xuzhougeng/wisp-science
Wisp-science's actual agent tool surface and runtime boundaries. Load this when deciding which Wisp tool can perform a task, checking whether Python can reach agent or desktop capabilities, choosing between interactive analysis and persisted Runs, or answering questions about delegation, images, skills, memory, artifacts, lineage, credentials, session history, and other self-introspection capabilities.
npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/self-awareness"Quick start
Install it or open the source, trigger it with a clear task, then follow the source workflow.
npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/self-awareness"Use self-awareness to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.
No structured workflow was detected; follow the original SKILL.md below.
Continue to the workflowDirect answers
Wisp-science's actual agent tool surface and runtime boundaries. Load this when deciding which Wisp tool can perform a task, checking whether Python can reach agent or desktop capabilities, choosing between interactive analysis and persisted Runs, or answering questions about delegation, images, skills, memory, artifacts, lineage, credentials, session histo…
It is relevant to workflows involving Research, Python.
SkillSignal detected this source-specific command: npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/self-awareness". Inspect the repository and command before running it.
The upstream source does not declare a dedicated Agent platform.
Static analysis detected exec-script signals. Review the cited source lines before installing; these signals are not a security audit.
This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.
SkillSignal brief
Wisp-science's actual agent tool surface and runtime boundaries. Load this when deciding which Wisp tool can perform a task, checking whether Python can reach agent or desktop capabilities, choosing between interactive analysis and persisted Runs, or answering questions about delegation, images, skills, memory, artifacts, lineage, credentials, session histo…
Useful in these contexts
Core capabilities
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyon
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
Use only tools advertised in the current conversation. Wisp exposes agent and desktop capabilities as explicit tools; do not assume that an SDK documented by another application also exists here. Some tools are conditional on the desktop session, project settings, execution context, or capability grants. If a tool is not advertised, treat it as unavailable.
Use python for persistent Python analysis and r for persistent R analysis.
Their variables and imports persist per project and execution context. Pass a
context_id such as local, ssh:<alias>, or wsl:<distro> when the work must
run somewhere other than the default local context.
The Python worker initializes an ordinary namespace with common standard-library
modules and any available convenience packages. It does not inject a Wisp
control-plane object. Code executed with python therefore cannot directly call
the agent model, spawn Agents, submit or monitor Runs, inspect Wisp credentials,
or query internal project/session metadata. Leave the Python cell and call the
corresponding Wisp tool instead.
| Need | Wisp interface | Availability and boundary |
|---|---|---|
| Read, create, or patch project files | read, write, edit | Operate on normal filesystem paths within the granted workspace. |
| Find files or text | search, grep | Use before broad manual inspection. |
| Run a short command | shell | Use for bounded foreground commands, not as a long-running job manager. |
| Interactive Python or R analysis | python, r | Persistent per project and execution context; no injected control-plane SDK. |
| Inspect a local image | view_image | Explicit tool call for a supported local image; this is not a Python method. |
| Track a multi-step plan | update_plan | Update task progress when a plan materially helps. |
| Present the completed result | attempt_completion | Wisp's normal completion path; there is no separate structured-output submission SDK. |
| Discover and load workflow guidance | search_skills, use_skill | Search by task/domain, then load the exact returned skill name. |
| Search or save durable notes | search_memory, append_memory | Available only when project memory is enabled; memory is not a transcript or telemetry database. |
| Delegate multi-file codebase reading | explore | Read-only sub-Agent with its own context and read/grep/search access. |
| Delegate general bounded tasks | delegate_tasks | Desktop-only and capability-gated. Use it only when its schema is advertised; it is not callable from Python. |
| Read a truncated delegated result | get_delegated_result | Desktop-only and available with delegation. Use only when the compact result lacks necessary detail. |
| Submit long-running work | run_in_context | Persist a Run in local, ssh:<alias>, or wsl:<distro>. Prefer this over extending shell timeouts. |
| Read one Run snapshot | get_run | Call once for an immediate status check; never poll it in a loop. |
| Wait for a Run | monitor_run | Call exactly once with the Run id. Wisp waits without repeated model calls. |
| Cancel a Run | cancel_run | Request cancellation through the persisted Run lifecycle. |
| Record project research objects | research_graph | Desktop-only. Record data assets, papers, or decisions and link existing graph nodes; it is not a generic artifact browser. |
| Create a specialist | save_specialist | Desktop-only and create-only. Editing and deletion remain in Settings. |
| Make an extra model call from Python | Not available | Continue through the normal agent turn. For bounded delegated work, use explore or advertised delegate_tasks. |
| Resolve artifact ids to paths, list a generic artifact store, or inspect lineage | Not available | Use ordinary project paths plus read/search/grep. Do not invent artifact ids, version ids, or lineage records. Run output registration is limited to the explicit output_specs contract of run_in_context. |
| Read credentials from Python or an agent tool | Not available | Wisp keeps secrets outside SQLite in its keyring path; no credential accessor is exposed to the agent. |
| Query frames, token/cost accounting, tool-call history, or the internal metadata DB | Not available | Use only conversation context and tool results already provided. Do not claim access to hidden session tables or telemetry. |
python or r for interactive analysis whose next step depends on the
computed result.run_in_context for persisted, recoverable, or long-running work. Use
monitor_run once when the result is needed in the current task, or return
the Run id for fire-and-forget work.explore when codebase understanding requires more than a couple of
reads. Use delegate_tasks only when desktop delegation is currently
advertised and the work benefits from independent or parallel Agents.For SSH-direct details, load remote-compute-ssh; its Run workflow and current
limitations are the authoritative Wisp contract.