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WenyuChiou

research-hub

by WenyuChiou
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Python
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discover_continue

Apply fit-check scores from an AI judge to filter high-confidence candidates and produce a papers_input.json file for ingest into the vault. Use after running discover_new.

Instructions

Apply fit-check scores from an AI judge and produce a papers_input.json ready for ingest.

Second half of the interactive discovery flow. The user runs discover_new first (which emits a search-results stash + a scoring prompt), pastes the prompt into an AI of choice, then feeds the AI's scored output back through this tool. The scored candidates are filtered by threshold and written to papers_input.json in the cluster's discover-stash directory, ready for the standard ingest pipeline (research-hub auto or research-hub clusters ingest).

When to use:

  • You have a JSON list of fit-check scores from an AI judge and want to admit only the high-confidence candidates into the vault.

  • You're running the two-phase discovery flow because the topic boundaries are fuzzy and you want a human / AI in the loop on which papers belong.

When NOT to use:

  • You haven't run discover_new yet — there's no stash to apply scores against. Run discover_new first.

  • You already have a fully-resolved list of DOIs to ingest; skip discovery and call add_paper per item, or auto_research_topic for the one-shot path.

  • You want to re-score an EXISTING ingested cluster's papers; use fit_check_emit + fit_check_apply (the post-ingest re-scoring path).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_slugYesSlug of the cluster whose discover stash will be consumed. Must match the slug passed to ``discover_new``.
scoredYesEither a flat list of score dicts (each with at least ``slug`` + ``score``), or a wrapping dict like ``{"scores": [...]}`` — both shapes accepted. Score values are 0-5 integers; entries missing a score are treated as score 0.
thresholdNoMinimum score (inclusive) for admission. Defaults to ``None`` — when ``auto_threshold=False`` this falls back to the cluster's configured default (typically 4).
auto_thresholdNoWhen ``True``, ignore ``threshold`` and pick a cutoff automatically from the score distribution (a bimodal gap heuristic). Default ``False`` (use explicit ``threshold``).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description covers the key behavioral aspects: input processing (scored list), threshold filtering, auto-threshold heuristic, and output writing. It does not explicitly mention whether it overwrites existing files or the stash, which would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a summary, detailed process, and usage guidelines. It uses formatting for code and clear sections. While slightly verbose, it remains focused and front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity and presence of an output schema, the description adequately covers prerequisites, input handling, filtering logic, and integration with the discovery flow. It addresses edge cases like missing scores and auto-threshold, making it complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, providing a baseline of 3. The description adds value by explaining default threshold behavior (falls back to cluster default), that missing scores are treated as 0, and that 'scored' can be a list or dict. This goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: 'Apply fit-check scores from an AI judge and produce a papers_input.json ready for ingest.' It specifies the tool is the second half of the discovery flow, distinguishing it from siblings like discover_new or discover_clean.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'When to use' and 'When NOT to use' sections provide clear guidance, including prerequisites (run discover_new first) and alternatives (add_paper, fit_check_apply). This helps the agent select the correct tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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