Benchmark Due Diligence
Take a benchmark the user envies — a founder, KOL, company, or product whose success looks suspiciously shiny — and produce a teardown that ends in "what this means for ME", not a neutral report. The deliverable answers three questions a balanced briefing never does: How much of this success is real vs marketing bubble? How much is replicable method vs luck/timing? And what, specifically, can the commissioner do with it?
This is the adversarial, decision-oriented cousin of
. Where deep-research builds a trustworthy picture of the world, this skill
assumes the picture is inflated until proven otherwise and converts the survivors into the commissioner's own moves.
CRITICAL: run inline, never
This skill is an
orchestrator — it spawns parallel collection + verification agents (via the
tool, or
agents) and may invoke other skills (
,
,
). Subagents cannot spawn subagents or call skills. Setting
would silently break the entire fan-out.
Do not add a field. (Same constraint osint-investigate documents — it's a hard runtime rule, not a preference.)
The one rule that protects the commissioner: two injection channels
Everything the agents see flows through exactly two channels. Keeping them separate is the single most important discipline in this skill:
| Channel | Content | Injected into |
|---|
| FACTS | Already-verified public facts about the benchmark (relationships, who-owns-what, the headline claim flagged ) | Every agent — collection, verification, synthesis |
| COMMISSIONER_CONTEXT | The commissioner's private reality — real resources, client names, strategic intent, what they can actually leverage | Only the final mapping agent (Phase 4) |
Why this split is non-negotiable: collection and verification agents take their input and run external
on it. If the commissioner's client names or strategy leak into those prompts, they get searched on the open web — a privacy breach. The mapping phase genuinely needs "who is the commissioner"; the collection phase must never see it. Encode this in the orchestration (see
references/workflow_orchestration_template.md
), don't rely on remembering it mid-run.
Phase 0 — nail the foundation by evidence, not appearance (do this BEFORE any agent)
The fastest way to waste a 12-agent fan-out is to build it on a foundation you inferred from appearances. Two failure modes recur and both have burned real runs:
- Inferring relationships between entities from names/domains. "Their content lives at , and they're the founder, so they must own that community" — when in reality they were just an invited guest. A shared domain, a similar name, or co-occurrence is an observation, not ownership. Verify with an authoritative source before treating any A↔B relationship as fact.
- Treating the commissioner's client as the commissioner's asset. If the commissioner does service work for an accelerator/brand, that accelerator is the client's asset — the commissioner can't leverage its audience or capital. Mapping the benchmark's playbook onto resources the commissioner doesn't actually control produces castles in the air.
So before fanning out, establish by evidence (not vibes):
- The benchmark's real entity graph — who owns whom, who merely partners/guests. Don't reason from names.
- The headline-claim attribution — the benchmark's whole narrative usually rests on one trophy stat ("took product X from 0 → 1M users"). Are they the founder, or the departed growth lead? This is the #1 to-verify target; write it into FACTS with a .
- What the commissioner truly controls — separate owned assets from client/partner assets.
Write the results into
(public half) and
(private half). A shaky foundation makes every downstream agent confidently wrong.
The four-phase orchestration
Use the
tool (preferred — deterministic fan-out, see the ready-to-fill template in
references/workflow_orchestration_template.md
) or
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 ( vs vs ). Anything not found goes in — never filled by guessing.
- Verification agent — adversarial, default-skeptical stance. Grade every claim and rule . The job is to actively hunt falsifying evidence, especially for the headline claims (the trophy stat, "#1 ranking", funding amount, user counts). names the biggest water in that dimension.
Grading rubric,
, verdicts, and both JSON schemas →
references/evidence_grading_rubric.md
.
Typical dimensions (tailor to the benchmark type — person / company / product):
- Subject background + headline-claim attribution (the #1 bubble target)
- Corporate base — entity, founding, funding/valuation
- Core product/business real metrics — user counts, revenue, rankings, awards, cross-verified against third parties
- Playbook teardown — platform matrix, persona, content types, how they borrow other people's audiences, how personal IP funnels to the product
- Comparison sample — a structurally-similar peer or parallel path
- Sector + how this class of playbook usually wins and usually fails
Phase 3 — synthesis: due-diligence conclusion (single agent, consumes all verdicts):
- Real relationship map (correcting the common misreadings from Phase 0)
- Bubble-busting table — claim | evidence level | verdict | one-line basis, sorted by most-water-first
- Playbook teardown — concrete, copyable actions
- 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):
- 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
- Landing points — exactly how the commissioner uses it (their to-B service / their own IP / their tooling)
- 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 . (What's unique here is the skeptical verification stance and the L1–L4 bubble grading, not the parallelism.)
- Person-subject identity / footprint checks — invoke (ACH hypothesis matrix, Bellingcat-style pivots) rather than re-deriving identity attribution.
- Mainland-China corporate registration / funding — invoke the family of skills for 工商 data.
- Social-platform playbook data — the CLI covers B站/小红书/抖音/YouTube/X.
Read before you run
references/evidence_discipline_traps.md
references/evidence_grading_rubric.md
references/attribution_and_resource_mapping.md
references/workflow_orchestration_template.md
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