devops-detector
Detects CI/CD tools, containerization, and orchestration from public signals
Detects CI/CD tools, containerization, and orchestration from public signals
Quick ephemeral sticky notes for project-wide capture before formal classification
Connects to a team's Zotero library to read, sync, and write paper collections, annotations, and notes. Use when the user wants to sync PaperClaw with their Zotero library, import an existing paper collection, pull annotations from Zotero, push summaries back to Zotero items, or export a collection to BibTeX. Triggers on phrases like "sync with Zotero", "import from Zotero", "our Zotero library", "pull from Zotero", "export to BibTeX", "Zotero collection".
Tracks specific scientific claims over time — who made them, who replicated or challenged them, and whether they still stand. Use when the user wants to verify a claim before building on it, check if a key result has been replicated, register a central claim from their own work, track whether cited results remain valid, or check if a paper has been retracted. Triggers on phrases like "has this been replicated", "does this claim still hold", "track this result", "verify this finding", "has X been retracted", "provenance of this claim", "register this claim".
A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.
OKR trees, KPI dashboards, North Star Metric, leading/lagging indicators, and experiment design. Use when setting team goals, defining success metrics, building measurement frameworks, or designing A/B experiment guardrails.
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Update an existing SOP to reflect changes in tools, processes, or best practices
Trace harness generation (Phase 2.5). Use when: (1) instrumenting a system's source code to emit NDJSON traces for TLA+ trace validation, (2) writing test scenarios that exercise protocol code paths, (3) producing the first batch of traces from instrumented tests.
Record a bug to the shared Specula bug tracker. Use when: (1) a bug has been confirmed and you need to log it, (2) a bug's status needs updating (e.g., after filing an issue/PR), (3) you need to check what bugs have already been recorded.
TLA+ Verification workflow (orchestration). Use when: running the full verification loop — iterating between trace validation and model checking until both pass, ensuring spec faithfully models the system.
Specifies event tracking and analytics instrumentation requirements for a feature. Use when defining what data to collect, ensuring consistent tracking implementation, or documenting analytics requirements for engineering.
Documents the results of a completed experiment or A/B test with statistical analysis, learnings, and recommendations. Use after experiments conclude to communicate findings, inform decisions, and build organizational knowledge.
Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
Charge Carrier Mobility Analysis - Analyze carrier mobility: calculate new mobility, compute vacuum permittivity, and error analysis. Use this skill for semiconductor physics tasks involving calculate new mobility calculate vacuum permittivity calculate absolute error calculate mean square. Combines 4 tools from 2 SCP server(s).
Statistical Error Analysis - Analyze measurement errors: absolute error, scientific notation, max value, mean square, and formatting. Use this skill for statistics tasks involving calculate absolute error convert to scientific notation calculate max value calculate mean square format scientific notation. Combines 5 tools from 1 SCP server(s).
Electrical Circuit Analysis - Analyze electrical circuit: compute capacitance, convert resistance units, calculate total charge, and duty cycle. Use this skill for electrical engineering tasks involving convert resistance kOhm to Ohm calculate geometric term calculate absolute error. Combines 3 tools from 3 SCP server(s).
Length & Dimension Measurement - Precision length measurement: convert mm to m, calculate length plus width, area, and error. Use this skill for metrology tasks involving convert length mm to m calculate length plus width calculate area calculate absolute error. Combines 4 tools from 3 SCP server(s).
Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
Update the samples data file by fetching sample metadata from the microsoft/aspire-samples GitHub repository. Use when adding new samples, refreshing sample data, or ensuring samples.json stays in sync with the upstream repo.
Use when adding or modifying tests. Ensures behavior changes are covered with focused tests and fast feedback.