본문으로 건너뛰기
cccskills
무료GitHub에 공개

dag-context-bridger

Builds scoped, provenance-preserving context packets between DAG nodes. NOT for execution or aggregation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(2)

  • SKILL.md5.2 KB
  • references/provenance-context-and-loss-audit.md5.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

DAG context bridger

Build a context packet for one downstream task, preserving what it is, where it came from, what authority permits, and what was omitted. Compression saves tokens; it cannot make a source true, authorize disclosure, or repair a missing prerequisite.

1. Bound the packet

State the downstream question, artifact/version requirements, recipient authority, retention/redaction policy, token or byte budget, and non-negotiable obligations such as counterexamples, caveats, approval constraints, and exact quotations. Assign each fragment a stable ID, source URI or artifact digest, time/version, producer activity, responsible agent, disclosure class, and derivation relation. PROV-O supplies useful entity/activity/agent and derivation vocabulary; it does not prove truth or authorization.

flowchart LR
  I[Scoped request and recipient authority] --> F[Filter disclosure and retention]
  F --> S[Select provenance-bound fragments]
  S --> O[Check obligation coverage]
  O --> C[Compress with source links]
  C --> P[Packet, omissions, and loss audit]

2. Retrieve only eligible evidence

Filter authority before ranking. For a hybrid search, retain lexical and dense lists only when dense vectors have compatible spaceId; fuse ranks with RRF only after that gate. RRF's sum 1/(k + rank) is a rank vote, not relevance probability. Include dependency and provenance rankings when they illuminate input lineage, but make their role visible.

flowchart TB
  A[Authorized corpus] --> L[Lexical retrieval]
  A --> D[Dense retrieval with compatible spaceId]
  A --> G[Dependency and provenance candidates]
  L --> R[RRF or recorded selection rule]
  D --> R
  G --> R
  R --> V[Obligation and contradiction coverage]
  V --> K[Context packet with source IDs]

3. Choose a packet mode and preserve evidence through compression

Choose full-forward, selective plus summary, output-only, progressive loading, or hierarchical summaries according to recipient need and authority. Use the manifest procedure in the reference to calculate the recipient tokenizer budget plus response reserve; fixed token thresholds are not portable. Cache keys include artifact/version, recipient scope, model/tokenizer, policy revision, and summary method. Expiry, contradiction, or policy change invalidates the cache and holds work that depends on the invalidated packet.

Use extractive snippets for exact claims, constraints, numbers, and caveats that cannot safely be paraphrased; use generated summaries only with links to their source fragments. A budget is task-specific: do not apply fixed token cutoffs or a generic relevance threshold. Deduplicate only after comparing provenance and version: equal text can carry different authority or time meaning. Record each omitted fragment and whether it was excluded for authority, budget, irrelevance, incompatibility, or uncertainty.

Worked positive case

A reviewer needs a deployment recommendation. The packet includes the signed approval record, the tested artifact digest, a source caveat, and an extract of the failed negative test. A duplicate prose summary is omitted with its source IDs. The output says the packet is decision support, not an approval itself.

Worked negative case

Two fragments say the same thing, but one is from an expired policy revision. Do not deduplicate them as interchangeable. Retain the current fragment, report the stale one and its version, and avoid silently presenting the claim as timeless.

4. Audit and hand off

Before sending, check every stated obligation against included fragment IDs. Return the recipient/scope, source and derivation records, filters, compatible vector-space evidence, summary method, omissions, unresolved contradictions, and a request for additional evidence where coverage fails. Read provenance context and loss audit. Cite W3C PROV-O for provenance concepts and Cormack et al., 2009 for rank fusion. Neither source guarantees safe disclosure, correctness, or adequate context.

Scope and handoff checks

This is a completed-node, single-DAG information handoff. It does not spawn agents, combine final results, alter topology, implement real-time streaming, or establish durable cross-run memory. Route those needs to the executor, result aggregator, graph builder or storage owner. Before release, verify recipient scope, required-obligation coverage, measured packet budget, source version/lineage, omission reasons, contradiction status and recipient readback. The reference gives cache/cycle recovery and a concrete packet worksheet.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/port-daddy32026년 10월 19일 업데이트

curiositech의 스킬 모두 보기

이 스킬의 문제 신고