Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.
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。
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概要と使いどころ
Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.
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。
Use when adapting academic findings for a public audience (op-ed, social post, podcast) and the simplification must stay evidence-faithful, avoid overclaiming, respect embargoes, and disclose AI assistance.
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Use when authorship order, CRediT contribution roles, or AI-assistance attribution must be recorded transparently for a manuscript, when an author dispute needs an evidence trail, or before a contributorship statement is required at submission; this skill maintains the human-attribution ledger of who contributed what and in which role (authorship order, CRediT taxonomy, revision responsibilities), which is distinct from the disclosure-metadata audit owned by ai-disclosure-auditor.
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。
Use when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.
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。
Use before a tag, GitHub release, or Zenodo archive, when README, CATALOG, CHANGELOG, CITATION.cff, the disclosure aggregate, and skill discovery must agree, or when a go or no-go release verdict needs recorded evidence behind it.
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Use when reviewer comments arrive and each one needs a tracked decision, when a revise and resubmit letter must map every comment to a manuscript change, or when contradictory reviewer demands need evidence-based, editor-facing framing.
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Anti-forensics and evidence destruction techniques for red team operators conducting authorized engagements. Covers log clearing on Windows (wevtutil, Clear-EventLog, ETW provider patching) and Linux (journal truncation, utmp/wtmp binary editing, syslog manipulation), timestamp manipulation via Timestomp and SetMACE to defeat timeline analysis, filesystem-level anti-forensics including NTFS Alternate Data Streams for payload hiding and secure deletion with sdelete/shred, memory artifact removal to counter live forensics, disk artifact manipulation targeting MFT entries and USN journal records, network forensics evasion through encrypted C2 channels and DNS-over-HTTPS tunneling, and anti-VM/sandbox detection to avoid dynamic analysis environments. Tools: Timestomp, wevtutil, sdelete, shred, MimiPenguin, Invoke-Phant0m. Aligns to MITRE ATT&CK T1070 (Indicator Removal), T1027 (Obfuscated Files or Information), T1497 (Virtualization/Sandbox Evasion). Each technique includes the forensic artifact it targets, t...
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。
Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.
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。
Audit whether a "current product focus receipt" (Decision/Now/Why-now/Evidence/Not-now/Cut-suspend/First-visible-proof/ Acceptance-gate/Kill-revisit-trigger/Owner/Review-date) and the work order it gates state a real, testable decision and a real agent launch — not planning-placeholder prose that sounds like an assignment but can't be proven. Use when deciding whether to launch an agent chain against a milestone or a stated "focus," when a work order lacks explicit input/output/owner/proof-gate, or when an acceptance gate would only prove a cached UI state instead of daemon truth. NOT for distinguishing roadmap items from energy-driven sidequests (legible-roadmap-with-sidequests), reviewing a PRD/prototype for product-completeness gaps like cold start or pricing (product-reality-reviewer), or accepting already-finished work against a personal acceptance bar (port-daddy-user-surrogate-pm-review).
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Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).
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Classify every destructive/gated action an agent body can attempt (git, filesystem, network, shell, github) into block/approve/allow tiers, and audit whether the pre-tool and post-tool governance backing that classification is real: blocked actions proven zero-side-effect by a negative fixture, every denial backed by a receipt and a transcript event, every block-tier denial paired with a safe alternative, and no unmanaged or same-UID body ever marked "contained." Use when building or reviewing Agent Harbor's C5 governance gate (destructive git blocker, approval request/result, denial receipts), gating a new tool surface before it can run destructive commands, or auditing whether an existing "destructive-action blocked" claim actually has evidence behind it. NOT for proving an existing sandbox boundary actually contains an adversary once an action is isolated (use sandboxed-adversarial-test-harness), deciding where in a DAG to place a human review node or designing the approval UX itself (use human-gate-designer), or designing the general work-receipt schema across an entire agent task (use agent-work-receipt-designer).
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Threat-models protocols where authority, scarce resources, bonds, bounties, escrow, reputation, ordering, adjudication, or settlement create strategic incentives. Use to test attack utility, conservation, collusion, Sybil resistance, oracle capture, griefing, custody, liquidity, and residual risk. NOT for choosing payment or bond amounts without evidence, smart-contract code audit, legal or employment classification, DeFi trading, admitting workers, or settling claims.
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。
Ranks eligible skill candidates with explicit retrieval evidence, capability coverage, and uncertainty. NOT for discovery, authorization, or execution planning.
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Validates versioned permission requests against a grant scope and records comparison evidence. Activate on 'validate permissions', 'permission check', 'inheritance validation', 'permission matrix', 'security validation'. NOT for runtime enforcement (use dag-scope-enforcer) or isolation management (use dag-isolation-manager).
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。
The intelligence layer of Jury-rig. Decomposes natural language tasks into proposed Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, proposes wave and revision contracts, and expands nodes only when evidence supports a revised graph. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.
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Design and validate versioned DAGs with typed dependencies, gates, and evidence-bound readiness. NOT for executing a DAG, granting work authority, or treating an unverified gate as cleared.
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Manages agent isolation levels and resource boundaries. Specifies concrete controls and the evidence needed to assess their boundaries. Activate on 'isolation level', 'agent isolation', 'resource boundaries', 'sandboxing', 'agent containment'. NOT for permission validation (use dag-permission-validator) or runtime enforcement (use dag-scope-enforcer).
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Validates agent outputs against expected schemas and quality criteria. Reports structural and content checks within the declared evidence scope. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).
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。
Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).
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Design bounded applications and workflows for an always-on AI agent with episodic memory. Covers product ideas, persistence-dependent workflows, and constructed examples that make delegation, interruption, evidence, and opt-out explicit. Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).
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。
Author a pull request an AI coding agent can actually get merged: a scoped, coherent diff; a Summary and Test Plan backed by real evidence; correct triage of required-and-blocking CI gates versus external/advisory checks (e.g. a Cloudflare Pages preview build); draft-while-WIP discipline; named fixup commits for real review findings; and a clean landing through a merge queue without force-pushing or bypassing branch protection. Use when opening, updating, triaging red CI on, or landing an agent-authored GitHub PR. NOT for this repo's internal release ceremony, actor embodiments, or contributor mirror-sync mechanics (use port-daddy-internal-dev), tracking backlog/issue lifecycle across a board (use agent-issue-tracker-workflow), or deciding what to build next / sequencing a roadmap (use legible-roadmap-with-sidequests).
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。
Research AI coding assistant products, audiences, user stories, social proof, hacks, and unmet needs for apps like Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf, GitHub Copilot, Cline, Aider, and homegrown agent stacks. Use when designing or positioning agentic developer tools, Port Daddy Agent Harbor, swarm consoles, background coding agents, or competitive/product research for AI software engineering. NOT for implementing the coding agent runtime, generic market research, or UX mockups without developer workflow evidence.
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。
Safety-case design for always-on agents with episodic memory. Covers data hygiene, privacy and security risk surfaces, cost controls, scope, user dependency concerns, retention, incident containment, and responsible deployment evidence. It identifies hazards and asks for named controls, tests, owners, and residual uncertainty; it does not provide legal, clinical, youth-safety, or jurisdictional determinations. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).
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。
Design or audit an agent identity, credential, session, continuity, and reputation plan. Use when attributed outcomes must survive key rotation, restart, recovery, or review; when choosing Elo, TrueSkill, a contextual bandit, or an LLM judge; and when making Sybil, newcomer, calibration, collusion, and evidence limits explicit. Produces a supplied-plan consistency audit, never proof of observed delivery, cryptographic identity, or live enforcement. NOT for proving personhood, global Sybil resistance, or an unobserved delivery.
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。