fathomx

# PM DeepResearch **PM DeepResearch** is a product manager's **deep competitive-research skill** for Claude Code. It turns a product decision ("should we enter / differentiate / build / upgrade to AI?") into a decision-oriented, evidence-complete **13-chapter competitive report**, applying a five-dimension product methodology (Job & real competitive set, capability matrix, Kano, ODI opportunity gaps, positioning & whitespace) with explicit epistemic tagging and falsifiability. It is a **Skill / orchestration layer (Layer 1)** that runs on top of the **Lapis** MCP research core (Rust, upstream [`4o3F/Lapis`](https://github.com/4o3F/Lapis), AGPL-3.0). Lapis owns MCP execution, provider calls, agent loops, budget guards, schema validation, and byte-equal evidence provenance; PM DeepResearch carries the product methodology via prompt assets + Skill-layer assembly. Lapis is consumed unchanged — any engine-schema needs are filed upstream as requirements, not patched in. > Status: Phase 4 — **engine window closed; 4 capabilities validated**. All four capability profiles ran end-to-end on golden topics against the upstream-synced engine (#9 `9db7464`, verbatim): competitive **22/24**, product-capability **23/24**, innovation-direction **24/24**, product-requirements **24/24** — zero regression at each anchor. **2026-05-31 pivot ([ADR-0010](docs/decisions/0010-upstream-contribution-pivot.md))**: the skill capability layer is now contributed directly upstream to `4o3F/Lapis` (two-layer split — skill commits upstream / engine needs filed as issues); productization (installer / TUI→CLI / packaging) is handed off to 4o3F. ## The skill | Path | What it is | | --- | --- | | [`skills/pm-deep-research/SKILL.md`](skills/pm-deep-research/SKILL.md) | The competitive deep-research skill entry: decision-intent routing, complexity tiers, five-dim decomposition, persona assembly, Lapis MCP calls, evidence post-processing, gap audit + quality-floor self-verification, 13-chapter report. | | `skills/pm-deep-research/prompts/layer1/` | Orchestration prompts: `task-decomposition`, `agent-allocation`, `evidence-postprocess`, `final-report`, `claude-only-degradation`. | | `skills/pm-deep-research/prompts/layer2/` | Persona prompts: `persona-strategist`, `persona-experience-analyst`. | | [`skills/deep-research.md`](skills/deep-research.md) | Generic Lapis research skill kept as a fallback / base. | When the Lapis MCP server is unavailable, the skill degrades to **Claude-only** (search MCP directly, same methodology) — degradation is not failure; the methodology lift is pure prompt capability. ## Key documents | Document | Purpose | | --- | --- | | [`ROADMAP.md`](ROADMAP.md) | Single source of truth for the plan (rolling-wave). | | [`docs/specs/pm-deep-research-spec.md`](docs/specs/pm-deep-research-spec.md) | Universal product deep-research spec (MECE-6, method library, personas, evidence pillar) shared by all four capabilities. | | [`docs/specs/capabilities/competitive.md`](docs/specs/capabilities/competitive.md) | Competitive deep-research profile (five-dim spine, 13-chapter mapping) — v2.0 validated. | | [`docs/specs/orchestration-interface.md`](docs/specs/orchestration-interface.md) | Layer 1 ↔ Lapis interface (request/result, budget, evidence post-processing, degradation). | | [`evaluation/rubric.md`](evaluation/rubric.md) | 12-dimension scoring rubric + prose floor; golden-sample anchor. | | [`docs/decisions/`](docs/decisions/README.md) | Architecture decision records (ADR-0001…0010). | ## Vendored Lapis engine This repository **vendors the Lapis engine source** (`crates/`, `Cargo.toml`, `lapis.example.toml`) as a **verbatim, unmodified snapshot** of an upstream pinned commit (currently `9db7464`), so the MCP core can be built and run locally for development and validation. The engine is upstream-owned ([`4o3F/Lapis`](https://github.com/4o3F/Lapis)); we do not develop or patch it here — any engine-schema need is filed upstream as a requirement ([ADR-0009](docs/decisions/0009-lapis-binary-source-vendored-ci-build.md), reinforced by [ADR-0010](docs/decisions/0010-upstream-contribution-pivot.md)). Packaging / installer / binary distribution are handed off to 4o3F; this repo's skill capability layer is contributed upstream rather than shipped as a standalone product. Build and run the engine locally: ```bash cargo build --release cp lapis.example.toml lapis.toml # then enable providers + export the referenced API keys ./target/release/lapis serve --config lapis.toml ``` The server speaks MCP over stdio and exposes two tools: | Tool | Purpose | | --- | --- | | `aspect_research` | Runs one research aspect and returns an `AspectResearchResult`. | | `deep_research` | Runs multiple research aspects and returns a `DeepResearchResult`. | Supported request `schema_version`: `0.1`. Engine-side details: | Document | Purpose | | --- | --- | | [`docs/mcp-usage.md`](docs/mcp-usage.md) | MCP client interface and tool schemas. | | [`docs/configuration.md`](docs/configuration.md) | Runtime configuration, providers, budgets, logging. | | [`docs/development.md`](docs/development.md) | Repository layout and contributor commands. | ## Requirements - Rust toolchain with Rust 2024 edition support (to build the vendored engine). - Claude Code (or another MCP client) able to run stdio MCP servers. - At least one enabled model provider, and a search provider for aspects that allow search. ## Common development checks ```bash cargo fmt --all -- --check cargo test --workspace cargo clippy --workspace --all-targets -- -D warnings ``` ## License GNU Affero General Public License v3.0. See [`LICENSE`](LICENSE) for details.