Research Mode

Run durable long-running research in OpenClaw using isolated cron iterations, persistent state, bounded execution, and milestone updates. Use when the user wants background research that can continue for hours or days, pause/resume/stop cleanly, accumulate sources/findings over time, and produce a final report instead of a single one-shot answer.

Use this skill for long-running research workflows, not for ordinary one-shot questions.

Use it for:

• background research that should continue across hours or days;
• gradual source/finding accumulation;
• pause/resume/stop control over a durable task;
• review-gated final reports or deliverables;
• follow-up research based on an approved result.

Do not use this skill for:

• quick one-shot summaries;
• a single web/search lookup;
• ordinary coding tasks;
• ad-hoc analysis that fits in one normal turn;
• tasks that do not need durable state, cron iteration, or review-loop.

Core model

Do not treat durable research as one giant prompt or one endless session. Use:

1. a research task directory under research/;
2. state.json as the control plane;
3. append-only artifacts (sources.jsonl, findings.jsonl, iterations/*.md);
4. isolated cron scheduling for repeated bounded work.

Main helper:

bash

python3 scripts/research_mode.py --help

Current hardened baseline:

• task ids are safe single path segments;
• explicit task paths are constrained to the selected research root;
• approval and delivery files must live under the task directory;
• research adequacy must pass before finalization;
• awaiting_review means review-ready, not delivery-ready;
• helper-code changes must pass scripts/check_research_mode.sh from the package root.

Default workflow

1. Create or start a task with the helper.
   • Use create when you want to inspect/attach/prepare first.
   • Use start when you want create + schedule in one step.
2. Schedule isolated work with the helper cron flow.
3. Each worker iteration must do exactly one bounded cycle:
   • begin
   • stop immediately on skipped / paused / final states
   • do one focused iteration
   • write result JSON
   • finish
   • fail if the leased iteration breaks
4. Use summary / preflight / health / reconcile / draft-report / status for operator inspection instead of manually stitching files.
5. Use pause / resume / stop and working-memory mutation helpers instead of hand-editing state.

Command families that matter

Task lifecycle

• create
• start
• schedule
• begin
• finish
• fail
• pause
• resume
• stop
• unschedule

Operator/query surfaces

• list
• status
• summary
• preflight
• health
• reconcile
• queue-status
• draft-report
• render-prompt
• prepare-runtime

Recovery handoff

• recover --apply-pending-result
• recover --refresh-derived

Review and delivery

• approve
• request-changes
• reopen
• mark-delivered
• format-delivery

Steering / working memory

Prefer helper mutation commands over direct state.json edits:

• mutate-working-memory
• add-angle
• add-constraint
• add-instruction
• set-deliverable

If there is exactly one active non-final task, user-facing commands can omit --id. If several are active, the helper should fail loudly and require explicit targeting.

Corpus helpers

Use corpus helpers when the task should carry local/web material across isolated iterations.

Available helpers:

• attach-input --file ...
• attach-input --dir ...
• attach-input --glob '.../**/*.md'
• attach-note --title ... --text ...
• attach-url-as-md --url ...
• attach-pdf --file ...

Image files attached through attach-input are preserved in the corpus manifest and marked with content_hint=image, so future begin work orders can recognize them as visual inputs rather than generic files.

These helpers should remain lightweight:

• update manifest/provenance;
• make attached material visible in future begin work orders;
• avoid turning research-mode into a heavy ingestion platform.

Runtime / local analysis

If deeper coding or local data work is needed, use prepare-runtime and keep generated scripts/exports/datasets under the task-local workspace/. Install extra Python packages only into the task-local runtime, not globally.

Recommended task-local layout after prepare-runtime:

• workspace/analysis/ — one-off analysis scripts and code notebooks-in-files
• workspace/tools/ — tiny task-specific helpers/utilities
• workspace/data/ — intermediate structured inputs / normalized datasets
• workspace/outputs/ — derived tables, JSON, CSV, reports
• workspace/outputs/screenshots/ — raw screenshots and saved visual captures
• workspace/outputs/vision/ — derived vision notes / visual interpretations / auxiliary artifacts
• workspace/tmp/ — disposable scratch artifacts
• workspace/data/analysis.sqlite — optional task-local SQLite store for structured analysis
• workspace/analysis/schema.sql — SQLite schema used for this task when DB is helpful
• workspace/analysis/queries/ — saved SQL queries / views / exports

Treat code as a first-class helper when it improves accuracy, scale, or reproducibility, especially for:

• parsing / extraction
• structured data cleanup
• deduplication / comparison
• scoring / ranking
• calculations / aggregations
• corpus-wide transforms

Treat SQLite as an equally valid helper when the task becomes structured and query-heavy, especially for:

• repeated filtering / segmentation
• deduplication / entity resolution
• joins across normalized records
• aggregation / ranking / queue generation
• coverage/accounting layers over many observations

Before creating task-specific SQLite tables, the worker should explicitly decide:

1. the 1–3 core entities;
2. their relationships;
3. likely dedup keys;
4. provenance fields (source_id, captured_at, note, confidence where relevant).

Keep the schema minimal first. Prefer a small task-fit schema over premature over-modeling.

If code materially influenced the iteration result, the worker should:

1. save the relevant script/output under the task-local workspace;
2. report code_used=true in the result payload;
3. list durable artifacts via analysis_artifacts;
4. record any important runtime deps in packages_used.

If SQLite materially influenced the iteration result, the worker should also:

1. report database_used=true in the result payload;
2. list DB/schema/query/export files via database_artifacts;
3. summarize DB purpose/tables/row counts via database_summary.

Treat vision/image analysis as another first-class helper when the task includes screenshots, maps, charts, dashboards, UI states, photos, or user-provided images. If visual evidence materially influenced the iteration result, the worker should:

1. report vision_used=true in the result payload;
2. list screenshots / visual artifacts via vision_artifacts;
3. summarize the visual purpose via vision_summary.

Use vision as a helper, not as the sole source of truth when a stronger structured/text path exists.

Do not turn a bounded research iteration into open-ended product engineering. Prefer the smallest reproducible code path that answers the question.

Current package policy: prepare-runtime --package may install arbitrary task-local pip packages. This is intentional for now. Do not claim strict production package governance until an allowlist/lock policy exists. Do not install dangerous or suspicious packages without checking their source, necessity, and install-time behavior first. If a package looks unusual or risky, record the decision/risk in the iteration instead of treating the install as routine.

Search stack defaults

• For RU / regional / local-business / SERP-harvesting research, prefer regional/local search or SERP tools before synthesis-first search.
• Use discovery tools to gather candidate sites/resources/lists, then follow direct sources with whatever tools fit the case.
• Use synthesis-first search later for broader context, summarization, or international cross-checking.
• When the task is local and a city/region is known, include it explicitly in the query rather than relying only on abstract intent.
• Write user-facing summaries and final deliverables in the same language as the user's goal/instructions unless the user asks for another language.

User updates

Send updates only when there is real value:

• task started;
• milestone reached;
• blocker / user input needed;
• final result ready.

Avoid a message on every cron tick. When helper output returns notify_user=true, prefer the returned update_text instead of inventing a fresh one. If the task runs under the default isolated cron setup with internal-only delivery, use delivery_intent as the handoff contract: send pending intent text through the available messaging surface, then call record-notification with sent or failed, and reply NO_REPLY in the cron run. For chat-launched tasks, bind the owner at create / start time with --channel, --chat-id, and when needed --thread-id / --topic-id. Use --no-owner only when notifications are intentionally disabled; otherwise a missing owner should remain visible as notification_blocked:missing_owner.

Current hardened behavior

Operator-facing surfaces

summary, runs.tsv, and task-playbook.md are now the primary inspectable operator surfaces. Prefer them over manual artifact spelunking. Finalization surfaces include operator_next_action so the operator can distinguish review-ready candidates from worker rework and human-intervention states without reverse-engineering validation findings.

Terminal reasons

Statuses stay simple (idle, running, paused, complete, failed, cancelled), but lifecycle output may also expose normalized reasons such as:

• completed:worker
• completed:budget
• completed:topic_saturated
• stopped:user
• failed:blocker
• failed:error-threshold
• rejected:completion-validation

Deliverable-aware completion checks

Completion validation is intentionally lightweight but inspectable. It may reject completion when the requested output shape is clearly not satisfied (for example weak bullet-list/comparative/overview structure).

Research adequacy gate

Do not treat finalization as the place to discover whether the research itself is incomplete. Before a task can move to finalization, the worker must pass through phase=verify and report result.adequacy.

The adequacy check is about the accumulated research, not report polish:

• does the evidence answer the user's goal;
• were explicit constraints and user instructions accounted for;
• is the requested deliverable shape understood;
• are important open questions resolved or intentionally judged nonblocking;
• is the evidence base diverse enough for the task;
• are coverage gaps, evidence risks, and contradictions recorded honestly.

If the research is not sufficient, set result.adequacy.status to the appropriate state:

• needs_research -> return to search;
• needs_analysis -> return to analyze;
• needs_synthesis -> return to synthesize;
• needs_user_input -> pause for user/operator input;
• needs_intervention -> require operator inspection.

Only set result.adequacy.status="passed" when the research can responsibly move to finalize. Lifecycle code owns attempt counters, routing, and operator_next_action; worker-provided adequacy fields are candidate claims, not trusted control decisions.

Human-ready finalization

Do not treat a task as truly final just because a report file exists. Before calling a result user-ready, make sure the primary deliverables are human-facing rather than internal-agent scaffolding:

• avoid presenting draft-named artifacts as the final output when the task is marked complete;
• avoid final reports that mainly point to internal workspace paths without giving a human-readable synthesis;
• if needed, produce a polished final report and final-named deliverables before presenting the task as done.
• if the deliverable is a file, do not make the user hunt for it in workspace paths when the platform can attach/send it or when a clear delivery path can be provided.
• if the result is too long for a convenient chat reply, package it deliberately: concise summary in chat + full file/report as attachment or clearly named artifact.

For worker-initiated completion, result.finalization is mandatory evidence, not a decorative note. Before setting should_complete=true, the worker must record:

• status="passed";
• inferred user need, intended recipient, and primary deliverable kind;
• internal artifacts versus candidate user-facing artifacts;
• blocking and nonblocking defects found during recipient-style review;
• revisions made after self-review;
• validation evidence showing what was actually checked.

If result.finalization.status is missing / not_started, blocking defects remain, validation evidence is empty, or a raw workspace artifact is exposed as the final result, finish must route the task back to finalize / rework instead of awaiting_review.

Finalization also performs lightweight candidate artifact inspection:

• candidate artifact paths must stay inside the task directory;
• existing candidate artifacts must exist and be regular files;
• generated final-report.md can be validated from final_report_markdown before the file is committed;
• Markdown candidates are checked for basic readable structure;
• .xlsx candidates must be openable as workbook ZIPs with workbook/sheet entries.

These hooks are deliberately lightweight. They prove that the declared deliverable is inspectable, not that every domain-specific quality requirement has been solved.

summary --format json, summary --format text, and task-playbook.md expose the next operator action for finalization:

• review_candidate — inspect the candidate deliverable and use approve or request-changes;
• worker_rework — let the next worker turn repair failed finalization checks;
• operator_intervention — inspect repeated or explicit finalization failures before continuing;
• verify_review_state — finalization passed, but the task is not in the expected review gate;
• continue_research — no passing finalization evidence exists yet.

summary --format json also exposes operator_attention for execution-health conditions that should wake an operator or watcher even when lifecycle status is still running, such as a stale active run with or without a pending result. Treat operator_attention.status != "ok" as actionable.

Review-ready vs delivery-ready

Do not collapse review state and delivery state:

• delivery.review_ready=true means there is an artifact ready for review.
• delivery.ready=true means the artifact is ready for user delivery.
• Worker finalization to awaiting_review sets review readiness, not delivery readiness.
• approve or mark-delivered --ready is the normal route to delivery readiness.

Integrity markers

Successful finish writes transactions.finish.status=committed with the run id and iteration. If a stale worker left .tmp/result-<run-id>.json without calling finish, use recover --apply-pending-result; valid results are applied through the normal finish path and then marked consumed.

Execution discipline

• One run = one bounded iteration.
• Worker iterations are serialized per research root by the global iteration queue.
• A begin response with status=skipped and normalized_reason=deferred:global-research-lock is normal queue waiting, not a failed worker turn.
• A begin response with status=recovered means a stale pending result was applied; the next tick may acquire a fresh lease if more work remains.
• Use queue-status, status, or summary to inspect the active holder and waiters before attempting recovery.
• state.json remains the source of truth.
• Research task ids must be safe single path segments; never use /, \, ., .., or path traversal in ids.
• --path must point to a task under the selected --root; do not operate on arbitrary filesystem paths.
• Review and delivery artifacts must live under the task directory. If an external file is relevant, attach/copy it into the task workspace first.
• For XLSX deliverables, do not combine a worksheet-level autoFilter and an Excel Table over the same range. Use a table filter or a plain worksheet filter, not both; review-ready XLSX candidates are checked with strict OOXML compatibility validation.
• Before changing the helper code, run or update tests first. Before calling code changes complete, run scripts/check_research_mode.sh from the package root.
• Do not bypass path containment by symlink or absolute path. The helper validates resolved paths; if it rejects a path, move/copy the artifact into the task workspace.
• Do not rely on chat memory between cron iterations.
• Persist important context explicitly into task artifacts.
• Record no-progress iterations honestly (meaningful_progress=false).
• Keep future changes lightweight; do not silently redesign the platform.

Development / verification gate

For helper-code or skill-contract changes, run:

bash

scripts/check_research_mode.sh

The gate covers:

• compileall;
• ruff;
• pyright;
• auto-discovered selftests;
• pytest-compatible scripts/selftest/.

If pyright or pytest are not installed but uv is available, the script runs them through uvx.

Review handoff rules

When a research task reaches awaiting_review, the lifecycle enters a review-gated state. The following rules are mandatory:

What happens automatically

• begin short-circuits on awaiting_review — cron will not acquire a new lease until the operator resolves the review.
• The task status remains awaiting_review and surfaces display it explicitly.
• The active cron job is disabled while the task waits in awaiting_review, so isolated turns stop consuming tokens.
• The job binding is preserved in history.last_job_binding, allowing clean resumption after approval or changes request.
• request-changes / reopen re-enable the bound job when possible; if the task had already been approved and its old job was removed, reopen recreates a fresh cron job from the saved schedule template.

Required operator transitions

To move a task out of awaiting_review, use exactly one of:

• approve — mark the deliverable as accepted and move to complete.
• request-changes — record feedback and return the task to idle for runner rework.
• reopen (from complete) — return a completed task to idle for further work.
• stop — cancel the task; also removes the job binding.

Command shape:

bash

python3 scripts/research_mode.py approve --id <research-id>
python3 scripts/research_mode.py request-changes --id <research-id> "what to change"
python3 scripts/research_mode.py reopen --id <research-id>

Do not use pause/resume as a substitute for the review transition. resume only restores tasks from paused state, not from awaiting_review.

For plain paused tasks the same execution-layer rule now applies: pausing disables the bound cron job, and resume enables it again.

Forbidden operator actions (hard boundaries)

The following are never acceptable without an explicit manual_override outside research flow audit marker:

1. Manually editing final-report.md, workspace/*, delivery.primary_file, or other task artifacts after awaiting_review.
2. Using side-run sessions or ad-hoc file surgery to mutate deliverables without going through the lifecycle.
3. Telling the user the task is complete when only a runner rework is needed — use request-changes instead.
4. Polling begin in a tight loop while waiting for user input — the task stays in awaiting_review with an inspectable review_gated flag.
5. Using approve when the user requested changes — always use request-changes.

Manual override semantics

Only when the user explicitly requests intervention outside the research flow:

1. Mark the action with an audit_marker: "manual_override" in the state history.
2. Record the reason and what was changed.
3. Return to the normal lifecycle as soon as possible.

Before responding to the user

Verify that:

• Feedback was written to task state via the appropriate transition command.
• The task status reflects the correct transition (idle, complete, cancelled).
• delivery.review_ready, delivery.ready, delivery.primary_file, and review.status are consistent with what the user was told.
• Delivery paths are task-local and point to real files when telling the user a file is ready.

Linked research — universal continuation mechanism

When a completed research task should serve as the basis for a new, related investigation, use create-linked-research. This is the generic mechanism for launching a follow-up research task — not a business-specific preset, but a universal linked-task builder.

When to use it

• After an approved result, to investigate a sub-angle or unresolved question.
• To run a deeper phase of analysis on the same topic.
• To shift focus (e.g., from search to synthesize or compare) while building on prior work.

Command

bash

python3 scripts/research_mode.py create-linked-research \
  --id <source-task-id> \
  --goal "Проверить гипотезу о ..." \
  [--title "Фаза 2 — углублённый анализ"] \
  [--relation phase-2] \
  [--instruction "..."] \
  [--constraint "..."] \
  [--open-question "..."] \
  [--carry-summary] \
  [--carry-open-questions] \
  [--carry-constraints] \
  [--carry-deliverable] \
  [--carry-approved-artifact]

Carry-forward policy

By default, the linked task is clean: it starts fresh and only carries an explicit reference to the source. Use flags to selectively transfer context:

• --carry-summary — copy the source's working summary into the new task's working memory.
• --carry-open-questions — forward open questions from the source.
• --carry-constraints — forward hard constraints.
• --carry-deliverable — inherit the requested deliverable/output shape.
• --carry-approved-artifact — record paths to approved artifacts from the source task.

Constraints on carry-forward

• Carry-forward is opt-in per flag. No data is transferred unless explicitly requested.
• The linked task is a new research task, not a continuation of the source. It gets a fresh status, progress, lock, and corpus.
• The source task remains complete and untouched.

What this is NOT

• This is not a lead/contact enrichment pipeline or a business workflow registry.
• There are no hardcoded task types (contact-enrichment, outreach-prep, etc.) — those were removed in v1.4.1.
• The mechanism is domain-agnostic: any research topic can be continued as a linked task.