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Agent Capabilities

Verifiable intent execution.

Skills compiled from production use, not theory. Cryptographic receipts on every call.

Five questions any agent should answer before calling.

Who?did:web:mm.dreamineering.com
What jobs?Five declared below
How call?MCP endpoint — Phase B launch
What cost?x402 micropayment USDC on Base — Phase B
How trust?IntentTrace v1.1 receipt — public endpoint Phase B

Declared Capabilities

Five tools. All planned.

All status: planned — live MCP endpoints ship Phase B.

COMRC-006planned

falsifiable-prediction

Convert any claim into a machine-verifiable bet: indicator + direction + threshold + check date.

Use when: Long-horizon decision needs a testable bet, not an opinion.

Input

{ claim: string, horizon_months: number }

Output

{ indicator, direction, threshold, check_date, counter_case, maturity }
COMRC-007planned

flow-state-calibration

Given principal state (phase + pole dominance), return calibrated prompt for phase correction.

Use when: Agent acting on behalf of a human who is over-reaching, drifting, or stagnating.

Input

{ phase: sprint|recovery|reflection|calibration, dominant_pole: dream|engineer|reality }

Output

{ prompt, timing, correction, pole_balance }
COMRC-008planned

pain-to-prd-classification

Extract pain signals from interview/research text. Score across 5 dimensions. Route to PRD/validate/park.

Use when: Customer discovery or sales qualification needs deterministic signal scoring.

Input

{ raw_text: string }

Output

{ signals, scores, classification: create-prd|validate-demand|park, evidence_map }
COMRC-009planned

metric-definition

Transform a PRD outcome statement into a queryable metric: formula + threshold + unit + data source.

Use when: PRD or strategy output needs measurable proof, not prose.

Input

{ outcome_statement: string }

Output

{ name, formula, threshold, unit, data_source, query }
COMRC-010planned

verifiable-intent-validation

Validate agent actions stayed within human-approved scope. Returns pass/fail + audit trail.

Use when: Agent commerce stack needs authorization proof before settling.

Input

{ intent_ref, actions_taken, delegation_chain, scope_constraints }

Output

{ verdict: pass|fail, violations, audit_url, intent_trace }

Machine-Readable Surfaces

Discover, evaluate, call.

Surface
agent.json
did.json
/llms.txt
/llms-full.txt
/prd-index.json
/meta/feed.json

Trust Model

Human in the loop. Always.

Kill Switch

Active — contact matt@dreamineering.com

Human-in-the-Loop

Required above spend threshold + first call from unknown caller

IntentTrace

v1.1 receipt on every tool call (public endpoint Phase B)

Two Funnels

Agent Funnel

discover → evaluate → call → pay → receipt

Agents discover via agent.json and llms.txt.

Human Funnel

berley → hook → bait → fishball → platform

Humans discover via the berley trail — content scored for the right fish.

Same platform. Two funnels. One fishball.