Geek skills deep research

Deep Research V8.1

This skill is for evidence-rich research outputs, not for every question that happens to mention “analysis”.

The V8 shift is simple:

• Single-agent first. Start with one lead agent and only fan out when parallel work will clearly help.
• Thin harness, fat skill. Put reusable judgment and workflow here; keep deterministic checks in scripts.
• Context organization over prompt stuffing. Load the minimum active context bundle, then pull in references only when needed.
• Eval and observability built in. A good report is not enough; the run must also be diagnosable and improvable.

What this skill should produce

Choose the lightest artifact that satisfies the task.

Output type	Use when	Typical length	Required artifacts
Brief memo	user wants a concise answer with evidence	800-1800 words	research-plan.md, registry.md, draft.md, run-summary.json
Full report	user asks for comprehensive analysis / literature review / decision document	2500-6000 words	all core artifacts + evaluation.md
Delta update	user says “continue”, “second round”, “what changed”, “deepen round 2”	600-1800 words	prior round handoff (references/handoff-format.md) + new notes + delta draft

If the user did not ask for a long report, default to Brief memo.

When NOT to use this skill

Do not activate for:

• quick fact lookups or simple definitions
• summarizing a single provided article/PDF/page
• short comparisons the model can answer directly from 1-2 sources
• brainstorming without evidence requirements
• tasks where the user explicitly wants a short answer, not a report

If in doubt, ask yourself: Does this task need a reusable evidence artifact and multi-source synthesis? If not, do something simpler.

Org-policy boundary

This skill does not replace system policies, enterprise guardrails, or repo-level instructions. Put these outside the skill:

• data handling / PII / compliance rules
• approval requirements for external access or irreversible actions
• org-wide style and review policy
• environment-specific permissions

Keep those in system prompts, AGENTS/CLAUDE/OpenAI config, or the harness. This skill owns the workflow, not the company’s permanent red lines.

Active context bundle

At activation time, keep the active bundle small.

Always load first

1. This SKILL.md
2. references/methodology.md
3. references/report-assembly.md
4. references/research-notes-format.md

Load on demand

• references/subagent-prompt.md only if you actually dispatch subagents
• references/handoff-format.md only when a delta update continues a prior round
• references/evaluator-prompt.md only if you run the evaluator
• references/quality-gates.md before finalization
• references/observability.md when emitting metrics or diagnosing regressions
• references/tension-discovery.md only for contested / decision-heavy topics
• references/landscape-scan.md only when literature or ecosystem mapping matters

After compaction or context reset Reload only:

• research-plan.md
• active task notes
• registry.md
• unresolved issues list
• the one reference file for the current phase

Do not reload the whole skill tree unless the run drifted badly.

Workflow

P0 — Scope, route, and choose the lightest mode

Create workspace/research-plan.md with:

• research question
• intended audience
• freshness requirement
• geography / market / jurisdiction
• output type (brief / full / delta)
• stakes: low / medium / high
• why this skill is justified

Then choose the orchestration mode:

Mode	Default choice
Single-agent	default for most tasks
Lead + subagents	only when there are 3+ separable research threads or obvious parallel value
Delta update	when continuing prior research

Do not fan out just because subagents exist.

P0.5 — Optional modules (not mandatory by default)

Use optional modules only when they earn their keep:

• Tension discovery (references/tension-discovery.md): use for contested, hype-heavy, or decision topics where mainstream framing may be wrong.
• Landscape scan (references/landscape-scan.md): use when the domain is unfamiliar, broad, or literature-heavy. For non-academic topics, this can be an ecosystem/standards/vendor scan rather than arXiv.
• Reverse search: use when costs, failure modes, counter-evidence, or operational constraints are missing.

P1 — Plan the evidence work

Break the task into 1-5 research threads. Each thread needs:

• one crisp objective
• starting queries
• what “done” looks like
• what evidence would change the conclusion

If using subagents, each subagent gets one focused thread. Avoid overlapping ownership.

P2 — Investigate, extract, and write notes

Follow references/research-notes-format.md.

Rules:

• search broadly first, then chase named entities, standards, datasets, products, trials, laws, or papers
• fetch and read the best supporting sources for the highest-value claims
• write notes that separate facts, analysis, gaps, and unresolved conflicts
• capture support snippets/paraphrases for the top claims so later verification is easier

The lead agent should work from notes by default, but may inspect raw/fetched sources again when:

• two sources materially conflict
• a claim is high-stakes or decision-critical
• a note looks suspiciously weak or over-compressed

P3 — Build registry and verify evidence

Create workspace/registry.md from approved sources only.

Use scripts/source_evaluator.py as a helper, not an oracle. Authority scores are heuristics. Final acceptance depends on claim fit, evidence type, and whether the source can actually bear the weight of the claim.

Use scripts/verify_citations.py before finalization.

Evidence rules:

• core claims should lean on the strongest available evidence for that claim type
• anecdotes illustrate; they do not anchor the conclusion
• conflicting evidence must be surfaced, not silently averaged away
• if the topic is high-stakes, spot-check raw support for top claims before shipping

P4 — Synthesize the output

Follow references/report-assembly.md. For full reports, assets/report_template.md is an optional skeleton; adapt it rather than inventing a new structure.

Always include:

• clear answer to the user’s question
• explicit limitations / trade-offs
• separation of source-backed findings vs your own synthesis
• uncertainty calibrated to evidence quality

Only include a dedicated Decision Framework when the user is choosing between options. Only require a contrarian section when the topic actually has a mainstream narrative worth challenging. Otherwise produce a non-obvious insight instead of forcing fake contrarianism.

P5 — Evaluate and gate

For full reports and medium/high-stakes briefs, run the evaluator using references/evaluator-prompt.md.

Before finalization, check references/quality-gates.md:

• routing correctness
• process completeness
• grounding / citation integrity
• output quality
• efficiency and operational health

P6 — Finalize, summarize, and learn

Emit:

• final draft.md
• evaluation.md if run
• run-summary.json via scripts/emit_run_summary.py

In the run summary, record what actually helped: single-agent, subagents, tension discovery, landscape scan, reverse search, evaluator, or manual spot-checks. This is what makes the skill improve over time.

“Finalize” means deliver research artifacts to the user. Do not publish to an external site, send messages, request credentials, or make irreversible changes unless a separate explicit user request and the host policy authorize it.

Deterministic helpers

Use scripts for the parts that should be boring and repeatable:

• scripts/source_evaluator.py — baseline source scoring / diversity checks
• scripts/verify_citations.py — fail-closed citation integrity and source-pool checks
• scripts/emit_run_summary.py — structured observability output for the run

For reproducible recency scoring, pass an explicit evidence cutoff: scripts/source_evaluator.py sources.json --as-of YYYY-MM-DD. Record the same cutoff in the research plan and run summary.

If a deterministic check fails, fix the artifact first. Do not argue with the script unless you have a concrete reason.

Evaluation and observability

This skill is only “good” if it performs well on:

1. Routing — does it trigger when it should, and stay out of the way when it should not?
2. Process — did it create the right artifacts and evidence trail?
3. Outcome — is the final brief/report genuinely useful and grounded?
4. Efficiency — did it get there with acceptable tool/time/token cost?
5. Safety / governance — did it respect policy boundaries and handle uncertainty honestly?

See:

• evals/routing-evals.json
• evals/runbook.md — how to run the routing and mode checks
• references/quality-gates.md
• references/observability.md

Degraded mode

If subagents, shell, or a writable workspace are unavailable, keep the workflow but shrink the surface area:

• one lead agent only
• inline notes instead of files if needed
• fewer searches, but still enough to support the conclusion
• lightweight evaluator or self-check if full evaluation is impossible
• still keep limitations, uncertainty, and citation integrity

Stop conditions

Stop and ask for help only when the blocker is real and specific, for example:

• no credible sources exist for a critical claim
• the user’s requested scope conflicts with available evidence
• policy or access restrictions block the required research

Otherwise, continue with the best justified artifact and say where the confidence drops.