cite-holmes

cite-holmes (Cite Holmes): deep research with citation verification

One line: a question goes in — a verified report comes out.

Three differences from a plain "search and summarize":

1. Calibrate before working — ask sharp questions first; the most expensive waste is researching the wrong question.
2. Conclusions carry evidence grades — 🟢 two independent sources agree / 🟡 single authority / 🔴 contested.
3. Every citation is checked — mechanical layer (reachability, domain authority, field completeness, dedup) plus semantic layer (does the source actually support the claim?). Unverified references never masquerade as real.

⛔ Iron rules (zero exceptions)

1. Never fabricate: citations must come from pages actually fetched this session. Re-search rather than write URLs from memory.
2. Never pretend: unchecked references are marked unverified; fetch failures are unreachable (≠ nonexistent — flagged for human review).
3. Don't hide conflicts: when sources disagree, present the disagreement, mark 🔴, show each side's evidence.
4. Budgeted search: QUICK ≤6 searches, FULL ≤15. Out of budget → state the gaps honestly instead of forcing conclusions.
5. Calibrate before searching (FULL mode): scope / timeframe / audience / output format must be locked first.

Step 0: mode selection

A question answerable by one verifiable fact → QUICK. Needs synthesis or trade-offs → FULL. "Quick check" forces QUICK; "thorough/comprehensive" forces FULL. When unsure, default FULL.

Five-phase workflow

1. CALIBRATE (FULL only)

Ask 3–5 high-leverage questions at once (no drip-feeding): scope, timeframe, audience/depth, output format, decision context. Never re-ask what the user already provided. If the user declines ("your call"), proceed with stated defaults.

2. PLAN

Show a short plan: 3–7 sub-questions, source priority (primary/official > major media > community/blog as leads only), budget.

3. SEARCH (iterative, not one pass)

Read references/search-strategies.md first (diamond expansion, source pyramid, query matrix, gap-driven iteration). Essentials: each round targets one sub-question; evolve queries with discovered terms; search both English and Chinese for topics that span both internets; fetch full text of the 2–5 most valuable sources (never conclude from search snippets); verify key numbers/dates in the original page before quoting.

4. VERIFY (the heart of this skill)

Register every reference in research_refs.json (schema in references/report-template.md), then verify on two layers:

Semantic (the model must do this): for each reference ask "does the source page actually support the sentence I cite it for?" → supports / partial_support / not_in_source (drop or demote).

Mechanical (run the script):

python scripts/verify_refs.py --refs research_refs.json --out verify_report.md

Five verdicts: verified / partial / unreachable (needs_human_check) / invalid / unverified. See the script's --help for options (--offline, --strict for CI).

5. SYNTHESIZE

Follow the skeleton in references/report-template.md: executive summary first; every key conclusion carries a confidence grade + citation ids; the reference table carries verdicts; unverified/unreachable items live only in the "human review" section; finish with gaps, disagreements, and follow-up questions.

Files

Honest limits

• A fabricated citation pointing to a real, live, plausible page passes the mechanical layer; the semantic layer may catch it — model judgment, not a guarantee.
• unreachable ≠ fake.
• Reproducible demo: python scripts/verify_refs.py --refs examples/demo_refs.json (8 refs, 3 planted fabrications, all caught — measured 7.7 s).

Environment fallback

Without web tools: state honestly that only the "user-supplied material + mechanical verification" mode is possible; never pretend to search.