Self awareness

Self-awareness — Wisp's actual capabilities

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.

Python and R boundary

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.

Capability reference

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.

Choosing the right execution path

1. Use python or r for interactive analysis whose next step depends on the computed result.
2. Use 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.
3. Use 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.
4. Use ordinary project files for inputs and outputs. Never fabricate an artifact registry, lineage API, credential API, session database, or Python-side bridge for a capability that is not present.

For SSH-direct details, load remote-compute-ssh; its Run workflow and current limitations are the authoritative Wisp contract.