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A research project can leave you with code in one place, results
in another,

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and an important decision buried in a conversation.

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When you return, which result belongs to which experiment, and
what should happen next?

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That is the idea behind Dr. Claw: a persistent workspace for
human-directed research.

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It wraps existing command-line coding agents, adding
orchestration, state, and audit views around their execution.

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The researcher governs goals, constraints, and acceptance; the
system handles the executable workflow.

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We will follow an original project illustration, then examine a
recorded recovery and the paper's pilot comparison.

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The workspace maintains four state objects: a Task Graph, an
Artifact Store, a Decision Log, and an Execution Trace.

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The Task Graph represents what must be done and the dependencies
between steps.

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The Artifact Store holds the produced files and outputs,
connecting completed work to the plan.

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The Decision Log retains interventions, while the Execution
Trace preserves what actions and observations occurred.

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Together, these objects make an evolving project inspectable
beyond its latest message.

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The mechanism is continuity of recorded state: later steps build
on work that can be reviewed and recovered.

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Suppose you want to compare two ways of preparing a dataset.

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You specify the question, allowed operations, and what evidence
will be acceptable.

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The workspace converts that brief into a dependency-aware task
plan:

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prepare inputs, run experiments, analyze results, and draft the
report.

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Its loop is Plan, Execute, Verify, and Write-back. Outputs enter
the artifact store and task statuses update.

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At a checkpoint, you can inspect the evidence and revise the
next step

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rather than accepting a finished-looking report automatically.

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The intervention is retained, so a changed direction remains
part of the process history.

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That lets the next executor work from the shared project context
without losing the researcher's decision.

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The interface provides linked views for orchestration, skills,
and the task list.

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Skills are reusable instructions for stages such as literature
processing, experiments, analysis, and writing.

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The task's stage and type can suggest skills; users can filter
the catalogue or invoke a skill manually.

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Versioning and schema checks make an activated skill
identifiable for later replay and audit.

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The task list exposes progress, objectives, and linked skills,
and provides an entry point for the next action.

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A suggested skill is not guaranteed to run.

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The paper distinguishes recommending a capability from verifying
its invocation.

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The paper demonstrates recovery by inducing a wrong-path failure
within one research mini-project.

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The error is captured in the execution trace, while the prior
project state remains available.

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The system halts on the error, then recovers in place with all
five pre-existing files retained.

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The recorded walkthrough contains twenty-three tool events.

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Corrected artifacts are added without wiping the earlier
attempt.

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This is a single-condition design demonstration,

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not a matched experiment showing faster or better recovery than
another system.

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For our illustration, the useful idea is to fix the failed input
step while keeping the completed work and its history.

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The automated pilot compares Dr. Claw with the bare command-line
agent it wraps,

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using the same GPT-five point four backend.

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Three medical-research tasks receive the same open-ended
instruction,

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without a list of all expected research components.

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Completeness is the fraction of twenty-one research-practice
elements present in the produced files.

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The checks cover items such as reproducibility, multiple models,
calibration, subgroup analysis,

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and real literature citations.

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This is a coverage measure. It does not establish that every
analysis or scientific claim is correct.

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The wrapper changes as one bundle: task graph, state, skills,
and workflow instructions.

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The comparison does not isolate one component.

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Dr. Claw's pooled completeness is zero point nine five two,

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compared with zero point eight seven three for the bare agent.

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It exceeds the bare agent on two tasks and ties on the third.

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The observed difference comes from research-practice coverage,
including subgroup analysis and real citations,

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rather than stronger modeling checks.

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There is only one run per task. The reported bootstrap interval
for the pooled difference includes zero.

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The pilot therefore gives directional evidence about this
orchestration bundle,

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without a statistically confirmed superiority claim.

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Persisted audit objects are architectural affordances,

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not an independent performance score that the bare agent was
designed to satisfy.

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Dr. Claw's contribution is a workspace that links human
decisions with existing agent execution

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through persistent research state.

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The original project illustration shows how a plan, its
artifacts, and later interventions can stay connected.

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The recorded recovery shows prior files surviving a failed step,

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while the pilot separately measures expected-component coverage.

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These are different pieces of evidence: recoverability, process
visibility, and output coverage.

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The researcher's acceptance decision still matters, because a
traceable process needs its conclusions checked.

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The useful unit is the continuing project: what was attempted,
what changed, and what evidence supports the next step.