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ccg-analyze

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.

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

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ccg-analyze — Code Analysis

Graph-based analysis for change impact, call flow, and recent-change risk.

Invocation Gate

Proceed only when the user explicitly names ccg-analyze in the current request. A request about an algorithm, pipeline, impact, caller, or relationship is not by itself an invocation. Without that explicit name, do not load this workflow or run its traversal and impact-analysis tools; use the bounded ccg discovery workflow instead.

Intent → Tool Mapping

User intentToolNotes
"Impact of changing this function?"get_impact_radius (depth=3, max_depth=3)Raise both bounds to widen
"How does this algorithm or feature pipeline work?"Pipeline workflow belowGraph-first, then source-verified
"Trace call flow from this function"trace_flowIf unexpectedly thin, verify the causes below; cross_namespace: true continues into referenced namespaces
"Who calls this function?"query_graph (callers_of)
"What does this function call?"query_graph (callees_of)
"Risk of this change"detect_changes + get_affected_flowsgit diff-based
"Which repos depend on this one?"list_cross_refs (direction inbound)Annotation ccg:// refs, materialized; direction also takes outbound/both, plus a status filter
"Impact across repos?"get_impact_radius with cross_namespace: trueCrosses resolved ccg:// refs both ways

Pipeline Analysis Workflow

  1. Candidate discovery: use search for both cases. When you cannot name the symbol yet, phrase the query as a question — it is scored against recorded @intent/@domainRule reasons as well as names, and every hit carries a node_id to walk from. Name the symbol once you can. A hit carries its own evidence — the matched signals and the node's @intent — so pick entry points from that rather than from position in the list; a short list means few justifiable files, and weak_filtered counts what was cut. Hits come grouped by file and a shown file is shown whole, so limit counts files. When truncated is true more files answered than this page reached, and the response's next field holds the exact calls — usually the same search at a higher offset; make those instead of re-searching with a larger limit. pool_truncated is a second, separate signal: true means the page ended at the edge of the candidates that were fetched, so page on even when truncated is false. Only truncated: false together with pool_truncated: false means the search is complete. When a question-shaped query comes back empty, read annotation_coverage before concluding anything: with_reason: 0 means nobody recorded a reason in what you searched, so the empty answer says nothing about whether the code exists — annotate the area and ask again rather than rephrasing.
  2. Symbol identity: confirm each entry point or major stage with get_node; continue with qualified names rather than display labels.
  3. Relationship and structure evidence: use query_graph with callers_of/callees_of for direct call relations, and describe on a file or folder path for what is written there. This evidence does not prove runtime order.
  4. Call-chain evidence: use trace_flow from a verified entry point and inspect truncation plus fallback-edge metadata. Treat the result as a bounded static chain, not a runtime trace.
  5. Runtime semantics: read the narrowed source files to verify branch conditions, loops, callbacks, data transformations, error paths, and actual ordering. If graph and source disagree, report staleness, unresolved dynamic behavior, or the remaining uncertainty instead of silently choosing one.

Thin trace_flow Results

One or two returned nodes do not prove an interface-dispatch failure. The start may be a real leaf, the selected namespace or qualified name may be wrong, the graph may be stale, strict edges may be sparse, or dynamic dispatch may be unresolved.

Verify in this order:

1. Confirm the exact node with get_node.
2. Confirm namespace population and graph freshness.
3. Compare query_graph callers_of/callees_of with and without fallback calls.
4. Read the relevant source around unresolved interface or dynamic calls.

Report which explanation is supported; do not label a thin trace as an interface boundary without source or edge evidence.

get_impact_radius Bounds

  • depth requests the BFS hop count; its default is 1.
  • max_depth caps depth; its default is 3. Setting only depth=5 still returns at most three hops, so raise both values when widening.
  • Start with depth=3, max_depth=3, and a bounded max_nodes.
  • Inspect response metadata truncated, max_depth, max_nodes, and returned_nodes before interpreting the radius as complete.

If results are huge, narrow by namespace, starting symbol, depth, or edge mode before concluding the implementation change itself is too broad. High-fanout entry points can legitimately have a large radius.

Analysis Result Bounds

Use the ccg skill's one-query discovery budget only for the initial entry-point search. Once this explicit analysis workflow is active, use the analysis-specific bounds below and read each additional source range needed to verify the selected stages. Preserve per-page namespace labels and errors when accumulating federated results.

get_impact_radius and trace_flow are bounded rather than paginated. Follow their truncated metadata by narrowing the start/scope or deliberately raising max_nodes; do not call a truncated response complete.

Accuracy Limits (use with awareness)

  • Interface calls may over-predict (expands to all implementations)
  • Dynamic dispatch (reflection, plugins) → not captured
  • Build-tag-split files → both registered (noise)
  • Fallback call edges improve recall but may add false positives; use strict mode when evidence quality matters more than coverage
  • Treat graph results as a static approximation; cross-check important conclusions against source.
  • repo_root for detect_changes and get_affected_flows must be a server-visible, allowed path. If the MCP server cannot see the client path, report that constraint and use local git/source evidence instead.

Boundary

  • Start from a verified qualified name; do not infer a symbol from a display label alone.
  • Scope namespace, path, traversal depth, and result limits before widening a query.
  • Separate strict call edges from fallback edges when evidence quality matters.
  • Do not treat missing graph edges as proof that runtime behavior is impossible.
  • Do not hide per-namespace errors or truncation when federated/cross-namespace evidence is partial.

Prerequisites

Use the ccg skill's Freshness Boundary before interpreting graph or stored-flow results. If refresh is required, report the gap; do not invoke ccg-build unless the user explicitly names it. The stored-flow tools — list_flows, get_affected_flows — require flow postprocessing; an empty flow list is not evidence of no flow until that state has been checked.

Completion

Report the analyzed qualified names, namespace, discovery query, query_graph patterns, trace_flow bounds and truncation, included edge modes, fallback-edge counts, source files used to verify runtime semantics, any graph/source disagreement or server-visible path limitation, and the evidence supporting important conclusions.

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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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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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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

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

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