Benchmark Due Diligence

The four-phase orchestration

Use the Workflow tool (preferred — deterministic fan-out, see the ready-to-fill template in references/workflow_orchestration_template.md) or Task agents. Scale agent count to how thorough the user wants (a few dimensions for a quick read, 6+ with multi-vote verification for a deep audit).

Phase 1 + 2 — collect → verify, per dimension, as a pipeline (each dimension verifies the moment its collection finishes; no global barrier):

• Collection agent — objective stance. Every finding carries a source URL and a source_kind (对象自述/营销 vs 第三方独立信源 vs 混合). Anything not found goes in gaps — never filled by guessing.
• Verification agent — adversarial, default-skeptical stance. Grade every claim L1–L4 and rule 坐实 / 大体可信 / 存疑 / 证伪-水分. The job is to actively hunt falsifying evidence, especially for the headline claims (the trophy stat, "#1 ranking", funding amount, user counts). bubble_summary names the biggest water in that dimension.

Grading rubric, source_kind, verdicts, and both JSON schemas → references/evidence_grading_rubric.md.

Typical dimensions (tailor to the benchmark type — person / company / product):

1. Subject background + headline-claim attribution (the #1 bubble target)
2. Corporate base — entity, founding, funding/valuation
3. Core product/business real metrics — user counts, revenue, rankings, awards, cross-verified against third parties
4. Playbook teardown — platform matrix, persona, content types, how they borrow other people's audiences, how personal IP funnels to the product
5. Comparison sample — a structurally-similar peer or parallel path
6. Sector + how this class of playbook usually wins and usually fails

Phase 3 — synthesis: due-diligence conclusion (single agent, consumes all verdicts):

1. Real relationship map (correcting the common misreadings from Phase 0)
2. Bubble-busting table — claim | evidence level | verdict | one-line basis, sorted by most-water-first
3. Playbook teardown — concrete, copyable actions
4. Attribution breakdown (the core) — what share of the success is product vs market-timing vs personal-IP-marketing vs operations? Give % ranges with reasons, and explicitly split replicable method from luck / timing / non-transferable endowment.

Phase 4 — synthesis: what this means for the commissioner (single agent; consumes Phase 3 + COMMISSIONER_CONTEXT):

1. Resource-mapping table — benchmark's playbook elements × the commissioner's real resources; tag each cell ✅ borrow-able / ⚠️ not-replicable (luck/timing) / 🔄 already-doing / 🚫 bubble-don't-copy, one line each
2. Landing points — exactly how the commissioner uses it (their to-B service / their own IP / their tooling)
3. Action list + open questions (what's still unconfirmed)

Attribution weighting and the four-tag mapping framework → references/attribution_and_resource_mapping.md.

Don't rebuild what already exists

This skill's edge is the adversarial bubble-busting + attribution + commissioner-mapping layers. The plumbing underneath is not novel — reuse it:

• Fan-out collection / source governance — borrow the lead-agent + subagent pattern from deep-research. (What's unique here is the skeptical verification stance and the L1–L4 bubble grading, not the parallelism.)
• Person-subject identity / footprint checks — invoke osint-investigate (ACH hypothesis matrix, Bellingcat-style pivots) rather than re-deriving identity attribution.
• Mainland-China corporate registration / funding — invoke the qcc family of skills for 工商 data.
• Social-platform playbook data — the agent-reach CLI covers B站/小红书/抖音/YouTube/X.

Read before you run

• references/evidence_discipline_traps.md — the recurring traps (inferring relationships from appearances, headline-claim attribution, client-vs-asset, foundation-before-fan-out, grade-don't-binary, privacy leak) with real teardown war-stories. Read this first; it's where runs actually break.
• references/evidence_grading_rubric.md — L1–L4, source_kind, verdicts, collection/verification schemas.
• references/attribution_and_resource_mapping.md — attribution weighting + four-tag mapping + landing-point framework.
• references/workflow_orchestration_template.md — a ready-to-fill Workflow script with the FACTS / COMMISSIONER_CONTEXT injection split already wired in.

Next Step

After the due-diligence conclusion is ready, suggest the natural follow-on (opt-in, never auto-run):

Due-diligence teardown is done.

Options:
A) Render it as a shareable PDF report — pdf-creator (Recommended if this goes to a partner/team)
B) One dimension needs deeper neutral background — deep-research on that sub-topic
C) No thanks — the markdown teardown is enough