Loading...
Loading...
Found 400 Skills
Rewrite `outline/claim_evidence_matrix.md` as a projection/index of evidence packs (NO PROSE), so claims/axes are driven by `outline/evidence_drafts.jsonl` rather than outline placeholders. **Trigger**: claim matrix rewriter, rewrite claim-evidence matrix, evidence-first claim matrix, matrix index, 证据矩阵重写, 从证据包生成矩阵. **Use when**: `outline/subsection_briefs.jsonl` + `outline/evidence_drafts.jsonl` are ready and you want a clean claim→evidence index for QA/writing. **Skip if**: `outline/claim_evidence_matrix.md` is already refined and consistent with evidence packs. **Network**: none. **Guardrail**: NO PROSE; do not invent facts; only cite keys present in `citations/ref.bib`; if evidence is abstract/title-only, claims must be provisional.
Review an implemented user story or task (via GitHub Pull Request) for completeness, test coverage, and code quality. Use this when asked to QA, review a PR, verify implementation, or as a follow-up to the user-story-implementer skill.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Import cookies from your real browser (Comet, Chrome, Arc, Brave, Edge) into the headless browse session. Opens an interactive picker UI where you select which cookie domains to import. Use before QA testing authenticated pages.
Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)
Browser automation CLI for AI agents. Use when the user needs to inspect, test, or automate browser behavior: navigating pages, filling forms, clicking buttons, taking screenshots, extracting page data, testing web apps, dogfooding Open Design previews, QA, bug hunts, or reviewing app quality. Prefer local Open Design preview URLs unless the user explicitly asks for external browsing.
When the user wants to layer sales onto a PLG motion, build PQL scoring, design sales handoffs from product usage signals, or plan a hybrid PLG + sales model. Also use when the user says "product-led sales," "PQL," "PQA," "when to add sales to PLG," or "enterprise PLG." For broader PLG strategy, see plg-strategy. For expansion revenue, see expansion-revenue.
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", or "test this app" on mobile. Produces a structured report with reproducible evidence: screenshots, optional repro videos, and detailed steps for every issue.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.
Loads orchestrate mode — a disciplined delivery loop that enforces BDD specs in specs/, real integration tests (no mocks), PR CI and CodeRabbit babysitting, and mandatory end-user QA via computer-use or CLI dogfooding before anything is considered done. Use when starting any non-trivial implementation task, feature build, or delivery where you want the work driven from spec to proven-shipped state rather than stopping at "tests pass".
Create a Product-Led Sales Motion Pack (PQL/PQA definition, usage-signal spec + routing/SLA, sales outreach playbook, instrumentation plan, and pilot/scale plan). Use for product-led sales, sales-assist, PLG-to-sales handoffs, and converting self-serve usage into sales opportunities. Category: Sales & GTM.