Adversarial verification of research theses — cross-run/cross-thesis contradiction via the entity index, pre-mortem (Klarman/Kahneman: assume the loss already happened, reverse the path), inversion (Munger: invert, always invert), and wrong_if falsifiability review. ≈50-finding cap sorted by severity; content-derived finding IDs; incremental scoping with three backstops; lifecycle hooks at pillar-complete / thesis-complete / pre-reduction.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
The buyer-side IC verb: challenge the thesis. Not audit — that name was taken
three times (audit-xls, quality-audit.yaml, G3) — challenge is the thing that
happens in an investment committee.
reduce_journals.build_entity_index):
same (entity, metric, period) with different values. Same retrieved_at →
true contradiction; different → suspected restatement. The validator never
auto-resolves — candidates go to the IC agenda.wrong_if falsifiability — is each falsifier mechanically checkable
(metric + threshold + source)? A prose falsifier is not a falsifier (Q8-4).hash(entity+metric+period+gap_type)) —
unchanged re-runs yield byte-identical IDs (eval corpus comparability).as_of changed since last run)
with three backstops: every Nth converge / constitution MINOR-MAJOR / subscription
change (new subscriptions create new comparison pairs — Q9).# Resolve the kit root first — contracts/kit-root.md. The kit's scripts do NOT ship
# with the skills, so never assume the CWD is the checkout.
KIT=""
for c in "${AGENTII_KIT_ROOT:-}" \
"$(cat "$HOME/.claude/skills/agentii/.kit-root" 2>/dev/null)" \
"${CLAUDE_PLUGIN_ROOT:-}"; do
[ -n "$c" ] && [ -f "$c/scripts/agentii_cmd.py" ] && { KIT="$c"; break; }
done
if [ -z "$KIT" ]; then d="$PWD"; while [ "$d" != "/" ]; do
[ -f "$d/scripts/agentii_cmd.py" ] && { KIT="$d"; break; }; d="$(dirname "$d")"; done; fi
[ -n "$KIT" ] || { echo "agentii kit not found — see contracts/kit-root.md" >&2; exit 1; }
python3 "$KIT/scripts/challenge.py" --nth-converge 10
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3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements
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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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Audit spreadsheet, formula error detection, hardcoded cell finder, cross-sheet reference audit, workbook auditor, Excel model audit, financial model QA, spreadsheet review, cell dependency trace, formula integrity check
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Business model classification, business model analysis, structural analysis of how a company makes money, product offering decomposition, distribution channel analysis, customer segment analysis, revenue model identification, market sizing TAM SAM SOM, competitive positioning, business unit performance, management team & leadership analysis, what does the company sell, how does the company go to market, business model type platform service product, channel mix direct vs indirect, revenue concentration risk, CEO CFO executive backgrounds and changes
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Chart pattern recognition, price action patterns, candlestick signal bars, pullback bar counting H1/H2/H3/H4, trend channels, micro channels, trading ranges, breakouts, major trend reversals 5-step sequence, wedges, double tops and bottoms, triangles, head and shoulders, climaxes, measured moves, support and resistance
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The research-domain clarification skill — find underspecified items in a thesis spec.md (prose wrong_if, universe rows without rationale, missing budget/expiry/pins, ambiguous pillars), ask the human structured questions WITH options (max 5 per round), encode the answers back into spec.md's Clarifications section, and re-evaluate checklists/thesis-quality.md after every write (Q32 bidirectional maintenance).
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