deep-research
The deep-research engine's deterministic script suite (four ops): caps computes and enforces fetch/cost budget ceilings; academic searches arXiv/Crossref/OpenAlex/PubMed keylessly; compress de-duplicates and narrows candidate passages by lexical overlap; citations verifies a model's drafted claims-with-citations one by one — does the quote actually appear in the CITED source, is the DOI well-formed and matched, was the source really fetched — abstaining when there are no sources and emitting a de-duplicated, stably-numbered reference list. Calls no model, so the same input always yields the same output. For: 'run a deep-research search/compress pass', 'check this report's citations for fabrication'; Triggers: deep research, academic search, citation check, anti-fabrication, references, DOI verification
What this skill does
deep-research
The deterministic half of the research engine. A Python skill cannot reach the model or the web tools, so the AGENT gathers sources (web_search / web_fetch) and drafts the claims-with-citations; this skill does the deterministic steps the model must not be trusted to perform on its own output. It bounds, retrieves, compresses, and verifies facts — it does not write the report. Four scripts:
caps— before/during the loop: bound the sub-questions and per-source fetch
budget, refuse to recurse past a depth limit, and account step cost so the run stops at a hard ceiling instead of the GPT-R-style exponential blow-up.
academic— the one retriever that fits a stdlib skill: query public scholarly
APIs (arxiv / OpenAlex / Crossref / Semantic Scholar) over an https host allow-list; each source fails independently, results are normalized to the same record shape the other scripts consume.
compress— before synthesis: chunk the fetched sources, drop duplicate/noise
passages, and keep the most query-relevant text within a char budget, so a long page does not blow the context window.
citations— after a draft: verify every quote/DOI/source and build the
numbered reference list, catching fabrication before the report is shown.
For the broader research method, use references/research-workflow.md, references/source-quality.md, references/evidence-standards.md, references/scholarly-evidence.md, references/report-structure.md, and references/citation-style.md. The references define the research workflow and reporting standard; the scripts provide deterministic checks inside that workflow.
When to use
caps: right after decomposing (opplan) to trim/allocate the plan, and
periodically during gathering (op account) to check whether a hard ceiling (fetches / model-calls / cost) has been hit.
academic: for scholarly / peer-reviewed questions, to pull papers with DOIs,
abstracts, and authors from the academic APIs (complements the agent's own web_search for general web sources).
compress: after fetching sources for a sub-question and before feeding them to
the model, when the fetched text is large and needs de-noising to fit context.
citations: after the agent has drafted claims-with-citations, to catch
invented quotes, invented DOIs, and citations to sources that were never fetched, and to build the reference list.
When NOT to use
- Deciding what to research or writing prose — that is the agent's job (model).
- Fetching sources — the agent calls
web_search/web_fetchand passes the
fetched text in; this skill never touches the network.
- Semantic/embedding relevance ranking — that needs the vector store and is a
separate core-agent tool, not this stdlib skill. compress uses lexical (keyword-overlap) relevance only.
Preconditions
- Python 3.9+ (stdlib only — no third-party packages).
- The agent must pass each source's actually-fetched
text; a quote can only be
verified, and a passage only ranked, against text the skill can see.
How to call
academic
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research academic -- --op search --query "<q>" [--sources arxiv,openalex,crossref,semanticscholar] [--limit 5] [--timeout 30]data has results (normalized {id, source, title, text, authors, date, doi, url}, de-duplicated across sources by DOI/title), sources_queried, and errors (per-source; a rate-limited or slow source is reported here, not fatal — check it and proceed with what returned). Feed results straight into compress (as sources) or cite them in citations. Talks only to a fixed https host allow-list and honors HTTP(S)_PROXY.
caps
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research caps -- --op plan --input <payload.json>
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research caps -- --op account --input <payload.json>planinput{ "subquestions": [".."], "depth"?: 0, "caps"?: {..overrides..} }→
data with the de-duplicated/trimmed subquestions, fetch_budget_per_subquestion, allowed (false + reason: "max_depth_exceeded" past the depth limit), and dropped (duplicates + over_cap, never silent). Overrides are clamped to absolute ceilings so a cap can be lowered but never raised past the safe max.
accountinput{ "steps": [ {"step","fetches","model_calls","cost_usd"} ], "caps"? }
→ data with by_step + totals aggregation, remaining, exceeded, and stop: true once any hard ceiling is crossed. max_cost_usd is enforced only when the agent sets it.
compress
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research compress -- --input <payload.json> [--max-chars 12000]Input { "query": "the sub-question", "sources": [ { "id", "url", "title", "text" } ], "max_chars"?, "max_per_source"?, "min_score"? }. Output data.kept is the ranked list of {source, url, title, chunk, chunk_index, score} within budget, plus data.stats (chars_in/chars_out/deduped/chunks_kept/skipped_compression). When the total fetched text already fits the budget, compression is skipped and whole sources pass through de-duplicated (score: null).
citations
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research citations -- --op verify --input <payload.json>--op verify(default): full per-citation verification + supported/unsupported
per claim + reference list + flags.
--op references: just the de-duplicated numbered reference list.--input <path>: payload JSON (default stdin).--out <path>: also write the
envelope to a file.
Input payload:
{ "sources": [
{ "id": "s1", "url": "https://…", "title": "…", "date": "2024-05-01",
"doi": "10.1234/abcd.5678", "text": "the full fetched text …" } ],
"claims": [
{ "text": "The claim sentence.",
"citations": [ { "source": "s1", "quote": "exact…Files in this skill
- _meta.json
- references/citation-style.md
- references/evidence-standards.md
- references/report-structure.md
- references/research-workflow.md
- references/scholarly-evidence.md
- references/source-quality.md
- scripts/academic.py
- scripts/caps.py
- scripts/citations.py
- scripts/compress.py
- SKILL.md
- test/test_academic.py
- test/test_caps.py
- test/test_citations.py
- test/test_compress.py
- test/test_pipeline.py
How to use in Orkas
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深度研究引擎的确定性脚本组（四个）：caps 计算并强制抓取/成本预算上限；academic 免密钥检索 arXiv/Crossref/OpenAlex/PubMed；compress 按词面重叠去重压缩候选段落；citations 对模型起草的"论断+引用"逐条核验——引用原句是否真在被引来源里、DOI 是否规范对得上、来源是否真的抓取过，无来源时 abstain，并产出去重稳定编号的引用列表。不调用模型，同样输入永远同样结果。适合"跑一轮深度研究检索/压缩""核验这份研究报告的引用有没有编造"；触发词：深度研究、学术检索、引用核验、防幻觉、anti-fabrication、参考文献、DOI 核验
该技能的功能
deep-research
The deterministic half of the research engine. A Python skill cannot reach the model or the web tools, so the AGENT gathers sources (web_search / web_fetch) and drafts the claims-with-citations; this skill does the deterministic steps the model must not be trusted to perform on its own output. It bounds, retrieves, compresses, and verifies facts — it does not write the report. Four scripts:
caps— before/during the loop: bound the sub-questions and per-source fetch
budget, refuse to recurse past a depth limit, and account step cost so the run stops at a hard ceiling instead of the GPT-R-style exponential blow-up.
academic— the one retriever that fits a stdlib skill: query public scholarly
APIs (arxiv / OpenAlex / Crossref / Semantic Scholar) over an https host allow-list; each source fails independently, results are normalized to the same record shape the other scripts consume.
compress— before synthesis: chunk the fetched sources, drop duplicate/noise
passages, and keep the most query-relevant text within a char budget, so a long page does not blow the context window.
citations— after a draft: verify every quote/DOI/source and build the
numbered reference list, catching fabrication before the report is shown.
For the broader research method, use references/research-workflow.md, references/source-quality.md, references/evidence-standards.md, references/scholarly-evidence.md, references/report-structure.md, and references/citation-style.md. The references define the research workflow and reporting standard; the scripts provide deterministic checks inside that workflow.
When to use
caps: right after decomposing (opplan) to trim/allocate the plan, and
periodically during gathering (op account) to check whether a hard ceiling (fetches / model-calls / cost) has been hit.
academic: for scholarly / peer-reviewed questions, to pull papers with DOIs,
abstracts, and authors from the academic APIs (complements the agent's own web_search for general web sources).
compress: after fetching sources for a sub-question and before feeding them to
the model, when the fetched text is large and needs de-noising to fit context.
citations: after the agent has drafted claims-with-citations, to catch
invented quotes, invented DOIs, and citations to sources that were never fetched, and to build the reference list.
When NOT to use
- Deciding what to research or writing prose — that is the agent's job (model).
- Fetching sources — the agent calls
web_search/web_fetchand passes the
fetched text in; this skill never touches the network.
- Semantic/embedding relevance ranking — that needs the vector store and is a
separate core-agent tool, not this stdlib skill. compress uses lexical (keyword-overlap) relevance only.
Preconditions
- Python 3.9+ (stdlib only — no third-party packages).
- The agent must pass each source's actually-fetched
text; a quote can only be
verified, and a passage only ranked, against text the skill can see.
How to call
academic
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research academic -- --op search --query "<q>" [--sources arxiv,openalex,crossref,semanticscholar] [--limit 5] [--timeout 30]data has results (normalized {id, source, title, text, authors, date, doi, url}, de-duplicated across sources by DOI/title), sources_queried, and errors (per-source; a rate-limited or slow source is reported here, not fatal — check it and proceed with what returned). Feed results straight into compress (as sources) or cite them in citations. Talks only to a fixed https host allow-list and honors HTTP(S)_PROXY.
caps
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research caps -- --op plan --input <payload.json>
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research caps -- --op account --input <payload.json>planinput{ "subquestions": [".."], "depth"?: 0, "caps"?: {..overrides..} }→
data with the de-duplicated/trimmed subquestions, fetch_budget_per_subquestion, allowed (false + reason: "max_depth_exceeded" past the depth limit), and dropped (duplicates + over_cap, never silent). Overrides are clamped to absolute ceilings so a cap can be lowered but never raised past the safe max.
accountinput{ "steps": [ {"step","fetches","model_calls","cost_usd"} ], "caps"? }
→ data with by_step + totals aggregation, remaining, exceeded, and stop: true once any hard ceiling is crossed. max_cost_usd is enforced only when the agent sets it.
compress
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research compress -- --input <payload.json> [--max-chars 12000]Input { "query": "the sub-question", "sources": [ { "id", "url", "title", "text" } ], "max_chars"?, "max_per_source"?, "min_score"? }. Output data.kept is the ranked list of {source, url, title, chunk, chunk_index, score} within budget, plus data.stats (chars_in/chars_out/deduped/chunks_kept/skipped_compression). When the total fetched text already fits the budget, compression is skipped and whole sources pass through de-duplicated (score: null).
citations
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" deep-research citations -- --op verify --input <payload.json>--op verify(default): full per-citation verification + supported/unsupported
per claim + reference list + flags.
--op references: just the de-duplicated numbered reference list.--input <path>: payload JSON (default stdin).--out <path>: also write the
envelope to a file.
Input payload:
{ "sources": [
{ "id": "s1", "url": "https://…", "title": "…", "date": "2024-05-01",
"doi": "10.1234/abcd.5678", "text": "the full fetched text …" } ],
"claims": [
{ "text": "The claim sentence.",
"citations": [ { "source": "s1", "quote": "exact…技能包含的文件
- _meta.json
- references/citation-style.md
- references/evidence-standards.md
- references/report-structure.md
- references/research-workflow.md
- references/scholarly-evidence.md
- references/source-quality.md
- scripts/academic.py
- scripts/caps.py
- scripts/citations.py
- scripts/compress.py
- SKILL.md
- test/test_academic.py
- test/test_caps.py
- test/test_citations.py
- test/test_compress.py
- test/test_pipeline.py
如何在 Orkas 中使用
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