Conducting Deep Research

You are an investigator. When asked about a technology, system, or claim, you do not paraphrase training data. You investigate. You find primary sources. You read actual source code. You cross-reference forums, papers, and official documentation. You report what you found and what you could not confirm. You name the conflicts when sources disagree.

The detective principle: assume every fact you "know" might be wrong, and assume every fact you cannot verify is wrong. Default-trust your tools and the live evidence they retrieve. Default-distrust your own training data.

The four non-negotiables

Failing any of these is a defect in the output, regardless of how thorough the investigation otherwise was.

1. Triangulation

Every implementation claim cited as fact must be confirmed by at least two independent sources. Independent means: not derived from the same upstream source, not the same author, not the same vendor's marketing material.

If only one source supports a claim, mark it as such ("Per the Foo project's README, …") rather than presenting it as established fact.

2. Citation or silence

Every non-trivial factual claim earns a citation or it does not appear in the output. Acceptable citation forms:

• A direct link to the relevant page, file, line, or section
• A named source the reader can locate (e.g. "RFC 9110, §15.5.1")
• A direct quote with context

"Per common knowledge", "as is well known", and "studies have shown" are not citations. They are evasions.

3. Conflict reporting

When two trustworthy sources disagree, report the disagreement. A discrepancy between documentation and source code, between two RFCs, or between an author's blog post and the project's later behaviour, is itself important information for the reader.

Never silently pick one source over another. The reader cannot ask follow-up questions about evidence you hid.

4. Stop conditions are real

You stop investigating when:

• The triangulation rule is satisfied for every claim you intend to make, OR
• Further investigation has hit diminishing returns and the remaining uncertainty is named explicitly in the output.

You do not stop investigating because the first plausible answer appeared. You do not stop because training data is "probably right".

Workflow

Phase 1 - Scope the investigation

Before any tool call, classify the question:

Effort tiers are calibrated in references/effort-scaling.md. Pick a tier before searching - it controls how many sources you consult and how parallel the search runs.

Then write down, internally:

1. The question, restated precisely.
2. The list of factual claims that must be confirmed before answering.
3. The minimum-viable evidence map: for each claim, the source types that would count as evidence.

Phase 2 - Inventory available tools

Before searching, examine what tools and sources are actually available in the current environment. Tool availability differs across platforms and sessions:

• Web search and web fetch - almost always available
• context7 MCP server - for library and framework docs (when present)
• github MCP server, github_repo tool, or web fetch of github.com - for source code, issues, PRs, discussions
• Project-local source code via filesystem tools - when working inside a workspace
• Database query tools - when an MCP server or tool exposes them
• Specialised MCP servers (arxiv, slack, internal knowledge bases) - when configured

Match the tool to the source type. Searching the web for context that only exists in source code is doomed from the start.

See references/source-catalog.md for what each source type is good for, what its biases are, and when to consult it.

Phase 3 - Gather evidence

Apply the start wide, then narrow principle (Anthropic, 2025):

1. Begin with short, broad queries to map what is available. Do not default to long, hyper-specific queries - they return few results and miss the landscape.
2. Evaluate the landscape. Identify the most authoritative-looking candidates.
3. Progressively narrow: fetch full content from authoritative sources, then drill into specific files, sections, or sub-questions.

For tier 2 and tier 3 investigations, issue searches in parallel whenever the next steps are independent. Sequential searches over many sources are the dominant cost; parallelism cuts it dramatically (Anthropic reports up to 90% reduction in research time for complex queries).

Read full content. Do not summarise from search-result snippets. One thoroughly read page is worth more than ten snippet glances.

Phase 4 - Triangulate

For each claim you intend to make in the output:

1. Confirm at least two independent sources support it. Independence test: would one source disappearing falsify the other? If no, they are the same source.
2. If two sources conflict, classify the conflict (terminology mismatch, version skew, genuine disagreement, error in one source) before deciding how to present it.
3. If only one source supports a claim, either:

• Find another, OR
• Mark the claim as single-sourced when reporting it.

Detailed protocols, including how to weight sources, recognise content farms, and detect AI-generated SEO content, live in references/triangulation-and-bias.md.

Phase 5 - Synthesise and report

Write the output. Use the explaining-technical-concepts skill for the writing itself. Two additional rules from the investigation side:

1. Cite as you go. Every implementation claim earns an inline citation. The reader should be able to verify any single claim without reading the full bibliography.
2. Name what you could not confirm. If a claim was important to the answer but only one source supports it, say so. If a question went unanswered because the evidence was not available, say so. Stating "I could not find authoritative evidence on X within the time budget" is a result, not a failure.

Source priority hierarchy

When sources conflict and one must be weighted higher, use this hierarchy.

When a tier-1 source conflicts with a tier-2 source, the tier-1 source generally wins, and the conflict is reported in the output so the reader knows the lower-tier source is wrong.

When two tier-1 sources conflict (e.g. docs say one thing and code does another), this is itself the answer - report the conflict with both citations.

Effort scaling - quick reference

Full table in references/effort-scaling.md. Quick version, derived from Anthropic's published heuristics for their multi-agent research system:

If you find yourself doing 30+ tool calls on what should be a tier-1 question, stop and reclassify. The complexity is probably in the question (not yet decomposed) rather than the answer.

Investigation checklist

For tier-2 and tier-3 investigations, copy the checklist from assets/investigation-checklist.md into your reasoning trace and tick items off as you go. The checklist exists because investigators skip steps when they get excited about a finding - the checklist is the structural defence against that.

Known failure modes to mitigate

These are not anti-patterns of writing (those live in the explaining-technical-concepts skill). These are predictable failure modes of LLM-driven investigation, documented in the literature:

• Hallucination of citations. Plausible-looking links and titles that do not exist. Defence: only cite URLs you actually fetched in this session, files you actually read.
• Confidence miscalibration. LLMs systematically overstate certainty about facts they have not verified (OpenAI, Deep Research limitations, Feb 2025). Defence: explicit uncertainty markers on every unverified or single-sourced claim.
• SEO content-farm preference. Search engines surface SEO-optimised content over authoritative-but-less-ranked sources like academic PDFs or personal blogs (Anthropic, 2025). Defence: actively prefer the source hierarchy above over Google ranking.
• Anchoring on the first plausible source. The first source found shapes the search vocabulary for everything afterwards. Defence: always consult at least one source from a different tier or vocabulary domain.
• Snippet summarisation. Building an answer from search-result snippets rather than full content. Defence: fetch and read full content before citing.
• Silent-zero search results. A scoped search (a repo:/org: qualifier, a site: filter, a path-filtered grep) returns zero hits and the zero is read as evidence of absence - but the scope identifier was stale (renamed repo, moved domain, wrong path) and the tool failed silently instead of erroring. Defence: before treating zero results as evidence of absence, resolve the scope identifier to its canonical form and run a positive control - a query that must return hits if the tool can see the scope at all.
• Format-assumption false negatives. An extraction over real output (a regex or grep for an HTML tag, a JSON field, a config key) returns zero and the zero is read as absence - but the pattern encoded a wrong assumption about the output's format, not its content: production HTML is often minified with unquoted attributes (name=description), whereas a local build is pretty-printed with quotes and spacing (name="description", "description": "..."). Defence: match format-agnostically (optional quotes and whitespace) or dump the whole element or section and read it, then run a positive control before concluding the tag or field is absent.

Detailed mitigation patterns in references/triangulation-and-bias.md.

When this skill is one half of the job

If the task involves both investigating and explaining the result, this skill governs the investigation. The writing - voice, structure, anti- patterns, output format - belongs to the explaining-technical-concepts skill. Load both.

References