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ccg

Fast read-only code discovery with a bounded code-context-graph search and targeted source verification. Use when an ordinary positive lookup or explanation needs an entry point, recorded intent, known-path inventory, or direct relationship evidence. Do not use for absence, completeness, exhaustive inventory, deep flow or impact analysis, or graph writes; use ccg-search-verify for defensible negative claims, and require explicit invocation for ccg-analyze or ccg-build.

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tae2089/code-context-graph스타 17(GitHub 17 + cccskills 0)

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  • SKILL.md7.5 KB
  • references/search-execution.md8.0 KB
  • references/supported-languages.md645 B

SKILL.md(원문)

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ccg — Fast Search

Find the smallest source-verified evidence set that answers an ordinary positive code question.

Route

RequestRoute
Source path already knownTargeted Grep + Read
Source path unknown, including an exact identifierCore Loop
Known file or folder inventoryOne describe call
One direct caller/callee factOne bounded query_graph call
Absence, completeness, or exhaustive inventoryccg-search-verify
Deep flow or impact analysisRequire explicit ccg-analyze invocation
Graph write or refreshRequire explicit ccg-build invocation

An ordinary miss is not evidence that code does not exist. Route any negative or complete claim to ccg-search-verify.

Core Loop

  1. Search first when the path is unknown. Run one initial structured CCG search with limit: 5. Use an exact identifier, literal, or error text as a compact query. If the request already contains one focused code question, use it verbatim after removing only command wrappers or output instructions. Do not compress a behavior or reason question into keywords. Otherwise ask one focused natural-language question in the repository's vocabulary. CCG tries the precise match first, then uses OR matching with BM25/IDF, rewards more distinct terms, and rejects single-term coincidences. Choose the structured surface from available routing evidence: use MCP when repository instructions provide explicit MCP or server-visible routing; otherwise, when local ccg and a repository-local .ccg.yaml exist, use JSON CLI with ccg search --json --compact --limit 5 "<query>"; use MCP when no usable local configuration exists. Pass compact: true to MCP search. Compact mode keeps paths, declaration bounds, evidence, and continuations while omitting redundant storage fields. MCP tool availability or MCP documentation alone is not routing evidence; explicit routing names the target namespace together with MCP or a server-visible repository path. JSON CLI reads its namespace and database from .ccg.yaml. For MCP, when configuration supplies a namespace, include it in the initial search arguments and every continuation. When a namespace must be extracted, read only the namespace: field, never the full configuration.
  2. Choose evidence from the whole page. Compare the returned file paths, matched signals, reasons, and declaration hits. Start with the production hit that most directly addresses the question, not automatically the first row. Read each chosen declaration's exact range separately; do not span unrelated hits by reading from a file's earliest result to its latest. Do not re-locate a hit with grep when start_line and end_line are present. Use a targeted in-file locator only for missing bounds or a helper named by the verified declaration. For a production-behavior question, do not read tests to corroborate production behavior already established by current source. Tests become evidence only when the user asks about them or the production source leaves a material ambiguity.
  3. Stop by claim sufficiency. Before another tool call, name the specific evidence gap in the user's question and state that gap in one sentence. If no material evidence gap can be named, answer. A verified current-source call site plus the invoked declaration or contract establishes that mechanism; do not trace constructor or dependency-injection wiring unless the user asks which runtime implementation or configuration is selected. Use these completion rules:
    • Where: the current-source declaration is enough.
    • How/what: the branch or contract directly implementing the requested behavior is enough; include public input or output only when asked.
    • Why: one directly relevant author-recorded design reason plus current source confirming its mechanism closes the rationale gap unless the user requests several reasons; secondary consequences are optional. Do not trace downstream work merely to prove optional consequences.
    • One relationship: the edge and the endpoint declaration needed to interpret it are enough. Mandatory stop: when the matching condition is satisfied, the next action must be the final answer. Another tool call is allowed only when current source contradicts the recorded evidence or the user explicitly requested additional distinct reasons or details.
  4. Follow a new exact clue before semantic paging. When verified source reveals an identifier or literal that names a remaining usage, caller, or exposure gap, run a path-only reference lookup such as rg -l before semantic paging. Print file names only, exclude test-like paths unless relevant, then read the necessary production declarations. Do not create a new evidence gap from identifiers encountered after the original question is already answered. Never print repository-wide match context.
  5. Page only for an unresolved semantic gap. A truncation flag makes next available; it does not require paging. If the current page and source-derived exact clues cannot close the named gap, follow the returned next call verbatim. Preserve query, limit, namespace, and offsets. Use at most three next calls.
  6. Handle a miss once. An exact-identifier miss goes directly to a path-only source lookup. For a natural-language miss, retry local JSON CLI once only when it demonstrably uses a different graph or runtime than MCP. Do not repeat the same local graph query through another interface. If direct source search yields no new clue, stop without making a negative claim.

Conditional Details

Do not load a reference merely because declaration bounds are missing; Step 2's targeted in-file locator is the complete recovery rule. Read references/search-execution.md only for language translation, uncertain test classification, or ambiguous continuation metadata. Read references/supported-languages.md only for a language-support question.

Boundaries

  • Current source is authoritative for location and runtime semantics; CCG ranks entry points, intent, and relationships.
  • Do not call get_minimal_context, namespace-list, or graph-stat tools when the namespace is already configured.
  • After starting Core Loop search, do not add query_graph, get_node, flow, or impact calls merely to enrich an already supported answer.
  • This workflow is read-only. Never build, update, migrate, or postprocess. Do not invoke ccg-analyze automatically; deeper analysis requires explicit ccg-analyze invocation.

Completion

Return the answer with relevant paths or qualified symbols. Mention truncation only when the continuation cap limits the answer. Do not append namespace, freshness, or call-count reports unless requested.

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같은 저장소의 스킬

Explicit-only deep analysis of algorithms, feature pipelines, and code relationships with CCG impact radius, bounded flow tracing, callers/callees, git-diff risk, affected stored flows, and cross-namespace references. Use only when the user explicitly names the ccg-analyze skill in the current request. Do not invoke merely because a task asks about a flow, pipeline, impact, caller, or relationship.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

Author, refine, and verify CCG annotations such as @intent, @domainRule, @sideEffect, @mutates, @index, and @see. Use when adding business meaning to code, improving annotation-aware code or documentation retrieval, fixing annotation lint findings, checking supported tag syntax, or documenting operational contracts. Do not use for generated Markdown editing or annotations that merely restate symbol names.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

code-context-graph — annotation system. AI-driven annotation workflow, tag reference, and annotation search.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

ccg-build

무료

Explicit-only graph ingestion for building, updating, migrating, and postprocessing code-context-graph graphs from source visible to the CCG runtime. Use only when the user explicitly names the ccg-build skill in the current request. Do not invoke merely because a graph is missing or stale, annotations need reindexing, or another workflow would benefit from a refresh.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

ccg-docs

무료

Generate, discover, read, and lint CCG documentation. Use when producing Markdown and Wiki snapshots, narrowing broad module questions with search, reading generated docs from configured content roots with get_doc_content, or diagnosing orphan, missing, stale, incomplete, contradiction, dead-ref, and drift findings. Do not use for direct source annotation authoring or exact call-graph analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

ccg-docs

무료

code-context-graph — documentation generation, RAG indexing, and docs quality linting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tae2089/code-context-graph172026년 8월 22일 업데이트

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