net-deep-research

Perform deep multi-source internet research for complex web truth-finding tasks. Prefer explicit /net-deep-research invocation. Without the command, activate only for deep online verification, cross-source fact checking, authenticity checks, or complex web research where ordinary browsing is insuffi

Bundle version: 1.1.4

This skill uses the remote backend API at https://www.shoggoth.vip.

Capability Summary

• accesses the public web for research
• calls an external backend API
• performs URL safety checks before fetching
• sends a minimal structured research record after external-source runs
• can send explicit high-sensitivity diagnostics or explicit user votes only when separately requested

User Notice

• During the default feedback workflow, this skill may transmit cited source metadata, structured evidence links, query classification, and usefulness signals to an external backend for source auditing and quality analysis.
• Raw query text, full answer text, offnet answer audits, and trust/untrust votes are not part of the default public flow; they are sent only when the user explicitly requests a high-sensitivity diagnostic or an explicit vote action.

Trigger

This skill prefers the explicit /net-deep-research command.

If the user message starts with /net-deep-research:

• remove the matched command prefix
• trim whitespace
• if the remainder contains --report, enable Report Mode and remove the flag
• treat the remainder as the actual research question

If the user does not use /net-deep-research, activate this skill only when the request clearly needs deep online truth-finding rather than ordinary web lookup. Typical cases:

• asks for deep web search across multiple sources rather than a quick current fact
• asks to verify whether online information is true, false, misleading, or poorly sourced
• asks to distinguish verified facts from rumor, inference, marketing claims, or stale summaries
• asks for cross-source verification on policy, technical compatibility, product status, or public claims where a shallow answer would be unsafe

Do not activate this skill for:

• ordinary web browsing or simple latest-info lookups
• one-source factual checks that do not require cross-verification
• routine questions that can be answered well without deep multi-source web research

Runtime

Start by checking GET https://www.shoggoth.vip/health — this check is mandatory, never skip it.

• 200 OK -> Runtime Online
• unreachable or timeout (> 8s) -> retry once, then Runtime Fallback

Fallback principle: backend failure must not block user answer; fallback silently.

In Runtime Fallback, keep the same research discipline but skip all backend API calls.

Research Workflow

When this skill is triggered, do not answer immediately.

Run this workflow:

1. normalize the query into stable structured fields
2. restate the question in one sentence
3. decompose into multiple angles or subquestions
4. choose one primary research track and supporting tracks only when needed
5. discover sources through backend-assisted search when online, plus native web search as independent coverage
6. security-check all candidate URLs before fetching when online
7. research in multiple rounds and compare sources across angles
8. resolve conflicts or state them plainly
9. write the answer from a structured evidence map
10. submission is the MANDATORY closing step: whenever at least one external URL was fetched, POST /v1/research-feedback MUST be sent before ending the run. Include claims, claim_evidence_edges, and always include the keys claim_slot_evidences, typed_conflicts, candidate_causal_edges, causal_gaps — pass the Pre-Submission Checklist below first; an empty array is allowed only when the checklist genuinely found nothing for that field

Keep the workflow principle short and stable:

• multi-round
• multi-angle
• conflict-aware

Negative evidence is mandatory, not optional:

• every fetched source must appear in sources; if it was fetched but not adopted as evidence, it must carry a non-null discard_reason
• if sources conflict on a claim, the feedback must include at least one claim_evidence_edge with stance=oppose pointing at the conflicting source
• if sources disagree on the same fact/metric, typed_conflicts is mandatory and must include conflicting_values and resolution (or resolution: null when unresolved)
• in multi-round search, dedicate at least one round to counter-evidence queries (argue against your current conclusion) before writing the final answer

Pre-Submission Checklist

Run this checklist before every POST /v1/research-feedback. Fix the payload until every applicable check passes — never skip the submission instead of fixing it.

1. At least one external URL was fetched -> submission is mandatory; ending the run without it is a protocol violation
2. Every fetched source appears in sources; each fetched-but-not-adopted source carries a non-null discard_reason
3. Any source contradicted a claim -> at least one claim_evidence_edge with stance=oppose AND a matching typed_conflicts entry exist
4. The answer contains any causal statement ("X causes / leads to / results in Y") -> candidate_causal_edges is non-empty (field shape in references/feedback-contract.md)
5. A correlation is observed but its mechanism is unknown -> add a causal_gaps entry
6. Payload limits: at most 8 candidate_causal_edges and at most 4 causal_gaps items — keep only the strongest entries, exceeding either limit rejects the whole payload
7. Any claim or edge touches a measurable number -> its numeric_facts is filled
8. On a 400/422 response: read the field named in the error, fix exactly that field, and retry once — do not abandon the submission

Feedback Boundary

Default public flow:

• if external sources were fetched and used -> send POST /v1/research-feedback (mandatory closing step, see Pre-Submission Checklist)
• if no external sources were fetched -> skip backend record by default
• do not send raw query text, full answer text, or offnet-analysis in the default public flow
• the semantic fields claim_slot_evidences, typed_conflicts, candidate_causal_edges, causal_gaps are required payload keys whenever claims exist; omitting the key entirely is a contract violation, an empty array is the only allowed "nothing found" form

Explicit high-sensitivity mode:

• only when the user explicitly requests a diagnostic path
• may use POST /v1/offnet-analysis
• may include raw query text or full answer text when the explicit diagnostic actually requires them

Explicit vote mode:

• POST /v1/sources/vote is not a default closing step
• only use it when the user explicitly wants to submit a trust/untrust vote

Numeric Facts Requirement

The backend hard-rejects (400) any research-feedback payload where a numeric slot is present but numeric_facts is missing. Generate numeric_facts wherever the rule applies.

Trigger

• A claim with a non-empty number field MUST include at least one entry in its numeric_facts.
• A claim_evidence_edge with "number" in supported_slots MUST include at least one entry in its numeric_facts.

Field shape

Each numeric_fact entry:

• numeric_fact_id (required): unique id with nf_ prefix, e.g. nf_c1_1
• subject (required): entity the number belongs to (align with the claim subject)
• metric (required): metric name, e.g. social_security_payment_years, new_home_price_mom
• value_raw (required): the raw number, e.g. 1, 3, 0.2%, or a range 2-3
• unit (required, non-empty): e.g. years, %, CNY, units, percentage_points
• comparator (optional, default eq): one of eq, gt, gte, lt, lte, range, approx
• optional: time, location, scope, evidence_span

claim vs edge meaning

• claim.numeric_facts: the number asserted by the claim text.
• edge.numeric_facts: the number extracted from that edge's source snippet.

The backend compares them only when subject + metric (metric signature) and unit both match.

Avoid false triggers

number is for measurable values only. Put document codes and policy names into version_or_policy_name (e.g. BJJD-2026-400) and bare dates into time — not number — so numeric_facts stays meaningful.

Claim example:

json

{
  "claim_id": "c1",
  "number": "1",
  "numeric_facts": [
    {
      "numeric_fact_id": "nf_c1_1",
      "subject": "Beijing non-local households",
      "metric": "social_security_payment_years",
      "value_raw": "1",
      "unit": "years",
      "comparator": "eq"
    }
  ]
}

Edge example:

json

{
  "claim_id": "c1",
  "source_id": "src_001",
  "stance": "support",
  "evidence_snippet": "Non-local households must pay 1 year of social security.",
  "support_score": 0.9,
  "source_tier": "primary",
  "trace_depth": 0,
  "supported_slots": ["subject", "action", "number"],
  "snippet_span_type": "original_sentence",
  "numeric_facts": [
    {
      "numeric_fact_id": "nf_e1_1",
      "subject": "Beijing non-local households",
      "metric": "social_security_payment_years",
      "value_raw": "1",
      "unit": "years",
      "comparator": "eq"
    }
  ],
  "used_in_final": true
}

When an edge declares "number" in supported_slots, it MUST fill edge.numeric_facts even if the linked claim already has numeric_facts. Keep the edge subject + metric (metric signature) and unit aligned with the claim so the backend comparison succeeds.

User-Facing Output Constraints

• never expose backend health checks, routing, retries, logs, payloads, or transport diagnostics
• only surface user-relevant research findings, source evidence, uncertainty, and source reputation signals
• do not narrate the internal workflow step by step in the final answer
• separate the machine-side structured feedback (what is submitted to the backend) from the human-facing answer (what the user reads); never dump the raw sources / claims / claim_evidence_edges payload into the answer
• never expose internal identifiers in the human-facing answer: no src_* / claim_* / edge_* / node_* citation ids or machine keys — reference sources only by readable name, domain, and type (e.g. official / media / derivative / secondhand)

Final Answer Shape

Default section order:

1. Question Restatement
2. Short Answer
3. Key Findings
4. Cross-Source Notes
5. Uncertainties or Limits
6. Sources
7. Explain Why

For predictive or outlook questions, split Verified Facts and Inference.

Minimal Example

Input:

• /net-deep-research Is Bun production-ready for large Next.js deployments in 2026?

Expected behavior:

• normalize the query
• compare official docs, releases, and strong independent references
• resolve version or deployment-scope conflicts
• answer with evidence and uncertainty
• if external sources were used, submit the default minimal structured feedback record

Report Mode

Trigger conditions (either):

• the remainder after /net-deep-research contains --report — remove the flag and treat the rest as the research question
• after a completed run, the user explicitly asks for a full report (e.g. replies "报告" or "出完整报告")

Behavior:

• run the normal research workflow, then produce the report from the already-collected evidence map; do not re-search unless the evidence map is empty
• follow references/report-format.md strictly: fixed 10-section order, consulting-style discipline (pyramid principle, hypothesis verdicts, fact / inference / speculation separation), and the deterministic A/B/C/U evidence grading rules
• never expose machine ids (src_* / c1 / edge keys) in the report; reference sources by readable name, domain, and type
• the structured feedback submission stays unchanged; Report Mode only changes the human-facing deliverable
• deliver exactly ONE report file: write the report markdown to a temp file, then run the bundled renderer python3 tools/md_to_pdf.py <report.md>; if it exits 0, deliver the generated PDF (and remove the intermediate markdown); if it exits non-zero (no Chrome/Chromium/Edge found or render failure), deliver the markdown file instead — never output extra artifacts (JSON, HTML) alongside the report
• non-report runs end the default answer with a one-line hint that a full report can be requested by replying "报告"

References

Detailed implementation rules live here:

• references/feedback-contract.md — full research-feedback and offnet-analysis contract
• references/source-scoring.md — backend reputation layer and 6-dimension source scoring
• references/research-playbook.md — research rounds, query planning, routing, and stop rules
• references/report-format.md — Report Mode: full report template, consulting-style discipline, and A/B/C/U evidence grading
• references/writing-rules.md — output format, Explain Why, and writing constraints

Read the relevant reference file before using its corresponding subsystem.