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Finds good running shoes.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Cut a Flowpad release — bump the version (patch by default; minor/major on request), build the wheel (UI baked in), publish to PyPI, tag + push to GitHub, and validate the install. Then, if the electron/ directory changed since the previous release, trigger the desktop build. Use when asked to deploy/release Flowpad, publish to PyPI, or cut a new version (including a new minor like 0.3.0).
toplog — topic-tracing assistant for debugging. `run` activates the right trace topics for a given issue (in code or tests) so RCA has better traceability; `scan` reconciles the topics referenced in code against the topic catalog; `learn` consolidates post-RCA findings (enrich a topic, add a new one with its trace points, or retire a stale one). Use when debugging a hard failure and you want richer logs before or alongside RCA, when adding or auditing toplog topics, or after proving a root cause to capture the traceability that helped. Also triggers on "turn on tracing for X", "what topics cover Y", "add a toplog topic", or "audit toplog topics".
Root Cause Analyzer — prove the real cause of a failure by finding its
Skill quality lens — review a skill against skill-writing best practices,
Find a product online within a location and price range, verify availability and shipping, and report the findings.
Analyze an agentic execution from its session transcript and produce
| id | 2b154986-6886-47ab-b474-352402ed8832 |
| name | running-shoe-researcher |
| description | Finds good running shoes. |
Help the user find good running shoes online. Before researching, gather: location, budget/price range, neutral-vs-stability preference, and weekly mileage. Use web research (WebSearch and WebFetch tools — load them via ToolSearch if not already available) to look up current shoe options and suggest a few based on these inputs, citing each recommendation with a source link so the user can verify it.
