generate-theories

name	generate-theories
description	This skill should be used when the user asks to "generate theories", "theorize about", "what theories explain", "form scientific theories", "literature-driven theories", "hypothesize", "form hypotheses", "generate hypotheses", "what hypotheses explain", "run the theorizer", or wants AI-generated, literature-grounded scientific theories or hypotheses about a research question.
allowed-tools	Bash(asta auth login) Bash(asta auth status) Bash(asta generate-theories *) Bash(asta artifacts *) Bash(asta documents *) Bash(open *)

Generate Theories

Uses the Theorizer agent (via asta-gateway) to find papers, extract evidence, write theories, and score novelty. Auth: asta auth login.

The CLI is generated from the agent card — every subcommand, flag, and default. Discover the surface yourself:

• asta generate-theories --help — list subcommands.
• asta generate-theories card — full agent card.
• asta generate-theories describe <skill-id> — input schema for one skill.

This skill is in two halves. The first describes what Theorizer is — its three composable stages, the bundled end-to-end version, and the standalone subcommands. The second describes how to handle a generate-theories request end-to-end — choosing a mode, shaping the question, dispatching, checkpoints, handoff, and error handling.

What this skill is good for

• Generating literature-grounded scientific theories about research questions ("how does X affect Y, via what mechanism, in what domain").
• Finding evidence in published papers to support or contradict a hypothesis.
• Scoring theory novelty against existing literature.
• Surveying what's known and unknown in a research area through extracted variables and findings.

The pipeline

Theorizer's literature-driven theory generation is three composable stages:

literature-theory-generation = find-and-extract → form-theory → evaluate-novelty

• find-and-extract — searches papers for the research question and pulls theory-relevant findings (entities, variables, results) from each into a structured table via an extraction schema. Outputs: paper list + extraction findings. No theories yet.
• form-theory — writes a small set of theories from extraction results. Each theory is a few short laws (e.g. "X causes Y") with supporting and conflicting paper evidence attached. Accepts either a prior run's task_id or user-supplied extraction results.
• evaluate-novelty — scores each theory's statements against the retrieved literature, marking each claim as already established, derivable from known work, or genuinely new. Accepts either a prior run's task_id or user-supplied theories.

literature-theory-generation is the bundled end-to-end version that runs all three back-to-back without pause. Convenient when the user wants results in one shot; less useful when the user wants to inspect or steer between stages.

Two more standalone subcommands round out the surface:

• build-extraction-schema — normalizes the research question, drafts a paper search query, and generates the extraction schema that find-and-extract uses. Useful when the user wants to inspect or curate the schema before running the pipeline; pass the produced schema via the extraction_schema parameter on find-and-extract or literature-theory-generation.
• resume-extraction — add more papers to a prior run and extract findings from them. Useful after failed paper downloads, or to grow the evidence base of a prior run without starting over.

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How to run it

The sections below describe the choreography for handling a generate-theories request — what to ask, what to surface, and when.

Two modes: automatic vs piecemeal

Because the pipeline is composable, the user has a choice: let it run straight through, or step through stage by stage.

• Automatic — single literature-theory-generation call. Submit, wait, render results. Fastest path; user can't steer mid-run.
• Piecemeal — call the three stages separately. After each stage, show the user what came out and ask whether to continue, stop, or edit. Slower but steerable; user can prune papers, refine the query, or stop after they have what they need.

On the user's first generate-theories request in a session, after shaping the question but before submitting, ask which mode they want via AskUserQuestion with the two options as choices. Briefly explain the trade-off in each option's description.

After they answer, remember the choice for the rest of the session. If they later signal they want the other mode ("actually, just run it" / "wait, let me see the papers first"), respect that without re-confirming.

Exception: if the user's original phrasing already commits ("just run it," "use defaults," "I'll review papers before extraction"), skip the question and honor the implied choice.

Between stages

When running piecemeal, after each stage completes, surface a clear summary in chat oriented to the decision the user is about to make, then offer three options:

Option	What happens
Continue	Run the next stage on this run's output
Stop	End here. Render whatever results exist via the Asta Artifacts handoff
Edit	Modify before continuing (see below)

The summary should be tight and decision-oriented — not a dump of everything.

After find-and-extract:

• Show the paper list (titles + 1-line summary of what was extracted from each).
• Edit means: the user may want to add or remove specific papers, edit the extraction schema used to gather evidence, or restart with a refined query.

After form-theory:

• Show the theories: name + 2-3 sentence core idea + supporting/contradicting paper counts.
• Edit means: re-run form-theory with a different generation_objective (accuracy-focused vs novelty-focused) or against a subset of the extractions.

After evaluate-novelty:

• This is the terminal stage — there is no "continue."
• Show per-theory novelty labels in plain English.
• Edit means: re-run novelty against a refined theory subset, or branch into find-literature to challenge specific claims.

If the user picks Stop at any stage, hand off to Asta Artifacts as usual to export and index whatever was produced. Partial extractions and theories are valid artifacts.

Shape the question

This applies only when the next call generates or forms theories — specifically literature-theory-generation (full pipeline) or form-theory (standalone or continuation). Do NOT shape the query when the next call triggers find-and-extract, evaluate-novelty, or resume-extraction.

Theorizer works with both broad and specific questions. A lot of its value comes from taking a broad or exploratory question and searching broadly through the literature — surfacing variables, mechanisms, and scopes the user didn't think to name. A tightly-specified question is useful too, when the user already knows what they want, but it isn't a prerequisite. Don't treat under-specification as a problem to be fixed before the run can happen.

A Theorizer theory question might name some or all of the following four dimensions, and giving them helps the agent aim more precisely when the user already has opinions about them:

1. Intervention — the independent variable being varied (e.g., source citations, response latency, anthropomorphic phrasing).
2. Outcomes — concrete dependent variables (e.g., trust, retention, task completion).
3. Mediating mechanism (optional) — the hypothesized driver of the effect (e.g., perceived transparency, cognitive load, anchoring). Theorizer can run without one; include it only when the user supplies it.
4. Scope — the domain the theory should apply to (e.g., consumer chatbots, enterprise search, coding agents).

Three examples of fully-specified questions (the second includes an optional mechanism) — these are illustrations of one style Theorizer accepts, not a template every question must match:

• Build a theory of how spaced retrieval practice affects long-term retention in undergraduate STEM courses.
• Build a theory of how intermittent fasting affects insulin sensitivity, via autophagy upregulation, in adults with metabolic syndrome.
• Build a theory of how prescribed burns affect grassland biodiversity in temperate North American prairies.

A broad question like "what makes people trust AI?" is a perfectly valid input on its own. The offer to narrow it is a courtesy, not a gate.

Within a session, don't re-shape on subsequent runs unless the user changes their question substantively. Repeating the dance is friction.

How to handle the user's question — audit, then route:

Don't guess the dimensions (Intervention, Outcome, Scope) on the user's behalf and silently narrow the question — that commits the whole run to an angle the user never chose. If you want the question narrowed or changed in any way, ask; don't infer.

Audit first: Determine whether the user's question already names Intervention, Outcome, and Scope, or is broad/exploratory (one or more of those left open).

Then route:

1. Proceed silently if the dimensions are already present, or if the user has indicated (now or earlier) that they want to proceed as given. Don't announce the audit result, don't tell the user the question is "well-formed" — move directly to the next dispatch step. Add the "Build a theory of how..." framing yourself if the question already has the substance for it; otherwise submit their question as given.
2. Offer, don't gate if the question is broad or a required dimension is left open and you haven't offered yet. In a single message, note that the question reads as broad/exploratory, that Theorizer can work well with that as-is, and ask whether they'd like to keep it broad or add detail on a specific dimension. Make clear that running it as-is is the default if they don't want to spend time on this — never imply the run is blocked until they answer. You could also let the user know that they might get utility starting broad to explore, then subsequently narrowing their focus as they discover theories they'd like to further explore or refine.
3. Refuse only if the question isn't a question answerable with the scientific literature (e.g., "what should I name my startup," "summarize this paper"). State plainly that Theorizer's domain is generating scientific theories grounded in published evidence, and offer to reframe the question into that form or hand off to another skill. This is the only route that withholds a run.

Route 2 (Offer to narrow):

**Your question:** What makes people trust AI?

That reads as a broad, exploratory question — Theorizer can work well with that; it'll dig through the literature and surface the variables and mechanisms that seem to matter. If you'd rather narrow it yourself first, I can factor in specifics like:

- **Intervention** — a variable you want to vary (e.g., source citations, response latency, anthropomorphic phrasing)
- **Scope** — a domain to focus on (e.g., consumer chatbots, medical AI, search assistants)

Want to keep it broad, or add some detail?  You can also start broad, then narrow later based on what Theorizer finds.

If they add detail, reformulate and confirm:

**Reshaped:**
Build a theory of how source citations affect user trust in consumer AI chatbots.

_Approve, edit, or keep the original broad question?_

If they say to keep it broad, or don't engage with the offer, proceed with their original question verbatim.

Route 3 (Refuse):

**Your question:** What should I name my startup?

Theorizer generates scientific theories grounded in the published literature — questions like "how does X affect Y" that can be tested against evidence. Naming a startup isn't a testable hypothesis I can ground in papers.

Additional context and steerability

Users may include additional steering context alongside their core research question. For example: "Please build theories about X. I'm particularly interested in Y, or Z. For additional context, I'm interested in A, B, and C."

This additional information helps steer the theory generation process — pass it all through verbatim in the query you submit, rather than truncating to just the core question. The only exception is when the question is not already framed as theory building: in that case, you may reframe the query to start with "Please build theories about...", but keep edits light and do not alter the substance or additional context provided.

How to dispatch

1. Classify the user's request:

   • A clear request for theories from a research question → mode selection and then either literature-theory-generation (automatic) or the three-stage pipeline (piecemeal).
   • A request that targets one stage explicitly (e.g., "just find papers," "re-theorize from this prior run," "check novelty on these theories") → use the single matching subcommand and skip mode selection.
   • A continuation of a prior run → resume-extraction, or form-theory / evaluate-novelty with task_id of the prior run.

2. Capture the research question. If the user hasn't supplied one, ask for it. This is the only universally required input.

3. Shape the question — but only when the next call generates theories (literature-theory-generation or form-theory). Skip for:

   • find-and-extract standalone — pass the literature topic through.
   • evaluate-novelty, resume-extraction — no query input needed.

   See Shape the question for the rubric and the do-not-shape list.

4. Resolve mode (first run only). Skip if the user committed to a mode in their phrasing.

5. Resolve other inputs. Run asta generate-theories describe <skill-id> to see required and key optional fields. For any missing required fields and the 2–3 most impactful optionals (papers, objective, novelty), ask one AskUserQuestion question unless the user said "use defaults."

6. Surface what's being submitted. One line: "Using N papers, novelty evaluation on, accuracy-focused. Submitting — say 'tune' to change anything." This is the user-side half of the defaults-visibility pattern.

7. Submit.

   • Automatic mode: one asta generate-theories literature-theory-generation call.
   • Piecemeal mode: asta generate-theories find-and-extract first, then surface results and offer continue/stop/edit, then form-theory and evaluate-novelty in sequence with the same checkpoint pattern.

   Each subcommand takes a single JSON PAYLOAD argument. Inline JSON works for short payloads; for long queries, write to a file and use . Object-typed inputs also accept a URI (, , ).

Defaults

Schema defaults. The runtime "Using: ..." line in dispatch step 6 surfaces whatever was actually resolved so the user can intervene before submission.

Setting	Default	Effect	Tune when
max_papers_to_retrieve	10	~3–8 min runtime	Bump to 30 for medium runs, 100 for full coverage (15–25 min)
generation_objective	accuracy-focused	Favors well-supported theories	Set novelty-focused if user emphasizes novelty or new theories
do_qualified_novelty_evaluation	true	Adds 30–60 min and ~$5–10	Skip for fast iteration. No effect in piecemeal mode (the evaluate-novelty stage runs separately)

After the task completes

Hand off to the Asta Artifacts skill (asta-tools:artifacts) to export and index the result — it owns the path convention, manifest, and asta-documents registration. Pass generate-theories as the invoking skill and a slug shaped YYYY-MM-DD-<short-query-slug> derived from the theory query.

In piecemeal mode, export after each stage the user stops or completes at — don't wait for the full pipeline to finish.

Then write, in chat, in this order:

1. Where it lives — the indexed path + the asta documents search command + open <absolute path>. Use absolute paths.
2. One-paragraph synthesis — 2–4 fresh sentences. What do the theories collectively argue? Where do they converge / diverge? What's the headline "so what"? Read the theories; don't template.
3. Table of theories — one row per theory: name + 2–3 sentence core idea. Add a novelty column if novelty was scored.

No "let me know if you'd like…" tail — the search/open links cover it.

Resumption / errors

• state=input-required → CLI prints a Continue with: … hint. Relay to the user; resend with asta generate-theories send-message --task-id <id> --context-id <ctx> '<reply>' and re-run the wait.
• state=failed → report status.message and stop.
• Network/protocol errors exit non-zero with a JSON {error: …} on stderr.
• Partial pipeline (piecemeal) → the user can always continue later by passing the prior run's task_id to form-theory or evaluate-novelty.

References

• Theorizer: https://github.com/allenai/asta-theorizer-internal
• Agent card / schemas: asta generate-theories card / asta generate-theories describe <skill-id>
• A2A spec: https://a2a-protocol.org/latest/specification/