context-codebase

name	context-codebase
description	Project context engine for repo orientation, cached handoff, and task-focused code retrieval.

context-codebase

Project context engine for fast repo orientation, cached handoff, and task-focused code retrieval.

Artifacts:

• Snapshot: {project}/repo/progress/context-codebase.json
• Index state: {project}/repo/progress/context-codebase.index.json
• SQLite FTS5 index: {project}/repo/progress/context-codebase.db

When To Use

Use this skill when you need to:

• understand a codebase quickly
• refresh a reusable project snapshot
• retrieve focused files and snippets for a concrete question
• prepare a deep technical walkthrough from a cached project context
• fuzzy-search symbols by name (IDE-like Ctrl+P / Go to Symbol)
• identify change hotspots and blame history via Git integration

Modes

/context-codebase

Default entry.

Behavior:

• Generate a new snapshot when none exists
• Reuse the cached snapshot when the source fingerprint is unchanged
• Return a project overview optimized for fast model understanding

Use it when:

• entering a project for the first time
• switching to a new model or IDE
• you want a general repo overview

/context-codebase refresh

Incrementally update the index and cache metadata.

Behavior:

• Recompute the source fingerprint and compare it with the cached artifacts
• Reuse the cached artifacts when nothing changed
• Update the index incrementally when sources changed
• Keep the existing snapshot structure and only refresh metadata needed by the cache contract
• Can be combined with read / report to refresh before answering

Use it when:

• the repo changed and you want the cache to catch up
• you want fresh context without forcing unnecessary rebuild work
• you want read / report to consume the freshest snapshot first

/context-codebase read

Focused retrieval mode.

Behavior:

• Consume the existing snapshot and index
• Skip forced regeneration logic
• Return a retrieval-oriented payload with:
  • files
  • snippets
  • flowAnchors
  • nextHops
  • searchScope
  • hotspots
  • externalContext

Use it when:

• the snapshot already exists
• you want fast file and snippet retrieval for a specific question
• you want to preserve tokens and avoid rescanning

Host requirements:

• Explicitly say read mode is consuming the existing snapshot and index
• Do not imply the repo is being rescanned
• Start from files, snippets, nextHops, and searchScope
• Do not jump to repo-wide search first

Preferred opening:

• 我将直接读取已有快照和索引，不重新扫描仓库。
• 我会先用 read payload 里的 files、snippets 和 nextHops 做定点读取，不先做全仓搜索。

Avoid:

• 我先重新加载项目上下文。
• 我先重新扫描仓库。

Read answer contract:

• Treat read as a quick implementation summary, not a deep technical report
• Use this order when the payload has enough evidence:
  1. one-sentence conclusion
  2. call entry
  3. core implementation files
  4. key anchors
  5. one-sentence implementation summary
• Prefer 3-4 core files and 3-5 anchors
• Prioritize entry points and execution flow before type definitions or tables
• Stop once the next model can continue reading code efficiently
• Leave exhaustive tracing, edge cases, and broader architecture to report

/context-codebase report

Deep-analysis mode.

Behavior:

• Consume the existing snapshot and index when present
• Generate a snapshot first only when missing
• Return a deep-pack for host-side deep report generation
• Treat deep-pack generation as the default stopping point for the parent thread

Use it when:

• you want a full technical report
• you want a complete call chain or architecture trace
• you want to preserve the parent thread token budget

Host requirements:

• Explicitly say report mode is consuming the existing snapshot and index
• Mention snapshot generation only if the snapshot is actually missing
• Prefer delegating deep-pack to a subagent or delegated worker
• Do not continue parent-thread Read / Search expansion after the pack is generated unless delegation is unavailable

Preferred opening:

• 我将基于已有快照和索引生成 deep-pack，不重新扫描仓库。
• 如果宿主支持 subagent，我会优先把 deep-pack 交给子任务处理。
• 在可委派的情况下，我会停在 deep-pack 边界，不在主线程继续展开长报告。

Avoid:

• 我先重新分析整个仓库再写报告。
• deep-pack 已生成，现在我继续在主线程读取关键文件并输出完整长报告。

Execution Semantics

The entrypoint is always scripts/generate.py, but the mode determines whether source code is scanned or cached artifacts are consumed:

• default mode: may generate a snapshot or reuse a cached one
• refresh: incrementally updates the index when sources changed and keeps the existing snapshot structure
• read: consumes the existing snapshot and index to build a retrieval payload
• report: consumes the existing snapshot and index to build a deep-pack

Important clarifications:

• Seeing python ... generate.py ... --read or --report does not mean the repo is being rescanned
• Seeing refresh means "incrementally update the index if needed", not "force rebuild"
• freshness.reason inside a read or report payload describes how the current snapshot was produced previously; it does not mean the current invocation regenerated the snapshot
• git.status=dirty means the worktree has uncommitted changes; it does not automatically prove that the snapshot fingerprint changed

Retrieval Model

The retrieval pipeline uses FTS5 BM25 keyword search combined with graph-aware expansion and importance boosting:

• BM25 keyword (FTS5 SQLite) — lexical precision for exact matches
• Graph expansion (dependency graph neighbors) — structural context around high-scoring chunks
• Important-file boosting — prioritizes key configuration and entry-point files
• Recent-change boosting — boosts recently modified files for bugfix/code-review tasks
• Fuzzy symbol search (FuzzySymbolSearcher) — IDE-style camelCase/snake_case fuzzy matching

Chunking

• Regex chunker — line-based chunking (60-line windows with anchor-point overlap), works for all languages.

Symbol Search

• FuzzySymbolSearcher — IDE-like Ctrl+P symbol lookup with camelCase and snake_case aware fuzzy matching. Filters by file path patterns.

Git Integration

• GitEnrichment (git_index.py) — annotates chunks with change frequency, hotspot score, churn metric, recent authors, and blame data. Results feed into recent-change-boost and importance-boost retrieval strategies.

Retrieval Workflow

For read:

1. Inspect files, snippets, flowAnchors, nextHops, and searchScope
2. Read suggested files directly when possible
3. Only widen to repo search if the payload is insufficient
4. Exclude repo/progress/, node_modules/, dist/, build/, and __pycache__/ when widening search

For report:

1. Inspect coreFiles, snippets, flowAnchors, and recommendedReportShape
2. If the host supports subagents, pass the deep-pack there
3. Stop at the pack boundary on the parent thread when delegation is available
4. Fall back to same-thread deep reporting only when delegation is unavailable

Query Guidance

For focused questions, prefer --task with a UTF-8 safe query channel.

⚠️ Cross-Lingual Search Limitation:

• If your internal reasoning or the user's prompt is in a non-English language (e.g., Chinese) but the codebase uses English identifiers, you MUST append English keyword translations to your query string.
• Why? BM25 uses literal FTS5 token matching, which yields 0 hits if lexical characters do not overlap.
• Example: Instead of --query "记忆模块", use --query "记忆模块 memory module".

On Windows or any environment where non-ASCII query text may become mojibake:

• Prefer --query-escaped <ascii_only_query>
• Then --query-file <utf8_file>
• Then --query-stdin
• Avoid raw --query for non-ASCII input when the shell is unreliable

CLI output contract:

• stdout is reserved for UTF-8 JSON payloads
• warnings and errors must go to stderr

Manual Script Usage

Replace {skill_dir} with the actual installed skill path. In this repository, that path is context-codebase/.

python {skill_dir}/scripts/generate.py <project_path>
python {skill_dir}/scripts/generate.py <project_path> refresh
python {skill_dir}/scripts/generate.py <project_path> --read
python {skill_dir}/scripts/generate.py <project_path> --read --refresh
python {skill_dir}/scripts/generate.py <project_path> --read --task feature-delivery --query "skill lifecycle runtime"
python {skill_dir}/scripts/generate.py <project_path> --read --task feature-delivery --query-escaped "\\u6280\\u80fd\\u7ba1\\u7406\\u5668\\u5982\\u4f55\\u5b9e\\u73b0"
python {skill_dir}/scripts/generate.py <project_path> --read --task feature-delivery --query-file query.txt
python {skill_dir}/scripts/generate.py <project_path> --report --task feature-delivery --query "skill download flow"
cat query.txt | python {skill_dir}/scripts/generate.py <project_path> --read --task feature-delivery --query-stdin

Windows-safe example:

python {skill_dir}/scripts/generate.py <project_path> --read --task feature-delivery --query-escaped "\\u6280\\u80fd\\u4e0b\\u8f7d\\u6d41\\u7a0b"

References

Read these only when needed:

• references/snapshot-schema.md
  • read when you need field-level snapshot or retrieval payload details
• references/response-contract.md
  • read when you need the full presentation contract for overview or read-mode outputs
• references/deep-pack.md
  • read when you need the report protocol, host delegation rules, or deep-pack fields

Core Rules

• Exclude generated/vendor noise such as .git, node_modules, dist, build, __pycache__, and repo/progress
• Avoid self-referential scans of the skill's own output
• Prefer cached artifacts over rescanning whenever the mode allows it
• Use read for focused retrieval and report for deep-pack generation
• Keep the parent thread lightweight in report mode when delegation exists

Boundaries

• Focus on repo orientation, snapshot maintenance, and targeted retrieval.
• Do not expand into broad implementation work unless another skill or agent explicitly takes over.
• Prefer cached context artifacts over unnecessary rescans.

Escalation Rules

Pause and ask the owner before:

• forcing a full refresh when cached artifacts are still adequate
• broadening retrieval into a repository-wide rewrite or redesign exercise
• delegating deep-pack work when the added token and coordination cost is not justified

Final Output Contract (MANDATORY)

Output style is mode-specific:

• read:

  • Answer the user's concrete code question directly.
  • Prefer a compact summary of the code location, call entry, core files, and implementation flow.
  • Do not append the explicit headings Skill Fit, Primary Deliverable, Execution Evidence, Risks / Open Questions, or Next Action in normal successful reads.
  • Only surface execution evidence or risks when they materially affect answer quality, such as cache staleness, fallback retrieval, low-confidence hits, or missing source coverage.

• report and refresh:

  • End with a visible structured closeout containing:
    1. Skill Fit - why context-codebase was the right retrieval path
    2. Primary Deliverable - snapshot/read/report artifact or answer package
    3. Execution Evidence - cache usage, files indexed, or retrieval sources
    4. Risks / Open Questions - stale cache risk, missing context, or unresolved ambiguity
    5. Next Action - the recommended follow-up retrieval or implementation step