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Found 1,832 Skills
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Designs and implements CI/CD pipelines for automated testing, building, deployment, and security scanning across multiple platforms. Covers pipeline optimization, test integration, artifact management, and release automation.
Explains core Apache Beam programming model concepts including PCollections, PTransforms, Pipelines, and Runners. Use when learning Beam fundamentals or explaining pipeline concepts.
Adds Azure DevOps connector to a Power Apps code app. Use when querying work items, creating bugs, managing pipelines, or making ADO API calls.
Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist).
Diagnose and fix broken Goldsky Mirror pipelines. Use this skill whenever a user has a Mirror pipeline that is failing, stuck, terminated, won't start, is in a restart loop, or is blocked by an in-flight request. Also use when the user mentions a specific Mirror pipeline name alongside a problem — even if they don't say 'mirror' explicitly, if they're using `goldsky pipeline` commands (not `goldsky turbo`), this is the right skill. Runs CLI commands directly to check status, read errors, identify root cause, and apply fixes. For YAML syntax or config reference, use /mirror instead. For turbo pipeline problems, use /turbo-doctor instead.
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding', 'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'.
Generate candidate fixes for verified security findings. Consumes TRIAGE.json (preferred), VULN-FINDINGS.json, or a vuln-pipeline results directory. Pipeline input is delegated to the execution-verified `vuln-pipeline patch` ladder; static-analysis input gets a per-finding patch subagent + independent reviewer and is written as inert diffs for human review. Writes PATCHES/bug_NN/{patch.diff,patch_result.json}, PATCHES.md, and PATCHES.json. Use when asked to "fix the findings", "patch these vulns", "generate fixes", or "close the loop on triage".
Use to configure, set up, or repair the Sales Management agent and Agentforce Pipeline Management in a Salesforce org. Automates metadata creation for flows, prompt templates, permission sets, and data source configuration. TRIGGER when: user wants to enable Pipeline Management, configure Sales pipeline features, set up the Sales Management agent for opportunity field updates (including autonomous updates), connect enabled data sources like Einstein Conversation Insights or Einstein Activity Capture, customize opportunity stage descriptions, configure post-meeting suggestions, verify or audit configuration status, fix partially configured orgs, or troubleshoot Pipeline Management metadata issues. DO NOT TRIGGER when: user wants to build a custom agent (use agentforce-generate), configure general Agentforce tracing (use platform-tracing-agentforce-configure), work with non-Sales agents, or enable Einstein Conversation Insights or Einstein Activity Capture from scratch (provisioning is out of scope).
This skill should be used when the user asks to "add an annotation", "upload artifacts from a step", "share data between steps", "upload pipeline dynamically", "request an OIDC token inside a step", "acquire a distributed lock", "get or update a step attribute", "redact a secret from logs", "retrieve a cluster secret at runtime", or "debug environment variables in hooks". Also use when the user mentions buildkite-agent annotate, buildkite-agent artifact upload/download, buildkite-agent meta-data set/get, buildkite-agent pipeline upload, buildkite-agent oidc request-token, buildkite-agent step, buildkite-agent lock, buildkite-agent env, buildkite-agent secret get, buildkite-agent redactor add, buildkite-agent tool sign/keygen, buildkite:webhook, raw webhook payloads, or any buildkite-agent subcommand used inside a running job step.
Publishes .NET artifacts from Azure DevOps. NuGet push, containers to ACR, pipeline artifacts.
Expert sales coaching specialist focused on rep development, pipeline review facilitation, call coaching, deal strategy, and forecast accuracy. Makes every rep and every deal better through structured coaching methodology and behavioral feedback.