Read live from the toolbox’s capability manifest — each one a functioning, evidenced building block (not a roadmap promise), reachable through the surfaces shown. New capabilities appear here automatically as the platform grows.
JobFrame canonical Job Family × Focus × Level taxonomy + classifier
AdoptedThe governed job substrate — SOC/O*NET registry + families × functions × universal levels + verbatim profiles + aliases + /classify; the spine every other JobFrame + comp capability maps into.
RESTMCPUIData
JobFrame coordinate system ('Pantone for jobs')
ShippedPer-profile computable coordinates in measurable spaces (structural + content live, pay@1 + semantic staged) with 3-state Delta-E matching, versioned editions, and the Job Swatch UI — making similarity, leveling, and pricing measurable.
RESTMCPUIData
Non-infringing two-sided matcher + capability-DAG (PAT-204)
BuildingIP-clean design-arounds for two in-force matching patents: symmetric per-dimension twoSidedMatch, alignCapabilities via LCS + set coverage, and scoreDagGaps over a static prerequisite DAG — explainable match + gap typing, no GNN, no gated shared vocabulary.
Data
AnyComp decision layer (strategy → optimizer → simulator → scenarios)
ShippedThe compensation Decision OS loop over three-value-rooted measures: elicit strategy → priorities → objective → optimize → simulate → several scenarios ('never one option'). The flagship custom comp engagement.
RESTMCPUI
Onboarded comp-service cloud run (the data-services gate)
ShippedRuns a comp service over a client's onboarded dataset end-to-end: segmentation-studio intake → roster adapter → pluggable midpoint (incl. pay-model market reference) → compa-ratio audit → persisted, tenant-scoped, auditable run. The gate every cloud comp data-service rides; paid-or-internal entitlement-gated.
RESTMCPData
Tier 3 Bring-Your-Own-Survey source calibration
ShippedBuilds a client's pay model on THEIR licensed survey: fits a source log-offset so our model reproduces their observed medians (the source-as-a-variable mechanism), then gap-fills the cells their survey didn't cover. The enterprise-credibility tier; client data never persisted/redistributed.
RESTMCP
Proprietary benchmark store (the data flywheel)
ShippedCross-client, JobFrame-matched comp + HR-metric benchmark database that every data-absorbing tool contributes to automatically (by design). Aggregates-only + min-N + HMAC-tenant; turns accumulated observations into Bayesian priors with honest error ranges that tighten as data grows — the durable moat competitors can't accumulate.
RESTData
Market-pay model (combined-source equation + benchmark + dataset SKU)
ShippedThe combined-source ridge equation (holdout R²≈0.89) — predict + differentials + equity — with a free single-lookup SEO magnet and a gated bulk/feed dataset SKU; combines all comp sources with source-as-a-variable, IP-clean per PAT-203.
RESTUIData
Commerce till (Stripe checkout → entitlement → gated fulfillment)
AdoptedThe reusable till: a SKU catalog → Stripe checkout → entitlement → accessUrlFor gated download/feed, plus the offerings feed that drives the storefront. Powers every paid toolbox dataset/pack.
RESTData
Niche Benchmark Survey — consortium pooling engine + per-niche GTM
ShippedPools N peer companies' auto-matched comp/HR rosters into min-N-protected, anonymized benchmark cuts (profile + function rollups; sparse Focuses release upward). A cut publishes only with min-N datapoints AND >=2 contributors. One niche registry drives per-niche landing pages + systematized outreach kits. Metric-agnostic (pay + HR metrics).
RESTUIData
Enterprise Job Architecture Workspace + governance
ShippedTenant PRD-16 workspace: never-type top-down constructor, draft-to-approved versioned lifecycle, tenant overlays/overrides, job-person-position graph, thin governance (tenants never mutate canon).
RESTMCPUI
Organizational Overlays (philosophy / change / strategy)
ShippedFlagship, plausibly first-of-its-kind: apply a philosophy/transformation/strategy org-wide through the JD system; source-tagged fragments injected by level x function, composing with Focus, versioned + rollout-tracked.
RESTMCPUI
Assessment & Rubric Library
ShippedPaired kits per Family x Focus x Level: interview banks (STAR), BARS, perf criteria, with a validity/EEOC adverse-impact trail; reincarnation-composed.
RESTMCPUI
Career Architecture + Learning loop
ShippedCareer pathing off universal levels + coordinates + development gap + learning catalog, closing assess-gap-learn-re-measure with the assessment library.
RESTMCPUI
HRIS Dataset Mapper (the enterprise wedge)
ShippedBulk-map noisy client job records to canonical Family x Focus x Level: detect, cluster, predict (confidence+evidence, abstaining), cluster-grain correct, tenant overlay, export.
RESTMCPUI
People Analytics + Clarification Campaigns
ShippedAnalytics with 5-state value provenance + a clarification-campaign builder routing minimal questions to the people who know, turning corrections into governed evidence.
RESTMCPUI
Public-standards crosswalk (ESCO + Lightcast)
ShippedIP-clean crosswalk canon to open standards (O*NET/SOC + ESCO + Lightcast) via computed coordinate position, no copyrighted codes stored (PAT-203); global + EU-compliance interop.
RESTMCPUI
Persona Library (GTM Toolbox)
BuildingThe toolbox-owned library + serving + monetization of buyer personas (pa-site Bicycle generates; this persists/serves/sells). First deliverable of the GTM Toolbox cluster (GTM-TOOLBOX-1, G0).
RESTMCP
T1 — Positioning & Differentiation Wizard (GTM Toolbox)
BuildingOffer + buyer + competitive set → a positioning statement, a differentiation map (where you win vs each alternative), and a StoryBrand one-liner. The 'how to sell it' tool.
RESTMCPData
T2 — StoryBrand / Message Builder (GTM Toolbox)
BuildingCustomer + problem + guide role + plan + stakes → a complete SB7 brandscript + a website one-liner + an elevator pitch. Emits the universal brandscript contract verbatim (no new generator).
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T4 — Offer & Value-Prop Composer (GTM Toolbox)
BuildingProduct + target segment + price + pain removed → a value-prop statement, an offer structure (bonuses / risk-reversal / guarantee), and a value-vs-price framing.
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T5 — Channel-Mix & Distribution Planner (GTM Toolbox)
BuildingSegment + budget band + product type + where attention already lives → a ranked channel shortlist + a starter paid/owned/earned mix + the one channel to start with.
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T6 — Content Engine (GTM Toolbox)
BuildingBuyer + positioning → content pillars, an editorial calendar, and drafted pieces. The drafts ARE the value-first gifts in the give-before-you-ask doctrine.
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T7 — Growth-Experiment & Funnel Designer (GTM Toolbox, flagship)
BuildingThe AARRR stage that hurts + the per-stage rates you know → a diagnosed bottleneck (worst-converting stage wins) + an ICE-ranked, falsifiable pass/fail experiment backlog, each with a value-of-information bridge.
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T8 — North-Star Metric & Measurement Scorer (GTM Toolbox)
BuildingBusiness model + funnel → a recommended North Star metric, the 3-4 input metrics that move it, and a 'what you're flying blind on' gap read (each gap paired with its value-at-risk + cheapest close).
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GTM Studio — artifact store + run ledger + commerce seam
ShippedThe shelf where every GTM tool saves its outputs, where the paid version gets gated, and where every run is logged (tool_runs) — built once for all the tools. Generic artifact store (0.2.0) discriminated by tool.
RESTMCPData
Benchmark-Outreach — find niche people + run value-first sequences
ShippedThe engine that finds the right niche people and runs value-first email sequences to gather pay data. Four audience types, each resolving to one canonical persona. Exposed read-only to AI tools (sends stay gated).
MCPData
GTM Agency Services (web + app development)
BuildingDone-for-you delivery operating the GTM Toolbox as an agency: the tools generate the strategy, the agency builds it. Four productized packages — Landing Page Sprint (fixed), Marketing Website + Web App MVP (quote), Care & Growth retainer (fixed).
RESTUI
GTM Market Inventory (build-ahead 'Spots')
BuildingThe proactive flip: build + SEO-rank a staged listing page for a (niche × geography) ahead of demand, then rent it exclusively (one business per market) to the deliverer who serves it. Composes content-engine + canonical-segmentation + company-intelligence + benchmark-outreach + commerce.
RESTUI
Technical-Recruiting Directory + comp-survey flywheel (GTM-F1)
BuildingThe lead market-inventory vertical and the center of the technical-talent flywheel: niche engineering markets (JobFrame Focuses incl. emerging roles the big surveys miss) × metros → SEO listing pages carrying role intelligence + an honest comp snapshot. The micro comp survey is the flywheel — recruiters/employers contribute → proprietary pay data → better content → better ranking.
RESTUI
Single-model fairness monitor (automated-decision disparity audit)
BuildingONE classifier trained with a fairness-aware loss (demographic-parity / equalized-odds penalty) plus an external held-out audit cohort that measures protected-class disparity post-hoc. Non-infringing design-around of US11922435B2's dual-model bias architecture: no second/monitoring model, no pseudo-unbiased corpus, no dual-classifier comparison. Complements the HO-191 pay-equity analyzer (compensation disparity) on the protected-class fairness thread.
RESTMCPData
Semantic candidate↔role ranking via transformer embeddings (US11403597B2 design-around)
BuildingRank a candidate pool against a role by cosine similarity of TRANSFORMER CONTEXTUAL EMBEDDINGS (dense sentence/text embeddings of role + candidate skill/experience text). IP-clean design-around of in-force US11403597B2 — the representation is a dense contextual embedding, explicitly NOT the patent's topic-model (LDA bag-of-words) document vector, and the rank is cosine, NOT token-set overlap. The dense-embedding complement to the PAT-204 token-overlap two-sided matcher (not a fork). Embedding-provider-agnostic: precomputed embeddings OR a pluggable Embedder port with a deterministic mock fallback.
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Time-aware skill-trajectory matching via sequence alignment (CN120525496A design-around)
BuildingMatch a candidate's skill TRAJECTORY against a role's required trajectory by SEQUENCE ALIGNMENT (Needleman-Wunsch) of two typed (skill → proficiency → time) sequences, blended with final-state coverage and an optional deterministic recency attention — rewarding demonstrated GROWTH over time (proficiency that rose to the required level beats the same final skills with no/late progression). IP-clean design-around of in-force CN120525496A: 1-D sequence alignment (+ optional deterministic attention), explicitly NOT a graph neural network over a capability-evolution graph (no nodes/edges, no message-passing, no learned params). The time-aware complement to the PAT-204 token-overlap matcher and the US11403597B2 semantic-rank — both of which are point-in-time. Pure + deterministic, no external model dependency.
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Causal-discovery KPI dependency engine (US11620601B2 design-around)
BuildingConstraint-based causal-structure recovery over a single block of tabular workforce/KPI data from ordinary ETL: the PUBLISHED PC algorithm (Spirtes/Glymour/Scheines) with partial-correlation Fisher-z conditional-independence tests builds a KPI DEPENDENCY GRAPH (skeleton + v-structure/collider orientation -> a CPDAG subset) and a LEVERAGE RANKING of drivers by their DIRECT (partial) effect on a named outcome KPI -- a confounded/mediated correlation (X _||_ Z | Y) is NOT scored as a direct driver. Deterministic, stateless, pure compute; no external service, no API key. IP-clean design-around of in-force US11620601B2: published causal-discovery on standard ETL, explicitly NOT the patent's integrated multi-engine value-graph platform with multi-source ingestion and continuous ML retraining. The MULTIVARIATE complement to the bivariate calculus.key-driver importance screen (not a fork).
RESTMCPData
Outcome-first assessment↔performance validity loop (US20160092822A1, public-domain)
BuildingCorrelates a PREDICTOR BATTERY against actual job performance to refine which predictors actually predict here: captures the realized outcome as one INDEPENDENT measurement event, correlates each predictor against it (composing the single-pair criterion-validity primitive), emits per-predictor observed + operational validity coefficients, ranks predictors by realized validity, and returns a predictor-refinement report with refined normalized weights (proportional to positive significant validity; noise/counter predictors -> 0; signal-free battery -> honest uniform fallback). A standard criterion-validity loop in public-domain I-O psychometrics. COMPLEMENTS (does not duplicate) PAT-204's outcome-feedback weight loop (#196): that learns weights for one specific matcher's signals; this is a general assessment->performance VALIDITY layer over any predictor battery vs any realized-outcome event. Pure / deterministic / stateless; no live source or API key.
RESTMCPData
Schema-first canonical comp-data normalizer (US11829954B2 design-around)
BuildingIngests HETEROGENEOUS compensation records (arbitrary pay-component labels, periods, currencies, FTEs) and maps every component onto a CANONICAL COMP SCHEMA: classified base/variable/commission/allowance, converted to one display currency, annualized, and FTE-normalized -> canonical records + per-class totals. A DATA-QUALITY report (missing currency, missing/mismatched period, unmapped class, unknown FX rate, negative amount) is emitted as a BYPRODUCT of schema conformance -- it never compares against an expected payroll amount and removing it would change no normalized number. Deterministic, stateless, pure compute; static FX snapshot, no live source/API key. IP-clean design-around of in-force US11829954B2: that patent is an ERROR-DETECTION-FIRST pipeline that normalizes commission & performance-pay to FLAG PAYROLL ERRORS; this inverts the posture -- normalization is primary, anomaly/quality flags fall out of canonicalization rules. The missing front-door COMPLEMENT (not a fork) to anycomp.onboarded-roster (which assumes already-normalized rows) and wage-compliance/currency (single-amount FX) -- it produces the single comparable canonical basis those primitives assume, feeding benchmark integrity.
RESTMCPData
Deterministic counterfactual recommendations (US11836633B2 design-around)
BuildingActionable-explainability layer ON TOP of the JobFrame matchers: given a candidate FEATURE VECTOR + per-feature ATTRIBUTIONS (SHAP/anchor-style, supplied or derived by a transparent deterministic attributor) + a current/target score + declared CONSTRAINTS (immutable features, [min,max] ranges, step granularity), compute the MINIMAL feasible feature edits that raise the predicted outcome to the target -- "what would make this candidate rank higher". Output is edits to the REAL candidate feature vector (from->to->delta + per-change score contribution + rationale), never a synthesized profile; immutable features are never proposed for change; sparsity-budgeted (maxChanges); honest already-met + infeasible (reasoned, no partial answer). Pure + deterministic, no external model / no API key. IP-clean design-around of in-force US11836633B2 (which GENERATES counterfactual candidate profiles with a GAN -- adversarial generator/discriminator synthesizing fictional higher-ranking profiles): the mechanism here is deterministic feature attribution + constrained search over the declared feasible region -- NO GAN, no generator, no discriminator, no sampling, no learned synthesis. COMPLEMENT (not a duplicate) to the two-sided matcher / semantic-rank / trajectory-match: those give the score + gaps, this gives the minimal change set to clear a threshold.
RESTMCPData
Pay-structure-for-retention recommender (US11636435B2 design-around)
BuildingGiven a workforce COHORT (attributes + current compa-ratio + headcount), its CURRENT pay structure (band midpoint, range spread, base<->variable mix), and a RETENTION SIGNAL (current annual attrition), recommends STRUCTURE ADJUSTMENTS (midpoint lift, variable-share trim, spread widening) that optimize a retention objective -> the recommended structure + per-lever adjustments with rationale + the PREDICTED RETENTION EFFECT (projected attrition, attrition reduction, retained headcount, projected compa-ratio). A transparent, a-priori, OVERRIDABLE dose-response (every coefficient exposed; market-pressure-amplified; capped at the retention floor); below-band high-attrition cohorts get a bounded midpoint lift + mix/spread moves, well-paid low-attrition cohorts get an honest minimal/no-change result. Deterministic, stateless, pure compute; no live source/API key. IP-clean design-around of in-force US11636435B2: that patent predicts which BENEFITS/PLAN an employee SELECTS vs org financial goals; this INVERTS the I/O -- the target is the pay-STRUCTURE design and the objective is RETENTION (structure -> retention), NOT plan selection. The missing structure->retention RECOMMENDER complement (not a fork) to anycomp.byos-structure-build (prices grade ranges from supplied midpoints) and anycomp.exit-risk (per-employee P(exit) predictor) -- it recommends what structure to change and the retention payoff those leave out.
RESTMCPData
Dense semantic job-to-job similarity (US12524739B2 design-around)
BuildingScores how similar two JOB descriptions are (role-to-role) — each a bundle of role/responsibility/skill TEXT or precomputed dense EMBEDDINGS — by COSINE similarity in a shared vector space with standard facet-weighted HIERARCHICAL aggregation, returning the aggregate similarity in [0,1] + a per-facet cosine breakdown; also ranks a candidate job set against a query job. Embedding-provider AGNOSTIC: supply precomputed embeddings, OR let the spoke embed free text via a pluggable EMBEDDER PORT with a deterministic in-repo mock (feature-hashing; no live API key) — the same seam JobFrame's `semantic` coordinate space staged (FU-3) but left unimplemented. Deterministic, stateless, pure compute. IP-clean design-around of in-force US12524739B2: that patent parses a job description into a CUSTOM TRIPLET schema and scores hierarchical job-similarity over that proprietary structure; this uses a DIFFERENT representation — dense semantic-role EMBEDDINGS + standard cosine + standard hierarchical aggregation. The non-infringement is the representation (a dense embedding vector, NOT a triplet structure): no triplet extraction, no triplet-similarity schema, no structured-relation scoring; similarity == cosine over dense vectors. Job<->job is a DIFFERENT pair than JobFrame's candidate<->role two-sided matcher and is the missing concrete implementation of JobFrame's staged semantic seam — it complements (does not fork) the matcher + coordinate engine.
RESTMCPData
Cohort-similarity career-pathing over a skill ontology (US20200372473A1, public-domain)
BuildingRecommends a person's likely/feasible NEXT ROLES from a corpus of historical role transitions, ranked by cohort similarity over the skill graph, with the skill gaps to close for each. Given a current role + weighted skill profile and a transition corpus (from->to + each mover's skills + count): represents the person and every mover's origin profile as a sparse skill vector over the shared skill ontology, computes cohort similarity = cosine(person, mover) (no-skills movers get a small floor), keeps movers >= minCohortSimilarity (the cohort "people like you"), ranks destination roles by similarity-weighted move-share (the cohort's share of onward moves into that role), and for each destination emits the skill gaps = cohort prevalence - held weight (clamped >=0), largest first. The public-domain implementation of US20200372473A1's roles-as-skill-nodes + learned role-transition cohorts, via standard cohort/cosine-similarity methods. COMPLEMENTS (does not duplicate) career-development (taxonomy/coordinate ladders+lattices over the canon, no behavioral data) and job-family-agent (candidate<->role matching / single-role fit): this is pathing by behavioral DATA — "what did people with a profile like yours actually move into next?". A unique profile with no similar movers returns an honest empty/sparse result. Pure / deterministic / stateless; the transition corpus is posted in the request (no live source, no API key, nothing persisted).
RESTMCPData
Knowledge Graph (Marketing Intelligence Stack substrate)
BuildingThe entity/relationship substrate at the center of the Marketing Intelligence Stack: a typed, tenant-scoped graph that FEDERATES JobFrame roles + company-intelligence companies (reference nodes) and owns the cross-cutting marketing edges (competes-with, hires-for, covers-topic, ranks-for…). The join layer that makes CI/SEO/AI-Inbound/Content cohere + the grounding layer for AI-cited answers.
RESTMCP
SEO (search-visibility engine)
BuildingMarketing Intelligence Stack search engine: keyword research, rank tracking (append-only time series), technical/on-page audits, and (Phase 2) content gaps + briefs. Instruments the build-ahead market-inventory Spots + /recruiting directory so they rank — and proves it with real, never-fabricated rank/traffic. Internal + a productized agency SKU (audit · rank-tracking · keyword strategy).
RESTMCP
Marketing / Google Analytics (measurement layer)
BuildingThe measurement layer of the Marketing Intelligence Stack: GA4 + Google Search Console + a conversion warehouse. Ingests measured metrics (GA4 sampling flagged), mirrors the commerce till's real conversions, serves calculus-ready rollups, and supplies the HONEST market-inventory Spot traffic number (null until real). T8 recommends metrics; this computes the real ones. SEO reads our GSC rows.
RESTMCP
Competitive Intelligence (monitoring + gap findings)
BuildingMarketing Intelligence Stack competitor-monitoring layer: an append-only observation store (SERP / keywords / content / backlinks / positioning / hiring over time) that derives a ranked gap-findings opportunity list the rest of the stack consumes (SEO, Content, positioning). Distinct from company-intelligence (the company master): CI owns OBSERVATIONS + references the master by key. Internal + a productized competitive-audit/monitoring SKU.
RESTMCP
AI Inbound (AEO/GEO + capture)
BuildingMarketing Intelligence Stack AI-native inbound layer, two halves: (1) Answer-Engine Optimization — citability audits + 3-state answer-engine citation probes + the llms.txt answer-feed so AI engines cite us; (2) AI inbound capture — intent detection + conversational qualification → PII-redacted routed leads. Internal (our properties get cited + capture leads) + a productized agency service.
RESTMCP
Content Development (governed lifecycle)
BuildingMarketing Intelligence Stack content layer: the governed brief→draft→optimize→publish→measure lifecycle that produces the pages the market-inventory Spots + /recruiting directory rank on. Immutable versions + an optimization gate (seo/aeo/editorial/fact-grounding) + a human publish gate. CONSUMES the T6 content engine + Knowledge Graph + the existing corpus/library (referenced grounding) — never re-ingests books. Internal + a productized managed-content service.
RESTMCP
Organizational Network Analysis (connectivity + ONA summary measures)
ShippedBoils a collaboration graph down to theory-grounded, benchmark-bearing ONA summary measures (reach, density, broker-dependency, community structure, siloing E-I, assortativity, vertex similarity, tie diversity) — interpretable scalars for leaders, plus before/after graph similarity — never a node-link hairball.
RESTMCPData
Exec-ask translator (catalog front door / MF-180)
Takes a vague executive question and walks it backwards to the analytical question, the construct(s) at stake, the method, the data to pull, and the pitfalls — then routes the analyst into the matching acting tool (e.g. the MF-181 scale validator). The free front door / navigation model of the catalog. EXTENDS the intent-router (PAT-N1): reuses its centralized getModel client + generateObject discipline, adds the analyst-facing walk-back + a data-driven acting-tool routing registry.
RESTMCPData
Cross-cultural / cross-industry skill-equivalence (CN121503503A design-around)
BuildingScores how EQUIVALENT a source skill descriptor is to one or more target-market skills — each free text or a precomputed dense EMBEDDING — by COSINE in a shared vector space, returning a continuous Bayesian CONFIDENCE INTERVAL (not a discrete strict/approx tier) plus a POST-HOC region/industry adjuster applied at match time; also ranks a target-market skill set against a source skill. Embedding-provider AGNOSTIC (precomputed embeddings or a deterministic in-repo feature-hashing mock, no API key). Deterministic, stateless, pure compute. IP-clean design-around of IN-FORCE CN121503503A: that patent reads equivalence off a labeled-tier culture KNOWLEDGE GRAPH with pre-built culture labels; this uses a DIFFERENT mechanism on three axes — a DENSE embedding representation (no labeled graph), a continuous Bayesian confidence interval (no discrete tiers), and runtime POST-HOC region adjusters (no pre-baked culture labels). Cross-cultural use case: US "stakeholder management" <-> JP "nemawashi". Complements (does not fork) job-similarity (job<->job) and Principia equivalences (instrument<->instrument).
RESTMCPData
Task-as-Skill-Evidence — verifiable skills from completed work (US20210035048A1 free)
BuildingDerives a VERIFIABLE, evidence-grounded skill profile from the work a person actually completed — not self-report. Given completed-task records (description, outcome, difficulty, completedAt) + a skill ontology, tags each task to the skills it evidences by dense-embedding cosine, calibrates per-skill proficiency by NOISY-OR over evidence weighted by recency × outcome × difficulty, returns a Bayesian confidence interval (tightening with corroboration), the supporting tasks as PROVENANCE, honest unevidenced-skill gaps (never claimed), and co-occurrence skill adjacencies. Skill is demonstrated, not declared. Embedding-provider agnostic (deterministic in-repo feature-hashing mock, no API key); deterministic, stateless, pure compute. US20210035048A1 (project & skill-set verification) is ABANDONED → free to implement, no design-around. Complements: output FEEDS Principia competency-rollup as evidence; the missing tasks→skills inference that skill-trajectory-matching (skill SEQUENCES) does not do.
RESTMCPData