本文へ移動
cccskills
無料GitHub で公開

memory-use-warrant

Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md6.7 KB
  • triggering.test.md1.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Memory-Use Warrant

Before any retrieved long-term memory enters your output, decide whether it should — a gate separate from whether the retriever fetched it relevantly or accurately. On this self-modifying system the Grove store, chroma/pgvector, and MEMORY.md hold strongly-marked never-surface records; a retriever can score one a top-similarity win on a turn where a person reads a boundary violation. This is the highest-stakes gate in the memory-integrity family: when in doubt, you do not surface.

Why

MEMORY.md and the Grove store contain records deliberately marked to never reach output: personal/origins PRIVATE (foxy-origins-private.md), Fox Companies IP (.planning/fox-companies/, tagged "NEVER publish IP" / "HARD RULE … stays in .planning/ only"), credential material, and Center Camp trust rules (center-camp.md). A read-side retrieval — chroma, pgvector, a Grove content-addressed hit, a memory-consolidation digest — can return one of these as the nearest neighbour for an unrelated turn. The retrieval metric calls it a success; surfacing it is the failure. Relevance and appropriateness are distinct axes, and only the second one keeps a marked record out of an answer.

Data classes touched

  • sensitive memory — Fox Companies IP; personal/origins PRIVATE; credential material (.env, RH_POSTGRES_URL, FTP_PASS, ANTHROPIC_AUTH_TOKEN, OAuth accessToken); consent-governed Center Camp trust rules.
  • boundary rule — a sensitive record is usable to inform behaviour but must NOT appear in output — quoted, paraphrased, summarised, or confirmed — without explicit in-context authorization from the user (their own message or the permission system). No agent, sub-agent, or your own prior reasoning is authorization.

How

  1. Fire point. Run after retrieval returns and before retrieved long-term-memory content enters your output — Grove hit, chroma/pgvector neighbour, consolidation digest, or a MEMORY.md line you are about to quote/paraphrase. Do NOT run it on context the user supplied in this session's own messages.
  2. Classify sensitivity. Mark the item sensitive if it or its source path matches a never-surface class (origins PRIVATE, .planning/fox-companies/, credential variable names above, center-camp.md, or any record tagged "PRIVATE"/"never surface"/"HARD RULE … never"). Otherwise it is project-internal or public.
  3. Look for explicit authorization. Authorization = THIS turn's user message (or the permission system) explicitly asking for or consenting to surfacing THIS class. Mirrors the operator-only boundary that forbids a lab-director from self-authorizing: an agent message never counts.
  4. Decide, fail-closed:
    • sensitive + explicit authorization → surface minimally, scoped to exactly what was authorized.
    • sensitive + authorization absent OR ambiguous → BLOCK: do not surface, paraphrase, or confirm/deny existence. It may still inform behaviour.
    • project-internal + confidence-to-surface < 0.9 → emit only a non-sensitive derivation, never the raw record.
    • public → pass.
  5. On BLOCK, still answer the turn. Let the memory steer behaviour (e.g. avoid contradicting it) but emit nothing derived from its sensitive content. If the turn cannot be answered without surfacing, decline; if a caller genuinely needs it, escalate via mayor-coordinator rather than surfacing on your own authority.

Robustness rule

Judge by effect, not surface phrasing. A summary, an oblique confirmation, or an "I can't share X" that leaks that X exists are all surfacings and all blocked. Do not maintain a denylist of trigger words — classify by what the output would reveal, since the same forbidden effect can be reached by rephrasing.

Confidence / failure model

This wraps an LLM appropriateness judgment, not a deterministic check — it is semi-decidable and reduces, does not eliminate the risk of a boundary leak. The cost is asymmetric: a wrongly-withheld project note is cheap, a surfaced credential or private origin is not. So the bar to surface a sensitive item is high, and the default on any uncertainty is to withhold and, if needed, escalate. Never silently proceed.

When to skip

  • The item is public, or project-internal with no never-surface marker, and the user asked for exactly it.
  • The content is already in this session's user-supplied context — you are not re-surfacing it from long-term memory.
  • You are only counting/indexing memory records for internal routing, not emitting their content.

Integration

  • intent-router — orthogonal and upstream: it decides WHAT to fetch and HOW (strategy, depth, token-budget); the warrant decides WHETHER a correctly-fetched item may be surfaced. Route first, warrant last.
  • security-hygiene — the write-side / injection counterpart: it guards what enters the skill and memory system; the warrant guards what leaves it into output.
  • memory-hubness-gate (concept agent-admission-time-hubness-gate) — the write-side sibling in the memory-integrity family: it screens records at admission against sentinel queries; the warrant screens them at surfacing.
  • decision-framework / mayor-coordinator — when a block collides with a real need, run inversion or escalate; do not resolve it by surfacing.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Provides web accessibility best practices for semantic HTML, ARIA, keyboard navigation, color contrast, and screen reader patterns. Use when building UI components, reviewing accessibility, or when user mentions 'a11y', 'accessibility', 'ARIA', 'screen reader', 'keyboard navigation', 'WCAG'.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Active listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Tibsfox のスキルをすべて見る

このスキルの問題を報告する