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Stop AI coding agents from guessing. MCP server + Claude Code plugin that enforces intent alignment BEFORE coding: PRD → intent-confidence gate → Mermaid contracts (state machines / sequence diagrams / decision tables) → mechanical lint with zero CRITICAL → then code. Human-in-the-loop, file-persisted, zero-config.
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Stop AI coding agents from guessing.
Complex business requirements. A thin or sloppy PRD. No legacy code to reference. That is exactly where coding agents start inventing: "happy path only, no failure flow", "idempotency with no server-side design" — gaps get silently filled with plausible-looking guesses, and you find out far too late.
intent-gate moves intent alignment before coding, into the requirement-analysis stage: a PRD goes through an intent-confidence gate → gaps are resolved through a three-level funnel → the output is technically annotated Mermaid contracts (state machines / sequence diagrams / decision tables) → a mechanical lint gate must reach zero CRITICAL → only then is coding allowed to begin.
It runs as an MCP server (stdio subprocess) — plug-and-play with Claude Code and other MCP clients. No daemon, no credentials, zero config — full workflow out of the box.
intent-gate owns exactly one stage of the pipeline, the requirement-analysis stage:
PRD ──▶ [ intent-gate: confidence gate → intent alignment → Mermaid contracts ]
──▶ summary.md (every edge technically annotated, lint CRITICAL = 0)
──▶ coding agent (Claude Code / Cursor / any agent) ──▶ tests ──▶ shipThe leverage is asymmetric: if the contracts land well, the coding stage is a free win — every edge, step and rule already has a home, so any competent agent can implement the spec. That is why intent-gate deliberately does NOT touch coding, review or deployment: downstream agents are interchangeable; the input contract is not.
The diagram is the instrument, not a deliverable. Layer 1 (reading): sweep the PRD text for explicit ambiguities. Layer 2 (drawing): hard-draw draft diagrams in the FIRST turn — every node/edge that can't be drawn yet becomes a TBDn placeholder, never a guess (every edge is a forced decision). Placeholders route by kind: technical gaps go to code evidence, business gaps go to a structured human question — and may only be cleared by code evidence or a human ruling. A placeholder left in a delivered diagram is a lint CRITICAL (L13).
What one real requirement looks like after a full intent-gate pass.
The requirement, in one sentence:
"A smart-locker drop-off page — show the shipment details, allow changing the
locker size, scan and drop the parcel. Must be duplicate-proof, expiry-proof,
and masked."
That's it. Exactly where a coding agent starts guessing.
What happened along the way:
rules?) straight from the PRD.
and stalled at "submit": what about failure? Should duplicate protection live on the server or the frontend? Each un-drawable edge became a TBDn placeholder instead of a guess.
"How do you guard against double-submit on rapid clicks / re-scan?"
were logged verbatim → injected into the state-machine edge and decision-table BR-01.
and any leftover placeholder would itself be a CRITICAL.
The output: a technically annotated Mermaid contract (full state machine):
stateDiagram-v2
direction LR
[*] --> DROP_CONFIRM: 进入寄件确认页 (DB_QUERY_SHIPMENT, LOCKER_ROUTING_QUERY)
DROP_CONFIRM --> SLOT_OPTIONS_LOADING: 请求可用柜格 (IOT_GET_FREE_SLOTS)
SLOT_OPTIONS_LOADING --> DROP_CONFIRM: 获取成功 (RETURN_SLOT_LIST)
SLOT_OPTIONS_LOADING --> DROP_CONFIRM: 获取失败 (RETURN_ERROR, SIZE_EDIT_DISABLED)
DROP_CONFIRM --> SLOT_ROUTING: 修改柜型或站点 (DB_INSERT_SHIPMENT_SLOT, DB_UPDATE_SHIPMENT)
SLOT_ROUTING --> DROP_CONFIRM: 路由成功 (DB_UPDATE_SHIPMENT_SLOT, RETURN_NEW_FEE)
SLOT_ROUTING --> DROP_CONFIRM: 路由失败 (RETURN_ERROR, ROLLBACK_OR_KEEP_ORIGIN)
DROP_CONFIRM --> EXPIRED: 扫码时效超时 (RETURN_ERROR_CODE, GUIDE_RESCAN)
EXPIRED --> [*]: 引导回首页 (FRONTEND_NAVIGATE)
DROP_CONFIRM --> SUBMITTING: 点击确认投递 (REDIS_LOCK_SUBMIT, IOT_UNLOCK_COMMAND)
SUBMITTING --> STEP_QUERY: 提交成功 (DB_UPDATE_SHIPMENT, QUERY_CURRENT_STEP)
SUBMITTING --> DROP_CONFIRM: 提交失败 (RELEASE_LOCK, RETURN_ERROR)
SUBMITTING --> TERMINATE: 开柜指令失败 (DB_UPDATE_DROP_STATUS_TERMINATE)
STEP_QUERY --> LOADING: DropStep=awaitingDrop (RETURN_NEXT_STEP)
STEP_QUERY --> IDENTITY_CHECK: DropStep=identityCheck (RETURN_NEXT_STEP)
STEP_QUERY --> PAY_CHANNEL: DropStep=payChannel (RETURN_NEXT_STEP)
STEP_QUERY --> QUERY_PROGRESS: DropStep=queryProgress (RETURN_NEXT_STEP)
STEP_QUERY --> DROP_CONFIRM: DropStep=dropConfirm (STAY_ON_PAGE)
LOADING --> SUCCESS: 跳转下一步 (FRONTEND_NAVIGATE)
IDENTITY_CHECK --> SUCCESS: 跳转下一步 (FRONTEND_NAVIGATE)
PAY_CHANNEL --> SUCCESS: 跳转下一步 (FRONTEND_NAVIGATE)
QUERY_PROGRESS --> SUCCESS: 跳转下一步 (FRONTEND_NAVIGATE)
SUCCESS --> [*]: 投递流程结束 (END)
TERMINATE --> [*]: 运单终止 (END)Every edge carries a mandatory technical-action annotation — the edge DROP_CONFIRM --> SUBMITTING reads (REDIS_LOCK_SUBMIT, IOT_UNLOCK_COMMAND). What the coding agent receives is a spec, not an illustration.
Decision table (rule logic forced into a matrix — no "happy path only" survival):
Rule · Condition · Action · Failure branch BR-01 · Submit: lock LOCK:dropoff:submit:{shipmentId} conflict · Redisson lock serializes, concurrent submits rejected · Lock conflict → error code DUPLICATE_SUBMIT BR-07 · Scan deadline scanExpireTime · 5-minute double check: page countdown + server-side fallback on submit · Expired → SCAN_EXPIRED, guide re-scan
The full contract: 10 business rules (BR-01..BR-10) + 3 sequence diagrams + an intent-injection mapping table (15 Q&A rounds, every answer on record).
The mechanical lint gate (pre-delivery self-check report):
summary_lint: CRITICAL 0 / 2 findings total (all MINOR, human-review class)- [MINOR][L3] state STEP_QUERY has 5 outgoing edges — confirm triggers are distinguishable- [MINOR][L3] state SUBMITTING has 3 outgoing edges — confirm triggers are distinguishableWith CRITICAL > 0 the contract refuses to ship and coding refuses to start — this is checked by code, not self-reported by the model.
Same requirement without intent-gate (observed in our control run): the agent would front-end-disable the submit button instead of designing a server-side distributed lock, hardcode a success page instead of routing by queryCurrentStep, and never model the EXPIRED state at all. The guessing space is structurally compressed, not politely discouraged.
Same PRD (a deliberately vague withdrawal-confirmation page), same project, same model, same human answering: intent-gate vs a peer intent-alignment plugin, plus a two-round independent red-blue review of the intent-gate output.
and the adversarial rounds, including 6 failure edges that no amount of PRD reading reveals
intent-gate missed across 24 questions — the two detection surfaces complement each other; neither replaces the other
"no open issues" — and the red-blue review still found 6 CRITICAL defects (one would have let any user void someone else's order). Self-attestation is not evidence; independent review is not optional
Setup, isolation protocol, token metering and round-by-round findings (Chinese): summary · full report
intent-gate accepts UTF-8 text files or .docx (the dominant format for business requirements). Three ways to hand one over:
Way · Example · Notes Absolute path · analyze D:\docs\locker-dropoff.docx · Most reliable; readable from anywhere Relative path · analyze docs/locker-dropoff.md · Resolved against the project root (HG_WORKSPACE_ROOT, defaults to the startup directory) Conversation attachment · Drag the file into the chat; have the agent persist it first · Attachments are conversation content to the host, not a path — the agent must write them to disk before handing over a path
Supported formats:
.md / .txt / .csv / .json and similar.docx: native support — mammoth is a core dependency (installed automatically; tables become Markdown tables); if markitdown already exists in your environment (e.g. for another document MCP), it is reused for enhanced extraction.doc / .pdf / .xlsx and other binaries: convert first — Word「Save As → .docx or Plain Text (.txt)」, PDF export/save-as textErrors carry the next step: missing file, binary format, and encoding failures each return a distinct message telling you what to do.
Once installed, you drive it with plain language. This table is the whole manual:
You say · Who picks it up · What happens "分析这个需求 / analyze this PRD"(贴文档或指文件) · 🔴 Red team(requirement-alignment) · Reads the playbook first, runs the Step 0 confidence check, then asks you structured questions (≥3 options + "other"), one gap at a time "画个状态机 / 生成 DDL" · 🔴 Red team · Same entry — pattern routing decides which diagrams your requirement actually needs "继续"(中断后/新会话) · 🔴 Red team · Resumes from the on-disk ledger (.harness/requests/{feature}/_review/) — no session memory needed 回答它的提问:"1",或 "4 余额不足一律拒绝" · the funnel · Answer logged verbatim → injected into the diagrams → settled with a precise landing point "红蓝对抗 / blue-team review" —— 另开新会话说 · 🔵 Blue team(red-blue-review) · Independent adversarial review of the delivered summary: R1–R9 checks → findings with verdict PASS / FAIL-可整改 / FAIL-重做 "按 findings 整改"(回到红军的会话里说) · 🔴 Red team revision discipline · Each finding settled into revision-log.md with a real landing point, lint re-runs to zero CRITICAL; anything conflicting with your earlier rulings comes back to you for a decision 什么都不说,直接让它写代码 · compact routing Hook + contract-coding · The agent reads the landed summary.md contract before coding; blocked or lint-CRITICAL contracts refuse to be coded against
Two rules worth remembering:
your coding agent will execute against.
adversarial review into self-check. Deliver with the red team, then open a new conversation and say "红蓝对抗".
Intent-confidence gate (Step 0): assess the light status before analyzing any requirement. Until 🔴 core-logic gaps are eliminated, the report status must be blocked, the first task in any breakdown is forced to be [BLOCKER], and downstream coding is forbidden from starting.
Three-level alignment funnel (Step 0.5) — cost decreases level by level, fully non-blocking throughout:
① Code-grounded verification: technical gaps are checked against the code first;
a unique ground truth is recorded directly, zero interpersonal cost
② AI-disclosed inference: registered with an explicit evidence chain, batch-confirmed
by a human at session end (pure inference is forbidden on money-critical main flows)
③ Human ruling: structured-option questions (≥3 mutually exclusive options + "other"),
one question at a timeMechanical enforcement (enforced by MCP tools, not by prompt self-discipline):
pending-questions.md;until every box is ticked you don't get intent_aligned_ready;
resolve_question mechanically rejects an empty landing point — every injectedintent must be precise down to a state-machine edge, a sequence-diagram step, or a decision-table rule number; if no landing point is found, closing the question is forbidden and it must be asked again;
draft_mapping script locatesreal section/rule/step numbers;
lint_summary mechanical self-check (L0–L8: unparseable state machine(blind-guard) / missing terminal state / dead states / misplaced anchors / BR references / table read-write matrix…), CRITICALs must reach zero before delivery.