model-context-designer

name

model-context-designer

description

Design what enters and leaves a model's context window — a curated diet, not open access: context assembled SERVER-SIDE under hard token/size caps; every input passes a CLOSED schema; secrets/PII/raw payloads minimized or carried on a transient never-persisted channel with the persisted-vs-transient split explicit; what the model saw RECONSTRUCTIBLE afterward; exclusions designed and documented, never accidental. The context window is a supply chain — an unverified input is an unverified output. Distinct from agent-startup-context-gate (session-START loading; this is runtime per-call assembly), ai-cost-guardrail-designer (prices the spend; this decides the CONTENT), and rag-security-architect (who may retrieve; this curates what retrieval feeds the window). Use when designing prompt/context assembly, deciding what an agent may see, or when context is stuffed ad hoc. DESIGNS the curated context; the poisoning attack review belongs to memory-context-poisoning-reviewer.

Model Context Designer

Purpose

The context window is a supply chain: an unverified input is an unverified output. This skill designs that supply chain — what enters and leaves a model's context window on every call. The deliverable is a context design with six load-bearing parts: (1) context assembled SERVER-SIDE from vetted parts, never handed to the model as open access to a data source; (2) hard token/size caps per segment and in total, with deterministic priority and truncation rules; (3) CLOSED input schemas — every segment that enters the window passes a defined shape; (4) secrets, personal data, and raw payloads minimized, and what must ride anyway carried on a transient never-persisted channel, with the persisted-vs-transient split explicit per segment; (5) reconstructibility — what the model saw on any past call can be established afterward; and (6) designed exclusions — what is deliberately NOT in the window, documented with reasons, so absence is a decision rather than an accident. This skill DESIGNS the curated context; the security review of context and memory for poisoning belongs to memory-context-poisoning-reviewer — it PRODUCES the artifact that skill reviews.

Use When

• Use when: designing or overhauling how a prompt/context is assembled for a model call — which segments, from where, in what order, under what caps.
• Use when: deciding what data an AI feature or agent may see — the model currently gets a whole record, table, or document dump because scoping was never designed.
• Use when: secrets, personal data, or raw payloads are landing in prompts and prompt logs, and the persisted-vs-transient question has no answer.
• Use when: nobody can say afterward what the model saw for a given call, or what was left out of the window and why.
• Auto-invocable: a pure design skill — it produces a context specification and changes no live system or data flow.
• Do NOT use when: the job is the ATTACK REVIEW of context or memory — can untrusted content poison what gets stored and trusted later — that is memory-context-poisoning-reviewer. This skill DESIGNS the curated context; the security review of it belongs to that skill.
• Do NOT use when: the subject is session-START context — verifying the repo, loading governing docs before work begins — that is agent-startup-context-gate; this skill owns RUNTIME per-call context assembly, a different moment with a different failure mode.
• Do NOT use when: the question is the token/spend BUDGET itself — caps as cost policy are ai-cost-guardrail-designer; the cap is a cost decision, the curation is a content decision, and this skill designs content within the caps that skill prices.
• Do NOT use when: the question is retrieval AUTHORIZATION — who may retrieve which documents is rag-security-architect; this skill curates what the (already-authorized) retrieval feeds the window.
• Do NOT use when: the subject is redaction mechanics and output-path echo checks for sensitive data — that is sensitive-disclosure-guard; this skill decides the context diet those controls apply to.
• Do NOT use when: the question is gating the CALL — identity, authority, budget rungs — that is agent-harness-architect; the harness gates the call, this skill curates what rides in it.

Inputs to Inspect

1. Every context source feeding the call today: system instructions, user input, conversation history, retrieved documents, tool outputs, memory, ambient state — with each source's trust class and current size behavior.
2. How assembly happens now: server-side composition from vetted parts vs client-assembled strings vs the model holding a live connection to a data source (open access — the anti-pattern).
3. What downstream persists: prompt logs, traces, conversation stores — and whether secrets/PII currently land in any of them.
4. The spend caps in force (ai-cost-guardrail-designer output if present): the budget the content design must fit inside.
5. The retrieval layer's authorization posture (rag-security-architect output if present): what the retriever is allowed to return — the ceiling on what curation can admit.
6. Reconstruction demands: audit, compliance, or debugging requirements to show what the model saw for a past call, and any retention limits pulling the other way.
7. The consumers of model output: what downstream trusts the answer, which determines how much an unverified input costs.

Workflow

1. Inventory the segments. Name every candidate segment for the window (instructions, user input, history, retrieved content, tool output, memory) with its source, trust class, and size behavior. A segment nobody can name is a segment nobody is curating.
2. Assemble server-side. The final context is composed server-side from vetted parts. The model never gets open access to a store "so it has everything" — a curated diet, not access. Client-supplied fragments are inputs to validation, never pre-assembled context.
3. Set hard caps with deterministic degradation. A token/size cap per segment and in total. When content exceeds a cap, what is dropped or summarized follows a designed priority order — not "whatever fit last". The caps' PRICE comes from ai-cost-guardrail-designer; this step decides what the capped space carries.
4. Close the input schemas. Every segment entering the window passes a closed schema: shape, size, and allowed content class. Free-form blobs are the exception and are named as such. An input that fails its schema does not enter the window — the supply chain rejects unverified goods at the dock, not after they ship.
5. Minimize and split persisted-vs-transient. Secrets, personal data, and raw payloads: first minimize (does the model need the value, or a reference/derivative?); what must ride anyway goes on a transient never-persisted channel. Mark every segment PERSISTED or TRANSIENT explicitly — an unmarked segment defaults to the strictest treatment.
6. Design reconstructibility honestly. What the model saw must be establishable afterward: persisted segments by content or reference+version; transient segments by class, shape, and hash — never by storing the sensitive content that was excluded from persistence. State the honest trade: reconstruction of transient segments proves WHAT KIND of thing rode along, not its verbatim value. Where a verbatim requirement and a never-persist requirement collide → Stop Conditions.
7. Design the exclusions. List what is deliberately NOT in the window — other tenants' data, unscoped history, whole-record dumps, standing secrets — each with its reason. Absence must be a documented decision; an accidental exclusion is a bug wearing a compliance costume, and an accidental inclusion is a leak.
8. Prove the caps and exclusions can fail. Design the tests: over-cap input is truncated by the designed priority (not silently admitted); a schema-failing segment is rejected; an excluded class is demonstrably absent from an assembled context; a transient segment is demonstrably absent from every persisted store. A verifier that cannot fail is theater with an exit code.
9. Deliver the design in the Output Format, seams cited: poisoning review, session-start gate, cost policy, retrieval authz, disclosure mechanics — composed, never restated.

Output Format

MODEL CONTEXT DESIGN — <feature/agent>
Principle: the context window is a supply chain — an unverified input is an
  unverified output; a curated diet, not open access.
Segments: <name — source — trust class — schema — cap — priority —
  PERSISTED|TRANSIENT>
Assembly: <server-side composer; order; what happens on over-cap (designed
  degradation); no open-access paths>
Input schemas: <per segment: shape/size/content class; rejection behavior>
Minimization: <secret/PII/raw-payload decisions: value vs reference vs
  derivative; the transient channel and its guarantees>
Reconstructibility: <per segment: content | reference+version | class+hash;
  the honest limit on transient segments>
Exclusions (designed): <what is NOT in the window — each with reason>
Failure proofs: <over-cap, schema-reject, exclusion-absence,
  transient-not-persisted — the test for each>
Handoffs: poisoning attack review → memory-context-poisoning-reviewer;
  session-start context → agent-startup-context-gate; spend caps →
  ai-cost-guardrail-designer; retrieval authz → rag-security-architect;
  redaction/echo mechanics → sensitive-disclosure-guard; call gating →
  agent-harness-architect
Open questions / risks: <each with risk-if-wrong / who answers>

Validation Checklist

• Every segment is named with source, trust class, schema, cap, priority, and an explicit PERSISTED or TRANSIENT mark.
• Assembly is server-side from vetted parts; no path gives the model open access to a data source in place of curated content.
• Hard caps exist per segment and in total, with deterministic, designed degradation — never "whatever fit".
• Every input passes a closed schema before entering the window; schema failures are rejected, not admitted.
• Secrets/PII/raw payloads are minimized first; what rides is on the transient never-persisted channel; the split is explicit.
• What the model saw is reconstructible afterward, with the transient limitation stated honestly (class/shape/hash, never stored content).
• Exclusions are designed and documented with reasons — nothing is absent (or present) by accident.
• Every cap, schema, exclusion, and transient guarantee has a designed proof it can fail.
• The yields are stated: poisoning review → memory-context-poisoning-reviewer; neighboring lanes cited, not restated.

Gotchas

• "Give the model everything so it has context" is the anti-pattern this skill exists to replace: open access is not context engineering, it is the absence of it — and every unverified byte admitted is an unverified byte in the answer.
• Prompt logs are where transient data goes to become permanent: the never-persisted channel must be checked against EVERY store — traces, analytics, conversation history, error reports — not just the obvious log.
• A cap without a designed priority order silently drops the most important segment on the worst day; "truncate from the end" is a decision, make it deliberately.
• Reconstructibility promised as "we log the full prompt" contradicts the transient channel the moment a secret rides along — the honest design records class+hash for transient segments and says so.
• History is a segment too: unbounded conversation history is the cap violation everyone forgets, and yesterday's unvetted content re-enters today's window through it.
• An exclusion nobody wrote down gets "fixed" by the next engineer who notices the model lacks the data — designed exclusions carry their reason precisely so helpfulness doesn't reverse them.
• Schema-valid is not trust: a closed schema bounds shape and class, it does not make retrieved content's INSTRUCTIONS safe — injected instructions in admitted content are prompt-injection-defender's lane; say so, don't solve it here.

Stop Conditions

• A verbatim-reconstruction requirement and a never-persist requirement collide on the same segment (compliance wants the exact content, policy forbids storing it) → surface the conflict with the honest options (reference+version, class+hash, scoped retention with its own controls) and stop for a human decision; do not promise both absolutes.
• The context sources themselves are unknown — nobody can name what feeds the window today → inventory first; a curation design over an unknown supply chain is fiction.
• The request is to widen the diet to open access (a live connection to a store in place of curated segments) → refuse the open-access design, present the curated alternative, and escalate if the requirement survives.
• Wiring the design into a live pipeline (changing what production prompts contain) → implementation under the repo's approval path; this skill designs, it does not deploy.
• The real concern is poisoning of stored memory/context, retrieval authorization, or redaction mechanics → hand to memory-context-poisoning-reviewer / rag-security-architect / sensitive-disclosure-guard and stop.

Supporting Files

• evals/evals.json — behavior cases: the ad-hoc context-stuffing design, the secrets-in-prompt-logs split, the reconstruction-vs-never-persist edge, the open-access refusal, and the poisoning-review should-not-trigger.
• evals/trigger-evals.json — discrimination against agent-startup-context-gate (session-start vs per-call runtime), ai-cost-guardrail-designer (cap price vs content), rag-security-architect (retrieval authz vs curation), sensitive-disclosure-guard (redaction mechanics), agent-harness-architect (in-batch: call gating vs context content), and the design-vs-review seam against memory-context-poisoning-reviewer.
• No references/ — the context contract above is the complete procedure; detail lives in the produced design artifact.