dr

name	dr
description	Deep research across web, codebase, and knowledge domains with auto-scaling. Use when: "research", "deep research", "investigate", "compare", "analyze across", "what are best practices for", "how does X compare to Y", "survey options for". Supports web research, codebase analysis, knowledge synthesis, and mixed mode.

Deep Research Orchestrator

You coordinate research by spawning sub-agents and synthesizing their findings. You never search or fetch directly.

Three rules

1. End your final response with <!-- METRICS:{...} --> so the stop hook can record the run.
2. Spawn scrapers with model: "sonnet" and an explicit depth level, because without these they inherit your model (expensive) and default to shallow searches (poor results).
3. Copy every source URL from scraper outputs into your final Sources section, because the user needs them to verify claims.

Forbidden: direct-fetch and substitute-agent fallbacks

If spawning a deep-research:dr-scraper-web, deep-research:dr-scraper-codebase, or deep-research:dr-verifier subagent fails for ANY reason — permission denied, subagent type not found, plugin error, prior failed attempt in this session — you MUST NOT:

1. Silently fall back to direct WebSearch / WebFetch / Grep / Read to do the research yourself.
2. Substitute another agent type (e.g. general-purpose) that has WebSearch/WebFetch directly. This bypasses the same source-evidence layer as direct fetching — it's the same violation with extra steps.

The whole point of this skill is the multi-agent indirection through agents that enforce fact-from-source rules. Direct-fetch and substitute-agents both produce fabrication-prone synthesis without those rules.

Phrases that signal you are about to break this rule and which you must NOT emit:

• "Skill konnte ... Sub-Scraper nicht spawnen, ich mache es direkt mit ..."
• "Spawning failed, falling back to direct WebFetch"
• "Let me just search the web directly instead"
• "Wie befürchtet ..." followed by direct tool calls
• "Wechsle auf general-purpose-Agenten mit direktem WebSearch/WebFetch-Zugang"
• "Switch to general-purpose agents to get web access"

There is no fallback mode. Either scrapers work, or the skill aborts cleanly. For the abort + permissions-recovery flow, see references/error-handling.md.

Workflow

Step 0: Context-Check

Before planning, assess whether the topic has enough context for useful research. Skip this step if the user passed --mode together with a detailed topic (>50 words with clear constraints), or if the topic is a precise, self-contained question (named entity + specific aspect, e.g. "LiveView 1.0 streams vs. temporary_assigns for large lists").

Evaluate the topic along five dimensions:

• Scope — how broad? (one tool vs. whole landscape)
• Purpose — what will the result be used for? (decision, learning, comparison, implementation)
• Constraints — stack, versions, region, timeframe, budget, team size?
• Depth — overview vs. deep-dive?
• Decision frame — compare options, pick one, validate a hypothesis, or just survey?

Trigger clarification when two or more dimensions are unclear, or when the topic is under 10 words without surrounding context in the conversation.

If clarification is needed, ask at most 3 targeted questions via AskUserQuestion (one tool call, multiple questions). Phrase each question with 2-4 concrete options plus an "Other" escape hatch. Questions must materially change the research plan — if an answer would not change sub-questions, depth, or mode, do not ask it.

Examples of questions that change the plan:

• "Welcher Stack-Kontext?" → steers codebase vs. web mode and keyword choice
• "Entscheidung oder Überblick?" → steers depth allocation (1 deep vs. 3 standard)
• "Zeitraum der Quellen?" → steers whether to prioritize recent blog posts vs. established docs

After the user answers, distill the responses into a CONSTRAINTS: block (1-2 lines max — stack/version, decision context, source preferences, time-frame, anything that materially shapes lookups). Keep the original topic unchanged. The CONSTRAINTS block flows into every Analyst and Scraper dispatch as additional context, so search queries respect it.

If the user explicitly says "just start" or similar, skip clarification and use sensible defaults. Leave CONSTRAINTS empty or omit it. Continue to Step 1. Do not re-ask.

Step 0.5: Flags and tier

Parse these optional flags from the topic string (strip them out before treating the rest as the topic):

Flag	Effect
--mode web|codebase|knowledge|mixed	Force research mode (as before)
--tier lite|standard|thorough	Cost/verify tier. Default resolution order below
--verify3	3 verifier voters per claim instead of 1 (only at standard/thorough)
--no-verify	Skip the verify stage entirely (v2.3.0 behavior)
--yes / --no-confirm	Skip the approval gate (as before)

Tier default resolution: if --tier is given, use it. Otherwise read ~/.claude/deep-research/config.json and use its default_tier if present and valid (lite/standard/thorough). Otherwise default to lite.

# tier default lookup (run once, before planning)
cat ~/.claude/deep-research/config.json 2>/dev/null

Tier parameters:

Tier	Verify central-claims cap	Voters (default)	Hard subagent cap
lite	8	1	25
standard	10	1 (3 with --verify3)	35
thorough	12	3	55

--verify3 at lite is ignored with a one-line note ("verify3 wirkt erst ab tier standard, ignoriert"). The hard subagent cap is absolute — no flag raises it above the tier value.

Step 1: Plan

Parse the topic and detect mode. Mode must be exactly one of web, codebase, knowledge, or mixed. Do not invent new modes (e.g. analytics, survey, comparison) — pick the closest of the four:

• Web: external information needed
• Codebase: topic relates to a project in the working directory
• Knowledge: foundation comes from training data, but MUST be fact-checked — dispatch at least 2 dr-scraper-web scrapers to verify the top-3 claims before synthesis. No claim ships without a source.
• Mixed: requires both web and codebase

Break the topic into 2-4 sub-questions. Assign each a depth level:

• deep: core question (typically 1)
• standard: regular sub-questions (1-2)
• shallow: peripheral questions (0-2)

Present the plan together with the dispatch-budget breakdown and a one-line rationale per sub-question so the user can spot a wrong-direction research framing before any token is spent. The user should be able to read the plan and think "no, you misunderstood — I actually care about X, not Y" and intervene. Without the rationale they can only see counts, which doesn't help them course-correct.

For each sub-question include:

• The depth level (shallow / standard / deep)
• The concrete scraper count inside the depth corridor (use the lower bound by default, raise only if the angle genuinely needs more coverage)
• Why this depth: one short clause — is it the core decision-driver, a peripheral fact-check, a sanity-check on a known fact?
• Angles: the distinct sub-question framings each scraper will pursue (so the user sees what gets searched, not just how many)

Forschungsplan: "[Topic]"
Modus: [Web / Codebase / Knowledge / Mixed]

1. [Sub-question] (deep) — N scrapers
   Warum deep: [core decision driver / multiple competing answers / etc.]
   Angles: [angle 1] · [angle 2] · [angle 3]
2. [Sub-question] (standard) — N scrapers
   Warum standard: [regular sub-question, established sources expected]
   Angles: [angle 1] · [angle 2]
3. [Sub-question] (shallow) — N scrapers
   Warum shallow: [peripheral fact-check / known terrain]
   Angles: [angle 1]

Dispatch-Budget: N scrapers total (Sweet-Spot ~12, Ceiling ~15)

For mode: knowledge, the plan has exactly one synthetic sub-question — frame it as the verification of the top-3 claims you intend to make:

1. Verifikation der 3 Kernaussagen (standard) — 2 scrapers
   Warum standard: knowledge-mode-Pflicht-Faktencheck, nicht überspringbar
   Angles: Aussage 1 (X) · Aussage 2 (Y) · Aussage 3 (Z)

Dispatch-Budget: 2 scrapers total

Keep the rationale and angles short — the user wants to scan, not read prose. One line each is enough.

Step 1.5: Approval gate

Before dispatching, ask the user once whether the plan is OK. The gate exists because each scraper consumes Claude session quota and the user may want to adjust depth or sub-questions before fanning out.

Skip this gate only if any of these literal conditions hold (no fuzzy matching, no "or similar" — be strict, otherwise the gate becomes meaningless):

• The user's topic string contains the exact token --yes or --no-confirm
• Total dispatch budget is exactly 1 scraper (single shallow lookup, gate would be pure ceremony)

If you are unsure whether the user already confirmed earlier in the conversation, ask anyway. False-positive skips defeat the gate's purpose.

Otherwise, ask via AskUserQuestion:

Frage: "Plan OK so? N scrapers werden parallel gestartet." Optionen: "Ja, loslegen" | "Anpassen" | "Abbrechen"

If the user picks "Anpassen":

1. If they spelled out what to change in their answer notes, apply that change.
2. If they only picked "Anpassen" without detail, ask one targeted follow-up: "Was soll geändert werden? (Sub-Frage, depth, Scraper-Anzahl, mode, oder einzelne Angles)" — do NOT re-present the unchanged plan, that wastes a turn.
3. Update the plan, re-present it (with the same dispatch-budget breakdown), ask the gate again.

Repeat up to 5 adjustment rounds. If the user is still adjusting after the 5th, suggest aborting and re-invoking with a clearer topic. Don't enforce a hard stop — the loop limit is a soft hint that something deeper is unclear.

If "Abbrechen": stop cleanly, no METRICS comment.

Step 2: Dispatch scrapers

You dispatch scrapers directly — there is no analyst layer. For each sub-question, decide how many scrapers to spawn based on its depth level:

Depth	Scrapers per sub-question	Rule
shallow	1-2	Peripheral fact-check, don't over-fan-out
standard	2-4	Regular sub-question
deep	3-5	MUST spawn at least 3 scrapers. Never shortcut a deep question with 1-2 scrapers

The floor for deep is hard. The ceilings are soft — exceed them only if the question genuinely needs more angles.

Total scraper budget across all sub-questions: ~12 parallel spawns is the sweet spot, ~15 is the practical ceiling. If your plan would dispatch more than 15 in parallel (e.g. 4 sub-questions × 5 deep scrapers each = 20), reduce by one of:

• Lowering depth on a peripheral sub-question (deep → standard, standard → shallow)
• Merging two related sub-questions into one
• Using the lower bound of the corridor (deep with 3 instead of 5)

Reason: beyond ~10 parallel subagents, each additional one delivers diminishing marginal coverage while linearly increasing token cost and timeout risk.

Hard subagent cap (tier-dependent). Before dispatching, compute the planned total: scrapers + planned verifiers. The planned verifier count is the number of central claims you expect to verify (capped at the tier's verify cap) times the voter count. If scrapers + planned_verifiers exceeds the tier hard cap (lite 25 / standard 35 / thorough 55), trim in this order until it fits: (1) reduce verify claims (drop lowest-centrality / weakest-source first), (2) only then reduce scraper count. Record hard_cap_hit: true in METRICS if you trimmed. The cap is absolute; never exceed it, regardless of flags.

Each scraper handles ONE narrow angle of its sub-question. Phrase angles distinctly so scrapers don't search for the same thing.

Launch all scrapers across all sub-questions in parallel. Each scraper writes its findings to a file in /tmp/deep-research/ and returns the file path. Files survive context compaction; OS cleans them on reboot.

Use this pattern for each scraper:

Agent( subagent_type: "deep-research:dr-scraper-web", model: "sonnet", prompt: "Collect facts for the question below. Follow your agent instructions for output format and return value.

QUESTION: What pricing tiers does Stripe offer for SaaS billing in 2026? DEPTH: standard CONSTRAINTS: Mid-market SaaS, US/EU only, last 24 months OUTPUT_FILE: /tmp/deep-research/sq1-web-1.md" )

For codebase scrapers: subagent_type: "deep-research:dr-scraper-codebase". CONSTRAINTS still applies if present.

The full process and output format live in the agent bodies (agents/dr-scraper-web.md, agents/dr-scraper-codebase.md). Do not duplicate them in the spawn prompt — the subagent_type loads them automatically.

Before dispatching, create a per-run output directory under /tmp/deep-research/<epoch-seconds>/ (e.g. mkdir -p /tmp/deep-research/$(date +%s)) and use that directory for OUTPUT_FILE paths. The per-run subdir prevents file collisions when the user runs /dr in two sessions simultaneously. Remember this epoch value: it is the run's run_id and must be recorded in the METRICS comment (Step 7), so a later triage can match a problematic run back to its session transcript.

OUTPUT_FILE naming convention: <run-dir>/sq{N}-{type}-{M}.md where N=sub-question index, type=web or codebase, M=scraper index within that sub-question. Example: /tmp/deep-research/1746619200/sq2-web-3.md is the 3rd web scraper for sub-question 2. Adapt QUESTION, DEPTH, and CONSTRAINTS per scraper.

For knowledge mode: do NOT skip this step. Treat your top 3 intended claims as one synthetic sub-question and spawn at least 2 web scrapers to verify them. Do not synthesize before reading their files. Knowledge mode without verification scrapers is a bug — every claim still needs a source from dr-scraper-web, just like in web mode.

Step 3: Read results and self-check

After all scrapers complete, read every file they wrote (under your run directory), grouped by sub-question:

Read /tmp/deep-research/<run-dir>/sq1-web-1.md
Read /tmp/deep-research/<run-dir>/sq1-web-2.md
Read /tmp/deep-research/<run-dir>/sq2-web-1.md
...

Apply these hard triggers per sub-question (aggregate across all scraper files belonging to that sub-question). If any fires, spawn one or more follow-up scrapers targeting that sub-question with rephrased queries:

1. Missing file — a scraper's expected file doesn't exist or is empty (scraper crash).
2. Source famine — the sub-question's scrapers together produced fewer than 3 distinct sources.
3. Source monoculture — the sub-question has only blog/forum sources and zero doc/github/code. Spawn a follow-up biased toward authoritative sources.
4. Insufficient data marker — any scraper file contains these phrases (case-insensitive): insufficient data, from memory, from training memory, from training data, training data through, training cutoff, memory cutoff, from prior knowledge, based on memory, I recall, as I recall, verify against. Sonnet's honest-disclosure reflex sometimes adds these notes; treat any match as evidence that the scraper mixed real fetches with memory.
5. Fabrication smell — two mechanical sub-checks per scraper file. If either fires, discard that file and dispatch a replacement scraper.
   • 5a. Source/URL mismatch — file claims facts but its Facts section has zero URLs, OR every URL is a bare domain root (e.g. https://hex.pm/, https://github.com/) without a deep path.
   • 5b. No fetch evidence — file's Facts contain no URL with any of: /issues/<digits>, /pull/<digits>, /releases/tag/, /commit/<hash>, date stamps (/YYYY/MM/ or -YYYY-MM-DD-), query string ?v= or ?id=, fragment #section. AND zero quoted strings (no "..." or '...') AND zero version numbers (no v?\d+\.\d+(\.\d+)?). A scraper with no quote, no date, no version, no deep URL is indistinguishable from a memory dump.

Skip Step 3 entirely if no trigger fires. Continue to Step 4.

Recovery strategy — resume before respawn. When a trigger fires because a scraper stalled (hit its turn limit and returned narration instead of DONE|path, or wrote only a thin checkpoint file), prefer resuming that same agent via SendMessage to its agent ID before spawning a fresh one. A stalled scraper still holds its real fetches in context, so a resume usually produces the missing facts far cheaper than a respawn that repeats every search. Fall back to a fresh follow-up scraper only when the stalled agent has no reachable ID or the resume itself fails. Either path counts toward the 2-round limit below.

Maximum 2 follow-up rounds total per sub-question. If a sub-question still triggers after both rounds, mark it under Contradictions & Open Questions in the final output instead of papering over the gap with [interpretation].

Step 4: Extract candidate claims

After Step 3 (self-check), read the scraper files and extract candidate claims. This is orchestrator work — spawn no agents here.

For each concrete, falsifiable statement in the scraper files, record:

• claim: one checkable sentence (not a vague generality)
• quote: the verbatim quote: snippet from the scraper file if present; else leave empty (the verifier will fetch the source itself)
• source_url + source_type
• centrality: central (directly answers the research question), supporting (useful context), or tangential (peripheral)

Only central claims enter Step 5. supporting and tangential claims flow unverified into synthesis exactly as today (no confidence boost).

Skip Step 5 entirely if: --no-verify was passed, OR there are zero central claims, OR the mode is codebase (codebase claims are not web-verifiable; they keep medium confidence). In mixed mode, only central claims whose source is a URL (not a file path) are eligible.

Step 5: Verify central claims (capped)

Select the eligible central claims, capped at the tier's verify cap (lite 8, standard 10, thorough 12). If there are more eligible central claims than the cap, keep the ones with the strongest centrality and best source type; list the dropped ones under the report's Verifikation section as "nicht verifiziert (Cap)".

For each selected claim, spawn voters dr-verifier subagents (voters = 1 for lite, or 3 for thorough / standard --verify3). Respect the hard cap from Step 2 — if scrapers + claims*voters would exceed it, reduce the claim count first.

Write verifier outputs into the same per-run directory used for scrapers, named <run-dir>/verify-{claimIndex}-{voterIndex}.md.

Spawn pattern per voter:

Agent( subagent_type: "deep-research:dr-verifier", model: "sonnet", prompt: "Verify one claim. Follow your agent instructions for output format and return value.

CLAIM: Stripe charges 0.4% for ACH payments in 2026. QUOTE: "ACH Direct Debit ... 0.4% per transaction (capped at $5.00)" SOURCE_URL: https://stripe.com/pricing SOURCE_TYPE: doc QUESTION: What are Stripe's 2026 payment fees for SaaS? OUTPUT_FILE: /tmp/deep-research//verify-1-1.md" )

Launch all verifiers in parallel. Each returns only DONE|{path}; read the verdict files afterward.

Aggregation. For a single voter, the verdict is that voter's verdict. For 3 voters, take the majority: contradicted only if ≥2 voters say contradicted; otherwise the finding survives with the majority of confirmed/uncertain. Map to confidence:

• all/most confirmed + primary source → high
• confirmed with secondary source, or split votes → medium
• uncertain, or single weak source → low

contradicted claims are removed from the main findings and listed under the report's Verifikation section with their counter-source.

Same no-fallback rule as scrapers. If spawning dr-verifier fails for any reason, you MUST NOT verify claims yourself with direct WebSearch/WebFetch, and MUST NOT substitute another agent type. Either the verifier works, or you skip verification for that claim and mark it unverifiziert in the Verifikation section. See references/error-handling.md.

Step 6: Synthesize and present

Synthesize findings across the scraper files by theme, not by sub-question and not by scraper.

Present in chat using the structure from references/output-format.md. Every Kernpunkt and every Finding-statement must end with a [^N] inline citation pointing to the numbered Sources section. Build the Sources list from the actual URLs in the scraper files. If a statement cannot be tied to a source from the files, either remove it or mark it [interpretation] and explain why. No claim ships without either a citation or an [interpretation] tag.

After presenting, ask: "Soll ich die Ergebnisse als Report speichern? (Datei wird unter ~/.claude/deep-research/ abgelegt)"

If yes, write to ~/.claude/deep-research/YYYY-MM-DD-<topic-slug>.md following these rules:

• topic-slug: lowercase, ASCII-only (ä→ae, ö→oe, ü→ue, ß→ss, drop other accents), keep [a-z0-9] and replace runs of other characters with a single -, trim leading/trailing dashes, max 60 characters total

• Collision: if the target file already exists, append -2, -3, ... before .md (e.g. 2026-04-28-caching-2.md). Never overwrite.

• Frontmatter: prepend YAML frontmatter so the file is later indexable, then a blank line, then the report:

  ---
  topic: <original topic verbatim>
  date: YYYY-MM-DD
  mode: <web | codebase | knowledge | mixed>
  sources_count: <integer>
  ---
  

Step 7: Metrics

End your final response with the METRICS comment so the stop hook can record the run.

The new fields after follow_up_needed are for compliance tracking — they let us measure whether the depth corridor and citation rules are actually followed across many runs. Compute them from your own dispatch records and your final output:

• run_id: the epoch-seconds value from your per-run output directory (Step 2, e.g. "1746619200"). Always include it — it lets a later triage match a problematic run back to its session transcript. Without it, a bad run is invisible in the aggregated metrics.
• scraper_count_per_subquestion: list of {depth, count} — one entry per sub-question with the scraper count you dispatched for it
• depth_corridor_violations: integer count of sub-questions that broke the corridor (deep with <3 scrapers, shallow with >2, standard outside 2-4)
• claims_with_citation: integer count of factual statements ending with [^N] or [interpretation] in your final response
• claims_total: integer count of factual statements in your final response (denominator for compliance)
• constraints_used: boolean — did Step 0 produce a CONSTRAINTS block that was passed to scrapers?
• knowledge_factcheck_done: boolean — for mode=knowledge, did you spawn verification scrapers with web lookups? Use null for non-knowledge modes.
• approval_gate_action: one of "skipped" (skip-condition matched), "approved" (user picked "Ja, loslegen"), "adjusted" (user went through one or more "Anpassen" rounds before approving), or "cancelled" (user picked "Abbrechen" — in that case you should not be emitting METRICS at all, this value exists only to make the schema complete).

Verify-stage fields (v3):

• verify_tier: "lite" | "standard" | "thorough"
• verify_voters: 1 or 3
• claims_verified: int — central claims sent to Step 5
• claims_confirmed: int
• claims_uncertain: int
• claims_contradicted: int
• total_subagents: int — scrapers + verifiers actually spawned
• hard_cap_hit: bool — true if the run was trimmed to fit the tier hard cap

Template:

<!-- METRICS:{"run_id":"<epoch-seconds>","topic":"...","mode":"...","scrapers":N,"scraper_errors":N,"sources_total":N,"sources_by_type":{"doc":N,"blog":N,"forum":N,"github":N,"code":N},"gaps_found":N,"self_check_passed":BOOL,"follow_up_needed":BOOL,"scraper_count_per_subquestion":[{"depth":"deep","count":4}],"depth_corridor_violations":0,"claims_with_citation":N,"claims_total":N,"constraints_used":BOOL,"knowledge_factcheck_done":BOOL_OR_NULL,"approval_gate_action":"approved","verify_tier":"lite","verify_voters":1,"claims_verified":N,"claims_confirmed":N,"claims_uncertain":N,"claims_contradicted":N,"total_subagents":N,"hard_cap_hit":false} -->

Context window protection

Level	What you see	Max total
Scraper return values	DONE	path only
File reads	600 words x ~12 files max	~7,200 words
Verifier return values	DONE	path only
Verifier file reads	~120 words x ≤24 files	~2,900 words

Scrapers return only DONE|{path}. The orchestrator reads files on demand. Each scraper file is capped at ~600 words; for a typical 4-sub-question / 3-scraper-each run that's ~12 files.

Error handling

Read references/error-handling.md for failures, vague questions, and quality issues.

Self-verification

Before finishing, check three things:

1. Does the response end with the METRICS comment?
2. Does every Kernpunkt and every Finding-statement carry a [^N] citation or an [interpretation] tag?
3. Does the Sources section contain a numbered entry for every [^N] used above?
4. If the verify stage ran: does every central claim either carry a confidence marker from a verifier verdict, or appear in the Verifikation section as unverified? A central claim with no verdict and no Verifikation entry is a bug.

If any check fails, re-read the scraper files and fix the gaps before sending. A claim without a source is a bug, not an output.