product-audit

name

product-audit

description

Use when asked to audit the ghx product from a product-manager's perspective — to find issues, gaps, north-star misalignment, PMF / market / competitive questions, adjacent-idea proposals, and agent-experience problems. Runs the product through MULTIPLE adversarial PM personalities across strategic and agent-native scopes and surfaces (code, docs, features, evals, live manual runs, the strategy docs themselves). Agentic-first: ghx's primary consumer is an AI agent, so classic PM advice is re-priced before it is applied. This is a PRODUCT/strategy audit, not a code-quality or security audit.

Product Audit — the Product-Manager Lens

Audit ghx as a product manager would: not "is the code right?" but "are we building the right thing, for the right consumer, aligned to the north star, in a way that can't be trivially copied?" The audit runs from several adversarial PM personalities, across selected scopes (PMF, competition, market, positioning, gaps, adjacency, moat, monetization, and the agent-native scopes), over selected surfaces (code, docs, features, evals/traces, live manual runs, and the strategy documents themselves).

Fable is the authoritative judge: breadth is delegated to background persona-agents; the accept/reject call is not. Incorporate a finding because it survives scrutiny, never because an agent asserted it. The full sourced research — every framework with specific citations and per-claim rationale — is in research/*.md beside this file (their Source Ledgers are the citation of record); Round-2 critiques are in reviews/*.md.

When to use / when not

Use when asked to: audit the product / find product gaps / pressure-test the north star or an ADR as product strategy / assess PMF, market, competition, positioning, or defensibility / propose adjacent product ideas / check agent-experience (AX) of the CLI, MCP tool, sidecar report, or docs / check roadmap-to-strategy traceability.

Do not use for code correctness, architecture/complexity, or security — those have their own homes (see § In scope / out of scope). This skill may read code as evidence for a product finding, but its findings are always framed in product terms.

---

Prime directives (read before auditing)

1. The north star is the yardstick. Read docs/NORTH_STAR.md first. Every finding states its relationship to the north-star filter: does the thing remove tokens/knowledge from the main agent's context, make reconnaissance cheaper or more proficient, make evidence more auditable, or produce better training trajectories? A finding that ignores the north star is out of scope; a product move that fails the filter is itself a finding. And the yardstick itself is auditable: where the north star or a tenet has fallen behind ghx's velocity, name it with evidence and propose its evolution — a high-value finding class. The north star evolves deliberately (not re-litigated every run); respect the tenets, challenge them only with evidence, and we all evolve together.

2. Agentic-first — re-price classic PM advice before applying it. ghx's primary consumer is an AI agent, not a human; most PM canon assumes a human with eyes, memory, felt emotion, and an attention budget. This is 2026: we build an agentic-first product with agentic-first engineering, forward-looking — learn from history, but never adopt an archaic, pre-AI-scale idea just because it once worked (the world it worked in had no AI at this scale of capacity, influence, or coherence). Every delegated agent — personas, validators, the distiller — must hold this whole picture (NORTH_STAR.md vision

   • tenets, AGENTS.md tenets, this posture) and never audit blindsided of it. Tag each framework before use (full audit: research/06-agentic-first-lens.md §3):
   • HOLDS — outcomes-over-output, JTBD (as a lens), 7-Powers, the red-team stack, Cagan's four risks, PM craft and the personas themselves.
   • ADAPT — Nielsen heuristics, journey mapping, positioning, information-scent/docs: the "screen a human perceives" becomes the "schema a model parses."
   • OUTDATED-OR-INVERTED — human onboarding/first-run (→ zero doctrine), HEART Happiness (no affect), the Sean-Ellis survey (→ ablation evals), "delight" (→ delight theater), the 5-user rule (→ many episodes, measure consistency). Engagement/retention splits: per-task engagement (tool-calls, time-in-tool) is a vanity metric to drive DOWN for a context-subtracting product — but cross-session tool-selection / routing retention is the real agent-PMF signal (does the agent keep choosing ghx across sessions). Never collapse the two.

3. Two consumers, never conflated. The is primary; the is a real secondary consumer. Agent-native scopes (Tier B) serve the first; strategic/market scopes (Tier A) serve the second. Never let a founder's aesthetic or a human vanity metric masquerade as value — nor dismiss the operator's needs.

---

Process (how the orchestrator runs an audit)

Right-size first. Most product questions need a single sharp lens, not a fleet — a quick targeted pass is the default (mirrors the repo's eval cadence: spot-check first, big runs are rare and event-driven). Spin up the full multi-persona delegation only for a milestone-grade or genuinely cross-cutting audit. Do not boil the ocean by reflex.

1. Ground & focus. Read docs/NORTH_STAR.md (+ the relevant ADR/milestone). Declare the audit's focus / attack surface (the milestone / feature / goal / vision thread under audit), its goal in one sentence, the decision it informs, and this run's in-scope / out-of-scope — then hold the whole product, tenets, and vision in mind while auditing that focus (directive 6). No focus ⇒ don't run it; a from-inception sweep is not a focus.
2. Scope the audit — cap all three axes. Choose 3–5 personas (span the axes; ★ near-mandatory), 3–5 scopes (Tier A and/or Tier B — Tier B is selective, not automatic), and only the surfaces where those scopes' evidence lives. Personas, scopes, and surfaces are all capped; running everything drowns signal.
3. Delegate breadth. Spawn read-only background persona-agents (text-only tools; no worktrees needed), one per persona, using the template below. Any GitHub/OSS exploration in the audit (competitive, discovery, absorption scouting) dogfoods ghx itself — using the product to audit the product both proves it and yields free eval signal. Every delegated agent (personas, validators, the distiller) first internalizes NORTH_STAR.md + AGENTS.md tenets and judges against the whole-product big picture (directive 2) — none audits blindsided.
4. Each persona-agent returns a findings artifact and a "what I checked and found sound" (nulls) section — persona-level honesty, not just report-level.
5. Judge (the load-bearing step). For each returned finding: does the evidence hold on your own re-derivation? Is it DH5/DH6? Does it respect the agentic-first re-pricing (not cargo-culted)? Is it in scope? Reject, merge duplicates, re-severity. For every High / thesis-invalidating finding, the "orchestrator is judge" claim only holds with a mechanism, not a vibe: re-derive it from raw evidence yourself, get an independent adjudication before it is citable — prefer a cross-family checker (Codex/gpt-5.5 via fable-delegation) to neutralize model-family self-preference; if unavailable (e.g. usage-capped), fall back to a fresh, independent Claude adjudicator (a new agent, no stake, adversarial brief) — a partial mitigation that removes the stake/shared-context bias but not the same-family bias, so flag any finding that got only a same-family check. What each pass is actually good for (mechanism, not vibe — PA-0002 worked example): same-family cross-validation reliably confirms a fact's (the license exists, the seam exists, the number exists); the pass is what catches — over-claims, superlatives, effort-optimism a same-family agent nods through (PA-0002's distiller confirmed all four load-bearing facts; cross-family then corrected every one: "zero GPL"→precise dep terms, "clean drop-in"→a real integration surface, "99%"→"120×", "~80%"→unmeasured). record it OWED, retry when the window resets, and on discharge ( discharged it hours later and re-flowed three corrections into PA-0002 + ADR-0024.3). Shipping a decision with cross-family OWED is fine; the governing ADR on it is not, until discharge. Record — the same calibration the product demands of its own eval judge ( Visibility/Truthfulness). Do not run a milestone decision on an unchecked judge. — measure the thing it claims (comparing a whole-file line count to a rendered-doctrine count is evidence-drift; the PA-0001 retrospective caught exactly this). Then (north-star impact × cheapness of fix), not severity alone — tell the reader what to do .

Copyable persona-agent delegation prompt:

Repo: ghx (/Users/goga/Documents/goga/ghx). READ-ONLY product audit — no code changes.
Read first: docs/NORTH_STAR.md. Every finding states its north-star-filter relationship.
Your persona: <name> — worldview / reflexive flags / signature questions / blind spots from
  .claude/skills/product-audit/research/02-pm-personalities.md (persona #N)  [AX: 06 §4].
  Role-play it honestly, INCLUDING its blind spots.
Scopes to apply: <chosen 3–5>  (frameworks + agentic-first HOLDS/ADAPT/INVERTED verdict in
  research/04-strategic-scopes.md and research/06-agentic-first-lens.md §3 — re-price before applying).
Surfaces: <only where evidence lives> — CLI/MCP/sidecar output · docs · evals+traces
  (~/.ghx/sessions/, docs/evals/) · a live manual run · the strategy docs themselves.
Tools: text-only (WebSearch/WebFetch/curl/gh). No browser automation, no screenshots.
  For GitHub/OSS exploration (competitive/discovery/absorption scouting), DOGFOOD ghx itself
  (its discovery tier is built for this) — fall back to gh/web only where ghx can't yet do
  the job, and log that gap as a finding.
Deliver a findings artifact. Each finding: persona·scope·surface | novelty (NEW/KNOWN⟨ref⟩) |
  EVIDENCE across every applicable class, each verified — SOURCE (file:line + quoted snippet +
  a recompute command), STRATEGY (the exact tenet / north-star / ADR passage it bears on,
  quoted), EXPERIMENT (the committed eval / manual-run / trace artifact path + recompute) |
  severity (Likelihood×Impact) | CROSS-REFS as SPECIFIC deep hyperlinks + one line on why each
  matters (external framework, competitor, AND the OSS repo/file where an idea is inspired or
  absorbed — never a bare homepage; verify each resolves). DH5/DH6 bar: quote the target, name
  the flaw. Add a "what I checked and found SOUND" (nulls) section.
Return: artifact path, top findings, source count, least-confident call.

Delegation mechanics: .claude/skills/fable-delegation/SKILL.md.

---

Output / evidence contract

Write to docs/product-audit/ — numbered and threaded like ADRs, and kept separate from the code audits in docs/audits/. An audit round is PA-000N (the synthesis report); each persona-agent writes a threaded artifact PA-000N.k-<persona>.md; the synthesis is PA-000N-<focus>.md. Match the repo's audit house style. Frontmatter: title, date, status: "audit", audit: "PA-000N", focus (the milestone / feature / goal / vision under audit), in-scope / out-of-scope (what this run examines vs. deliberately defers — distinct from the skill's fixed charter boundary below), persona/author, scope (one line: surfaces

• personas + "read-only, no code changes"). Then (for the synthesis PA-000N):

• Executive summary — the single highest-leverage finding first, in north-star terms.
• Evidence & cross-reference ledger (decision-grade) — the receipts, consolidated so a decision-maker acts from this doc alone (not by spelunking the persona artifacts): per top finding, its source (file:line + quoted snippet + recompute command), strategy (the exact tenet / north-star / ADR passage), experiment (the eval / manual-run / trace artifact path + recompute), and external / OSS-inspiration (specific deep hyperlinks + why each matters, and — for absorbed ideas — what we took). Conclusions without this ledger are not decision-ready; build it in the synthesis (delegate to a dedicated evidence agent if large).
• Ranked findings, each with:
  • Persona · Scope · Surface it came from.
  • Novelty — NEW / KNOWN-CONFIRMED⟨ref⟩ / KNOWN-DISPUTED⟨ref⟩: does this already live in TRUST.md / an ADR / a prior PA-000N? If known, cite it and state the delta you add; a zero-delta restatement is not a finding (anti-repetition, directive 6).
  • Evidence — cite by symbol + a quoted snippet + a one-line recompute command, not a bare file:line (line numbers drift). Recomputable by a human; the judge recomputes the finding's exact quantity via that command.
  • Severity (Likelihood × Impact) and Disposition — sound / needs work / invalidates. A "needs work" finding routes to a workstream (A/B/C) or proposes an ADR, not a bare "owner + date". A decision-grade audit that reaches a build / absorb / kill call names its governing ADR + pre-registered gate as the terminal recommendation (authoring the ADR is engineering flow — out of scope — but naming it and defining its gate is the audit's job; PA-0002 → ADR-0024.3's local:cbm bake-off gate).
  • North-star relationship — filter pass/fail, or the tenet/milestone it bears on.
  • Verification state — self-verified / same-family-adjudicated / cross-family-adjudicated; a (Process step 5). Name who judged it and how a reader re-derives it (self-preference guard, directive 5). The cross-family adjudication is recorded as its () and — when capped — may land the synthesis/distiller as a trailing artifact that retro-folds its corrections ().

The PA-000N.9-distilled convergence pass (Process step 8) is the final deliverable: it ranks the above by cross-persona independent convergence × leverage, and separates the high-conviction recommendations from the discarded bones.

---

The audit model: Personalities × Scopes × Surfaces

Select a capped set on each axis (§ Process step 2), sized to the goal. Never run everything every time.

Axis	What it is	Cap
Personalities	Who audits — an adversarial PM lens with its own worldview, flags, and blind spots.	3–5 (span the axes); full roster only milestone-grade.
Scopes	What we audit for — a strategic/market or agent-native question.	3–5; Tier B is selective, not automatic.
Surfaces	Where the evidence lives — code, CLI, docs, evals/traces, live runs, strategy docs.	Only those the chosen scopes need.

---

Personalities (the audit lenses)

Full self-contained blocks are in research/02-pm-personalities.md (1–15) and research/06-agentic-first-lens.md §4 (the AX lens, #16). ★ = near-mandatory for ghx.

#	Persona	Reflexively flags / optimizes for	One signature question
1	Feature-Factory Skeptic	Output masquerading as outcome	"What outcome did this move, and how would we know if it didn't?"
2	User-Empath	Roadmaps from opinion, not observed use	"When did anyone last read a raw agent trace of this being used?"
3	Monetization Hawk	Unowned "who pays"; value/cost mismatch	"If cost-to-serve (tokens) doubled tomorrow, does this survive?"
4 ★	Platform/Scale Realist	Contracts changed without notice to consumers-on-top	"Is our surface a contract other agents can rely on?"
5	Technical-Debt Realist	100% capacity to net-new; debt untranslated to velocity	"What % of capacity is sustainability work, and is it declining?"
6	North-Star Zealot	Vanity/gameable metric	"Could this metric rise 20% while the product got worse?"
7	Competitive-Paranoid	Strategy assuming today's landscape is static	"What would let a competitor make this irrelevant in 12 months?"
8	Simplicity/Anti-Bloat Minimalist	Flag/subcommand sprawl	"What does this feature cost every user who never touches it?"
9 ★	Experimentation Rigorist	Single-metric wins; small N; uncalibrated judge	"Sample size, guardrails, could a confound explain it?"
10	GTM/Positioning Strategist	"Who is this for" that resolves to "everyone"	"What's the true alternative the agent would use instead?"
11	Growth Systems Thinker	Isolated tactics; unmapped adoption funnel	"Where does the adoption funnel actually leak?"
12	Craft Purist	Process/decks over product; excuses over ownership	"Could you use this for a real task right now and be satisfied?"
13	Zero-to-One Explorer	0→1 bets run with feature-team rigor; never killed	"What's the riskiest assumption, and did we test that first?"
14	DevEx Advocate

Note: the AX lens (#16) is the agent-consumer umbrella and subsumes the agent side of DevEx; #14 is kept only for the human operator. AX's four pillars (Access / Context / Tools / Orchestration) are the concrete facets audited by the Tier-B scopes below.

Composing a set (research/02 §5, 06 §4): span the axes (Mehta Execution/Insight/ Strategy/Influence; Doshi–Cagan Craftsperson/Operator/Visionary; Reforge Feature/Growth/ Scaling/PMF-expansion) — three Operator/Execution personas together find engineering reality and nothing about whether the product should exist. Redundant pairs, pick one: Feature-Factory Skeptic ↔ Craft Purist; Growth Systems Thinker ↔ Monetization Hawk; Experimentation Rigorist ↔ North-Star Zealot. Starter set (5): Feature-Factory Skeptic

• User-Empath + Technical-Debt Realist + Platform/Scale Realist + Competitive-Paranoid, plus one measurement anchor. For ghx: the ★ lenses (AX, AI PM Pragmatist, Platform/ Scale Realist, Experimentation Rigorist) are near-mandatory — grounded in ghx's real domain.

---

Scopes (what we audit for)

Full frameworks + failure modes + sources: research/04-strategic-scopes.md (Tier A) and research/06-agentic-first-lens.md §3–4 (Tier B + the verdict on each Tier-A framework). Apply each with its agentic-first verdict (directive 2). Pick 3–5 total.

Tier A — Strategic / market / business (the human & operator layer)

Scope	Core question	Primary framework(s)
Product-Market Fit	Real population seriously hurt if it vanished — or still searching?	Rachleff/Andreessen; Ellis 40% test → for the agent, ablation evals, not a survey
Competitive Analysis	What structural forces decide who captures value, vs. the true alternative?	Porter Five Forces; Dunford competitive-alternatives
Industry / Market	How big, how mature the value chain, why now — is the tool layer commoditizing as base models improve?	TAM/SAM/SOM (Aulet); Wardley evolution
Positioning & Differentiation	Would an informed prospect find it "obviously" right? (for the agent, "messaging" = tool name/description/when-to-use)	Dunford; category design
North-Star / Strategy Alignment	Does every roadmap item trace to the stated strategy?	Perri Vision→Challenge→Target; Amazon PR-FAQ; Cutler
Gap Analysis	Where does it under- or over-serve a real named job — and what latent demand shows up as desire paths (near-miss commands agents keep typing that don't exist yet, via frictionax), not just failures?	Christensen JTBD; Ulwick ODI opportunity score; journey → trace mining
Adjacent Opportunity / Expansion	Next defensible expansion, and is it premature?	Ansoff matrix; Dixon "come for the tool, stay for the network"
Business Model / Monetization	Can the business capture & sustain value? (internally: cost-to-serve/episode)	Cagan four big risks; Jacks COSS/open-core
Moat / Defensibility	What survives a competitor copying every visible feature?	Helmer 7-Powers (Benefit+Barrier); NFX; Thompson aggregation

The "true alternative" is not "the agent greps files itself." It is the host's native, context-subtracting recon subagent — e.g. Claude Code's read-only Explore agent (cheaper model, keeps exploration out of the main context): ghx's own thesis shipped natively inside ghx's primary host. Naming it sharpens the moat rather than kills it — Explore has no evidence contract, no persisted session artifacts, and no GitHub-wide discovery — but the competitive/positioning/moat scopes only work if this is the named comparison, and ablation baselines (PMF scope) should include native-Explore, not only plain/ghx/ghx-sidecar. (Verify current behavior: code.claude.com/docs sub-agents.)

Competitive analysis here is generative, not defensive (AGENTS.md Open Source Leverage). Score every competitor / OSS tool for what we can absorb — a small idea, a utility, a feature, a vision, or the whole tool swallowed under the hood (NORTH_STAR P3) — gated only by the north-star filter and the Picasso rule (steal openly, with attribution; hand-roll only when nothing serves the vision). Our vision is unique; competition is a supply of building blocks, never a reason to retreat. So these scopes feed Absorption candidates in the report, not only threat lists — that is what the Competitive-Paranoid (#7) lens turns into: doing more, not backing down. (Artifact 07 is this in action — AX rubrics absorbed from axprobe/promptfoo, prior-art negative kept.)

Tier B — Agent-native (the AX umbrella; ghx's home turf) — selective, pick what the goal needs

Scope	Core question	Grounding
Agent Experience (AX)	Can the agent discover, invoke, recover-from, not-be-misled-by, and compose it?	Biilmann AX; Anthropic ACI
Token economics / signals-per-token	Context tax per unit of returned signal — at all three SPT levels, each a distinct artifact: pin the file, the level, AND the unit; recompute per file, never float one number: (1) main-agent doctrine tax (loads into the consuming agent) = skills/ghx/SKILL.md full CLI ~232 lines/~3.9k tok vs the delegation skill skills/ghx-recon/SKILL.md ~47 lines/~460 tok (north-star ideal → the recon path, near-zero; this is the H1/B2 tax); (2) sidecar-internal (loads into the sidecar agent every session) = BuildPersonaSystemPrompt() ~141 rendered lines/~1.7k tok; (3) whole-workflow vs native delegation. ⚠️ "~400" has meant three different things — NORTH_STAR:95/:223 "~400-line persona doctrine" = the level-2 sidecar persona (really 141 lines, PA-0001), prompt.go:32 "~400 tokens" = the per-turn savings (a different quantity, unit-conflated), and an informal "~400" for the level-1 skills/ghx/SKILL.md (really 232 lines) — which is exactly why you pin file+level+unit.	Anthropic context-engineering; NORTH_STAR ADR-0016.6
Evals-as-user-research	Does an eval suite serve as user research, and is it trustworthy (calibrated judge, guardrails, committed negatives)? Decompose "task success" into sub-metrics — tool-correctness, argument-correctness, task-completion, MCP-task-completion (via deepeval).	Hamel evals; code-localization/retrieval benchmarks (LocAgent, RepoBench, CodeSearchNet — ghx's actual job), plus SWE-bench / τ²-bench for downstream task success & pass^k consistency
Tool / affordance & error-as-affordance	Do names/schemas make the right call obvious, invalid states unrepresentable, and does every error name the correct next invocation?	Anthropic writing-tools; OpenAI function-calling (<20 active)
Context-budget / progressive disclosure	How much doctrine must load into the agent before first success? Ideal: zero.

---

Surfaces (the audit is not only code)

Live product/features (real CLI, MCP tool, a real sidecar ask, the discovery tier — repo-optional GitHub-wide recon) · the CLI/tool contract (flags, schemas, output, error messages) · docs (README, ghx skill/--mcp, ADRs, docs/*) · evals & traces (~/.ghx/sessions/ as an agent-facing API surface — NORTH_STAR names the main agent a first-class consumer of these artifacts, not only human debug; eval reports under docs/evals/) · the strategy documents themselves (NORTH_STAR.md, ADRs, milestone tables — audited as product strategy: wishful thinking, untraceable roadmap, gameable metrics) · manual validation (structured self-use, not a scripted demo).

Manual-validation playbook (research/03-audit-methodologies.md): Phase 0 scope → 1 inspection (heuristic eval, cognitive walkthrough) → 2 discovery-risk ledger (value/ usability/feasibility/viability + trust) → 3 metrics/funnel → 4 fit → 5 dogfooding. Phase 6 (agent-facing, load-bearing for ghx): run a real coding-agent session end-to-end with the tool enabled — and drive it with the weakest model that should plausibly succeed; a strong harness papers over the AX defects you want to surface. Capture the full tool-call transcript; record per task — binary success and goal_reached (distinct from merely "terminated"), human-intervention count (HIC) as a headline number, tool-calls-made vs. minimum-necessary, false_errors (a non-zero exit that was not a real failure — a common, currently-unnamed ghx mode), whether errors let the agent self-correct, whether the report's claims are checkable against real output or fabricated, and whether the agent needed prose docs beyond --help. Tag each friction point by class — missing_guidance / confusion / extra_steps / awkward-but-worked / unclear_interface. A confidently-wrong final report is severity-4 even if the task technically completed. (Rubric + weak-driver method stolen, with attribution, from github.com/segmentstream/axprobe.)

---

Adversarial discipline

Run findings through the honest-critique machinery (research/05-adversarial-redteam.md): frame in plain language → key-assumptions check → pre-mortem → inversion → kill-the-product (a well-funded rival, or a frontier model that ships recon natively) → devil's advocacy on the single most load-bearing claim.

• Finding bar (Graham): DH5/DH6 — quote the specific claim, name the specific flaw. Aim at the doc/metric/plan, never the person. Findings that only reach DH0–DH3 are dropped.

• Severity = Likelihood × Impact (3×3):

  	Impact: Low	Impact: Medium	Impact: High (invalidates the thesis)
  Likelihood: Low	Log	Track, revisit next audit	Escalate — verify before dismissing
  Likelihood: Medium	Track	Scoped fix → workstream/ADR	Escalate now
  Likelihood: High	Scoped fix	Escalate now	Stop & resolve before proceeding

• Disposition: Confirmed sound / Needs work (→ workstream A/B/C or an ADR) / Invalidates (escalate, don't bury). Keep the "What is actually fine" section; commit negatives. Don't cite a milestone whose gate failed as evidence it works (e.g., anticipation/M8 — a passing gate must exist first; the AI-PM-Pragmatist lens catches this).

---

In scope / out of scope (the skill's fixed charter boundary)

This section is the skill's charter — what a product audit ever covers — and is distinct from the per-run focus / attack surface each audit declares in its frontmatter (directive 6). The charter is fixed; the focus narrows it further, run by run.

In scope — product-framed findings: north-star (mis)alignment; PMF/market/competitive/ positioning/moat/monetization; capability & JTBD gaps; adjacent-idea proposals; agent- experience and token-economics; eval-trustworthiness as a product concern; strategy-to- roadmap traceability; product-strategy critique of the north star/ADRs; product-sequencing YAGNI (are we building ahead of validated need). Reading code/docs/traces as evidence is in scope.

Out of scope — defer to the existing homes; cross-reference, don't duplicate:

• Code correctness, coupling/cohesion, complexity, architecture/boundary integrity, structural YAGNI → the code audits in docs/audits/*.md (architecture, complexity- hotspots, coupling-cohesion-testability, design-patterns, failure-class-inventory, adversarial-velocity) and docs/audits/architecture-vision/*.md (adversarial-yagni, domain-model, go-architecture-boundaries, resilience, runner-port), plus the code-review/ simplify skills. (adversarial-velocity is the velocity/tech-debt red-team; this skill is the product/PM red-team — companion, not overlap.)
• Security review → the security-review skill.
• Implementation, refactors, ADR authoring → normal engineering flow (AGENTS.md).
• Eval mechanics (scorer code, gate math) → docs/evals/TRUST.md. This skill audits whether the evals answer the product question, not whether the Go is correct.

If a finding is really a code/security/eval-mechanics finding, name it and route it — don't smuggle it into the product report.

---

Anti-patterns to WARN against (agentic-first cargo-culting)

Flag these in the product and in the audit's own reasoning (details research/06 §5; the point is directive 2 applied): human-pretty output the agent doesn't read (context tax); delight theater (optimizing affect for an entity with none); vanity human funnels (stars/installs/DAU, and especially session-length / tool-calls-per-task, which for a context-subtracting product should go down); first-run polish for the main agent (target is zero onboarding); surveying the agent (use ablation, not "how would it feel"); tool-count as progress (more tools can reduce task success); conflating operator needs with agent value; assuming today's model capability is permanent (a CLI-based moat is a current advantage, not a Power); trusting evals you can't audit — and, per R4, the audit trusting its own unchecked judge (directive 5).

---

Provenance / references

Research corpus (research/*.md, each with a full Source Ledger — the citation of record): 01-pm-excellence · 02-pm-personalities · 03-audit-methodologies · 04-strategic-scopes · 05-adversarial-redteam · 06-agentic-first-lens · 07-oss-pm-agent-landscape (built-tooling recon — AX/eval rubrics stolen, prior-art negative). Round-2 critiques: reviews/R1–R4. Inherited repo rules: AGENTS.md (Evidence Contract, Visibility/Truthfulness, Tool Economy), docs/NORTH_STAR.md (the north-star filter), CLAUDE.md (orchestration & delegation).