression, the band math — the analysis those upstream policy and downstream judgment calls are made against.

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Assess business impact of a proposed change. Use when the user says "impact assessment", "impact analysis", "what will this change affect", "change impact", "who is impacted", "ripple effects", "downstream impact", "impact mapping", "what breaks if we change this", "readiness for change" - even if they don't explicitly say "impact assessment".

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Use this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).

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Use this skill when building, reviewing, or refreshing a RACI (Responsible / Accountable / Consulted / Informed) matrix for a Salesforce project so that every Salesforce-specific decision — data model change, automation tier choice, security model, integration boundary, deployment, license/edition — has exactly one accountable owner and a documented escalation path that downstream agents can route to. Trigger keywords: RACI matrix salesforce project, stakeholder authority salesforce, escalation path salesforce decisions, who approves data model change, salesforce decision rights, REFUSAL_NEEDS_HUMAN_REVIEW routing. NOT for the change advisory board operating model itself (use admin/change-management-and-deployment). NOT for end-user training rollout plans (use admin/change-management-and-training). NOT for the technical mechanics of permission set assignment authority (use admin/permission-set-architecture). NOT for pure stakeholder requirements elicitation (use admin/requirements-gathering-for-sf). More trigger keywords: who approves a sandbox refresh, who signs off a data load, release update activation owner, delegated admin authority vs RACI, multi-org decision rights, escalation time-box.

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Eliciting, documenting and structuring requirements for a Salesforce implementation: stakeholder discovery interviews, As-Is and To-Be process mapping, and gap analysis against standard Salesforce capability. Trigger keywords: requirements gathering, user story, As-Is To-Be, gap analysis, stakeholder interview, process mapping, business requirements, fit gap. NOT for authoring the user stories themselves - use admin/user-story-writing-for-salesforce. NOT for scoring requirements against a specific org - use admin/fit-gap-analysis-against-org. NOT for technical design decisions - use architect/solution-design-patterns. Also covers the requirements catalogue itself (one row per requirement, MoSCoW priority, source stakeholder, downstream target) and Salesforce non-functional requirements: record volume, sharing layer, integration pattern, reporting joins, mobile/offline, and licence implication.

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Documenting As-Is and To-Be business processes for a Salesforce project as swim-lane diagrams — one lane per actor, explicit decision points, parallel and exception/sad paths, and per-step automation-tier annotations that drive downstream build decisions. Trigger keywords: As-Is To-Be salesforce process, swim lane salesforce, business process map, automation candidate identification, sad path documentation, process actor mapping, manual residue. NOT for Opportunity stage and exit-criteria design — use admin/sales-process-mapping. NOT for picking Flow vs Apex for one requirement — use admin/process-automation-selection.

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Migrating Classic Knowledge (KnowledgeArticleVersion / Article Types) to Lightning Knowledge (Knowledge__kav with record types): article-type-to-record-type mapping, multi-language translation preservation, data category re-architecture, file attachment porting, version and publication-state retention, channel visibility translation, and downstream Case Feed / Community / Bot rewiring. NOT for new Lightning Knowledge setup or editorial publishing workflow design (use admin/knowledge-base-administration).

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Migrating Classic email templates (Text, HTML with Letterhead, Custom HTML, Visualforce email) to Lightning Email Templates (LET) and the Email Template Builder. Covers merge-field translation, Letterhead-to-Enhanced-Letterhead conversion, Visualforce email retention strategy, folder reorganization, sender-context (OrgWideEmailAddress) preservation, and downstream Email Alert / Process Builder / Flow rewiring. NOT for transactional Apex email sending (use apex/apex-outbound-email-patterns) or marketing email broadcasts (use Marketing Cloud / Account Engagement).

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Use this skill when writing test-first, behavior-driven acceptance criteria in Given/When/Then format for a Salesforce user story. Covers happy path, edge cases, negative paths, permission boundaries, and data-state preconditions so the AC block can drive UAT scripts and Apex test design downstream. Trigger keywords: given when then, gherkin, behavior driven AC, test first acceptance criteria, scenario outline, BDD acceptance criteria. NOT for the user-story format itself (use admin/user-story-writing-for-salesforce). NOT for UAT script writing (use admin/uat-test-case-design). NOT for Apex test method generation (use agents/test-class-generator). NOT for high-level UAT planning (use admin/uat-and-acceptance-criteria). More triggers: acceptance criteria record, ac_id, req_id on a criterion, artefact under test, negative case for a rule-type requirement, no-match fall-through criterion, oracle for a Then clause, test type apex flow manual, criteria linter, check_ac_format.py, --manifest-dir.

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Convert the canonical markdown+JSON deliverable of any SfSkills runtime agent into Excel, PDF, CSV, Notion card, ServiceNow ticket, or similar downstream format without adding dependencies to the consumer's project. NOT for an Agentforce action's output — use agentforce/agent-actions. NOT for exporting Salesforce data — use integration/bulk-api-2-patterns.

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Use this whenever a downstream Vue project needs to absorb upstream Devkit Vue changes — triggers on "update stack", "sync with devkit", "merge upstream", "pull stack updates", "resolve stack conflicts". Two-phase: ISO merge (stack modules stay byte-identical to upstream) then project alignment (apply MIGRATIONS.md, diff project modules vs `tasks` reference, /verify). Stack-code failures get an issue on `pierreb-devkit/Vue`.

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Compare two local documents and convert differences into LLM-ready Markdown in one synchronous call. Supports PDF, Word, PPT, and image documents accepted by Calliper, and returns a structured markdown diff table suitable for change review, clause comparison, downstream extraction, and rule-based validation.

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Use to structure a vague marketing request into a well-scoped brief before any real work begins. Triggers on "shape this campaign", "plan this launch", "scope this content", "marketing plan", "growth plan", "content strategy", "outbound plan", or when invoked as the /mshape slash command. Produces a filled brief (campaign, content, optimization, research, or pipeline) that downstream marketing skills can act on. Do not use for work already well-defined — go straight to execution. For engineering task shaping see prompt-shaper; for game-design intake see game-design-shaper.

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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs Solexa), or when a conversion risks silently dropping annotations or quality scores.

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

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