ContextAtlas

Stop watching Claude burn tokens grepping for context it can't possibly find.

ContextAtlas turns your codebase into a single-call context bundle for Claude Code — fusing LSP-grade structure, architectural intent from your Architectural Decision Records (ADRs), git history, and test associations. Measured 45-72% token reduction with zero quality regression across benchmark axes on architectural prompts across the hono / httpx / cobra benchmark suite.

Quick start → · Benchmark results → · Why not graph-based? → · Architecture → · ADRs →

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ContextAtlas ships two equivalent paths — CLI and Claude Code Skills — both producing the same atlas.json. See Quick Start for setup.

The Problem

Claude Code currently learns your codebase by brute force. Every session starts fresh. Every "where is X?" triggers multiple grep calls. Every "what depends on Y?" is another flurry of file reads. On a mid-sized codebase, answering a single architectural question can consume 40+ tool calls and 100,000+ tokens before Claude has enough context to reason well.

Worse: the architectural intent that governs your code — the ADRs, the design decisions, the "we did it this way because" — is invisible to Claude. The rule that OrderProcessor must be idempotent lives in docs/adr/. When Claude proposes a change, it has no way to know that constraint exists.

Yesterday's understanding doesn't carry to today. Every conversation starts from zero. Your ADRs, your commit history, your test coverage — none of it is on the agent's table.

What if expensive understanding happened once, at index time, and every query became a dictionary lookup?

That's ContextAtlas.

What ContextAtlas Is

ContextAtlas is an MCP server that gives Claude Code a curated atlas of your codebase — fusing LSP-grade structural precision with architectural intent extracted from your ADRs, docs, and git history, delivered to Claude in single-call context bundles.

Every bundle Claude receives combines four independent signals about a symbol:

1. Structural data from the language server — definition, references, types, diagnostics. Compiler-grade precision.
2. Architectural intent from your ADRs, READMEs, and design docs — structured claims extracted by Opus 4.7 at index time, keyed to specific code symbols.
3. Historical context from git — recent commits touching the symbol, hot/cold indicators, co-change patterns.
4. Test associations — which tests reference the symbol, where coverage lives.

One MCP call returns all four, fused. No ADRs in your repo yet? You still get LSP + git + tests in one call instead of fifteen — a meaningful baseline improvement. Add ADRs and the bundles get richer. The architecture is designed so any subset of signals produces value.

Given an ADR stating that OrderProcessor must be idempotent, a call to get_symbol_context("OrderProcessor") returns:

SYM OrderProcessor@src/orders/processor.ts:42 class
  SIG class OrderProcessor extends BaseProcessor<Order>
  INTENT ADR-07 hard "must be idempotent"
    RATIONALE "All order processing must be safely retryable."
  INTENT ADR-12 soft "prefer async base class for new processors"
  REFS 23 [billing:14 admin:9]
    TOP ref:ts:src/billing/charges.ts:88
    TOP ref:ts:src/admin/orders.ts:12
  GIT hot last=2026-03-14
    RECENT "Fix idempotency bug in retry path" a3f2c1d
  TESTS src/orders/processor.test.ts (+11)

When Claude is asked to modify OrderProcessor, it sees the idempotency constraint before proposing changes — not after a user review catches the violation.

Who this is for. ContextAtlas is built for the average developer using Claude Code on real codebases — not just engineers at large orgs working on 500,000-file monorepos. Token-burn reduction scales with codebase size — dramatic on a 200-file framework, modest on a 30-file library. But architectural intent capture is size-invariant. A 30-file library can have meaningful architectural decisions worth surfacing, and Claude respecting them matters just as much as on a larger codebase.

Beyond tokens: a design-alignment case study

Efficiency and quality are necessary but not sufficient. The substantive value of context-grounding shows up in design choices on non-trivial code-change tasks.

A/B trial during v0.3 development. Identical 3-paragraph prompt across two ContextAtlas clones: implement a known bug fix — locate the bug, design and implement the fix, write tests, document via ADR. The only setup difference: MCP availability.

Arm	MCP	Approach selected
A (vanilla)	none	Recall-first approach (broader matching; fought the project's precision-thesis with noise)
B (CA-aided)	ContextAtlas	Precision-optimization approach (aligned with the project's pre-extracted-claims-with-structural-attribution thesis)

Arm B's approach landed in main. Both arms functionally fixed the bug at similar wall-clock and token cost. The substantive difference was alignment with project design thesis — the CA-aided arm could read the relevant ADR + prior architectural work from the atlas, and made a choice that fit. The vanilla arm couldn't see that context and chose an approach that worked but fought the architecture.

Arm A's substantive consideration wasn't lost — captured as future-work investigation trigger. The recall-vs-precision tradeoff is preserved.

Full synthesis at v0.3 Round 3 dogfood evidence.

N=1 trial; this is anecdote, not benchmark. The systematic benchmark suite (hono / httpx / cobra) measures efficiency and quality (see §The Numbers below). This A/B trial measures the substantively-distinct design-alignment axis — which doesn't fit benchmark-suite methodology (every code-change task is repo- specific) but is the substantive value proposition for cohort developers building on real codebases.

The Numbers

We benchmark ContextAtlas against baseline Claude Code on three repositories chosen to reflect realistic developer workloads:

Repo	Language	Source files	Role
honojs/hono	TypeScript	186	Mid-sized framework
encode/httpx	Python	23	Focused production library
spf13/cobra	Go	19	CLI framework

Methodology. 24 prompts per repo, 6 task buckets, blind manual grading, pre-registered rubric, no cherry-picking. Full methodology in RUBRIC.md.

Efficiency: 50-71% tool-call reduction on architectural prompts

Phase 5 reference run on hono, six pre-registered prompts:

Prompt	Bucket	Alpha calls	CA calls	Δ	Alpha $	CA $
h1-context-runtime	win	18	9	−50%	$2.36	$1.52
h2-router-contract	win	11	5	−55%	$0.60	$0.53
h3-middleware-onion	win	5	5	0%	$0.38	$0.47
h4-validator-typeflow	win	21	6	−71%	$2.95	$0.52
h5-hono-generics	tie	11	13	+18%	$0.79	$1.17
h6-fetch-signature	trick	3	4	+33%	$0.17	$0.29
aggregate		69	42	−39%	$7.25	$4.50 (−38%)

The headline case: h4-validator-typeflow ran 7.3× cheaper ($2.95 → $0.52) at equivalent answer depth. CA opens with the governing ADR by number; the baseline reconstructs the architecture from source. Tie and trick buckets (h5, h6) show CA net-negative as the rubric predicted — CA over-engineers on questions where architectural intent doesn't carry load. Bucket-aware methodology surfaces these expected cases rather than burying them.

Cross-language replication: the same architectural-intent win mechanism holds on Python (Phase 6 — httpx) and Go (Phase 7 — cobra). Phase 8 re-ran the locked prompt sets against v0.3-sharpened atlases at the same pinned target SHAs: 45-72% token reduction on architectural-intent prompts across all three target languages. Full synthesis at phase-8-v0.3-reference-run.md.

Quality: blind-graded, paired-t with confidence intervals

v0.5 shipped the LLM-judge methodology under paired-mode anonymization (per ADR-19). Cross-cell rollup paired-t at N=27 differences per axis (5 anchor cells × n=5 trials × 2 conditions; hono h1 auto-stretch to n=8):

Quality axis	Mean Δ (0-3 scale)	95% CI	Tier
Factual correctness	+0.370	[0.176, 0.565]	CLEAN
Hallucination	+0.296	[0.032, 0.561]	Borderline
Actionability	+0.148	[0.005, 0.291]	Borderline
Completeness	+0.037	[-0.039, 0.113]	Not distinguishable

Threshold pre-registration: the three-tier framing (≥0.05 CLEAN; 0.001-0.05 BORDERLINE; ≤0 NOT distinguishable) was locked before precision values were computed. No goalpost-shifting after data. 76% tie rate confirms anonymization worked — the judge couldn't tell which condition was which on three-quarters of comparisons.

Full per-axis numerics + 9 named findings at the Phase-9 reference doc. Honest methodology limits documented in §Methodology and Honest Limits below.

How ContextAtlas Compares to Alternatives

A few deliberate framings — what ContextAtlas is and isn't relative to neighboring tools:

vs. graph-based code intelligence (Graphify and similar). We're in the same category — both build pre-computed indexes over codebases for LLM agents via MCP. That's genuine category overlap, and we want to be straight about it. Where we differ:

• LSP-grounded vs. heuristic-extracted. ContextAtlas delegates all structural questions to the language server (tsserver, Pyright, gopls, ruby-lsp). Graphify derives structure via parsing and extraction.
• Pre-composed bundles vs. graph primitives. ContextAtlas's MCP tools return fused bundles in one call. Graphify exposes graph operations (graph_query, get_neighbors, shortest_path) that callers compose.
• Narrow scope vs. broad scope. ContextAtlas indexes code + prose
  • git. Graphify ingests documentation, diagrams, research papers, and more.
• Claim-first vs. graph-first. ContextAtlas stores discrete claims with severity labels, optimized for "what constrains this symbol?" Graphify models the world as nodes and edges, optimized for "what connects to this node?"

Whether our bets produce better results for a given workload is an empirical question. See the numbers above.

vs. session-memory tools (claude-mem, engram, anamnesis). Those capture accumulated session history — what Claude learned or did in past conversations. ContextAtlas provides static architectural ground truth extracted from your code, ADRs, and docs. Different information sources with occasional overlap (when session discussions become ADRs or commits), but fundamentally different problems. Session-memory tools also can't really be committed to a repo; ContextAtlas's atlas can.

vs. LSP-in-MCP (LSP-AI and similar). ContextAtlas uses LSP as its source of structural truth. If you just want LSP-in-MCP, those projects solve that well. ContextAtlas layers architectural intent and git history on top.

vs. embedding-based search. We evaluated this and chose symbol-keyed claims instead. Embeddings are fuzzy; LSP symbols are exact. Embedding-based ranking is a post-MVP enhancement contingent on benchmark evidence that it helps — see ADR-09 for the full rationale.

The committed-atlas pattern

ContextAtlas produces a committable team artifact — atlas.json — that lives in the repo alongside your code and ADRs. This is the piece that turns ContextAtlas from a personal productivity tool into a team asset.

• New team member clones the repo: they pull down atlas.json with everything else. On first run, ContextAtlas imports the committed atlas directly into their local cache — no extraction API calls, no 10-minute wait. Productive from the moment they open Claude Code.
• Contributor submits a PR: if their code change affects architectural claims, they regenerate atlas.json as part of their commit. Reviewers see both the code change and the atlas diff in the PR.
• Developer bounces between machines: atlas state is version-controlled, not trapped on one laptop.
• Returning to a project after months away: pull the latest main, and the atlas reflects everything the team did in your absence. Only files changed since you last pulled need incremental reindex.
• Open-source projects: casual contributors benefit immediately without paying any setup cost. The project's accumulated architectural knowledge flows to them automatically.

For teams that cannot commit the atlas, set atlas.committed: false in the config. Every developer runs their own extraction. The team artifact benefit is lost, but ContextAtlas still works as a personal tool.

This model — committed team artifact with a local cache for query performance — is a categorical difference from both session-memory tools and knowledge-graph tools. Detailed in ADR-06.

Architecture

                INDEX TIME (once per source change)
                ──────────────────────────────────────
                ADRs ──────────┐
                Docstrings ────┤
                Git commits ───┼──► Opus 4.7 extraction
                LSP symbols ───┘              │
                                              ▼
                              atlas.json (committed to repo)
                                              │
                                              ▼
                              SQLite + FTS5 BM25 (local cache)

                QUERY TIME (every Claude call, zero API)
                ──────────────────────────────────────
                Claude Code:  get_symbol_context("X")
                                              │
                                              ▼
                              One fused bundle, sub-100ms
                              (LSP refs + intent + git + tests)

Five layers, each with one job:

1. MCP interface. get_symbol_context, find_by_intent, and impact_of_change tools exposed to Claude.
2. Query fusion. Composes results from signal sources per query.
3. Signal sources. LSP (via tsserver/Pyright/gopls/ruby-lsp), intent registry (from SQLite), git, tests.
4. Extraction pipeline. Opus 4.7 reads prose docs and emits structured claims keyed to symbols.
5. Storage. SQLite index, SHA-keyed for incremental reindex.

Signal fusion at query time works as a substantively cheap lookup: when Claude calls get_symbol_context("OrderProcessor"), the MCP handler hits the LSP for live structural facts (definition, references, types) + reads the symbol's pre-extracted intent claims from SQLite + folds in git heat + tests. The bundle returned to Claude is composed, not computed — substantive joins happened at index time. This is the substantive distinction from graph-based alternatives that expose primitives (get_neighbors, shortest_path) which callers compose at query time.

The architectural promise: expensive understanding happens once at index time; queries are local dictionary lookups, zero API calls. This bounds cost, latency, and unpredictability — and it's a hard invariant, not an optimization.

Full design in DESIGN.md.

Data flow and privacy

What ContextAtlas does and doesn't send off your machine:

Sent to Anthropic's API (at index time only):

• Text contents of ADRs, READMEs, and other markdown docs configured via .contextatlas.yml
• This happens once per document per change — only on initial index and on incremental reindex of changed files

Never sent anywhere:

• Your source code
• Your git history
• LSP symbol data (names, references, types)
• Query contents at runtime

Stored locally only:

• The extracted claims database (.contextatlas/index.db by default)
• All runtime query resolution happens against this local SQLite file

At query time — every get_symbol_context call Claude makes during your work — ContextAtlas performs a local SQLite lookup plus local LSP calls. No network traffic. No model calls. Your code never leaves your machine during normal use.

Index-time extraction uses the Anthropic API per standard API terms. If your ADRs contain sensitive architectural decisions, they'll be processed under those terms like any other API-submitted content.

The Three Tools

The three MCP tools are not three parallel features — they're one fused context substrate with three access patterns.

get_symbol_context — the primitive. "I know the symbol; give me everything." Returns the full fused bundle (signature, ADR claims, references, git heat, tests, types) in a single call. Multi-symbol mode handles up to 10 symbols per request (per ADR-15).

find_by_intent — the semantic-query composite. "I don't know the symbol; find it by what it does." Ranks by BM25 against indexed claim text in local SQLite FTS5 — no embedding service, no external calls, deterministic results (per ADR-09).

impact_of_change — the blast-radius composite. "I'm about to change this; what breaks?" Adds git co-change patterns and test impact on top of the primitive.

Refresh Discoverability

ContextAtlas atlas is a substrate you build once and refresh after code or ADR changes. ONE canonical entry point per cohort path; behavior adapts based on substrate state:

	CLI	Skills
Cold-start	contextatlas index (full extraction)	/index-atlas (full extraction)
Refresh	contextatlas index (Phase 4 SHA-diff incremental)	/index-atlas (refresh-aware workflow)

SHA-diff incremental refresh per ADR-12 is substantively cheaper than cold-start scaffolding. Unchanged ADR and docstring sources skip; only changed sources re-extracted.

Quick Start

Status: v0.9.0 shipped 2026-05-16. v1.0 public launch substrate complete. Package not yet published to npm; install instructions below describe the intended shape.

Runtime requirements:

• Node.js 20 or newer.
• A language server for each language you configure:
  • TypeScript — typescript-language-server (declared as a peer dependency rather than a direct one, so you control the version). Install alongside ContextAtlas (e.g. npm i -D typescript-language-server typescript).
  • Python — Pyright on the PATH (also a peer dependency).
  • Go — gopls on the PATH (install via go install golang.org/x/tools/gopls@latest).
  • Ruby — ruby-lsp 0.26.x. Recommended install via Bundler in your project's Gemfile (gem 'ruby-lsp', '~> 0.26.0', require: false under group :development). Rails projects additionally benefit from ruby-lsp-rails 0.4.x. Ruby 3.3+ required (4.0+ recommended).

Path A — Claude Code Skills (60 seconds, no API key)

npm install -g contextatlas
contextatlas init

Then in Claude Code:

/generate-adrs   # Skip if you already have ADRs (any path; see Using existing ADRs below)
/index-atlas     # Build the atlas
/prime-atlas     # Verify connection (once per session)

Path B — CLI (90 seconds, API key required)

npm install -g contextatlas
export ANTHROPIC_API_KEY=sk-...
contextatlas init
contextatlas generate-adrs   # Skip if you already have ADRs (any path; see Using existing ADRs below)
contextatlas index
contextatlas doctor          # Verify health

Using existing ADRs and docs

ContextAtlas extracts architectural intent from whatever ADRs and documentation you already have — generate-adrs is for repos without existing ADR substrate.

• Existing ADRs at docs/adr/? Skip generate-adrs; ContextAtlas extracts your existing ADRs automatically.
• ADRs at a different path? Set adrs.path in .contextatlas.yml (default: docs/adr/).
• README, design docs, or other prose? ContextAtlas extracts these too via docs.include (default: README.md + docs/**/*.md). Add custom paths to extract additional documentation surfaces.

The extraction pipeline produces structured claims from any prose source pointed at via config — existing substrate doesn't go unused. See Configuration below for full schema.

MCP server registration

Configure ContextAtlas as an MCP server in your Claude Code settings. Choose based on whether contextatlas is on your PATH:

Option A — global binary on PATH (e.g., installed via npm install -g or npm link):

{
  "mcpServers": {
    "contextatlas": {
      "command": "contextatlas"
    }
  }
}

Option B — direct dist invocation (no global install needed):

{
  "mcpServers": {
    "contextatlas": {
      "command": "node",
      "args": ["/absolute/path/to/contextatlas/dist/index.js"]
    }
  }
}

First-run behavior

• If atlas.json is already committed (teammate ran it first, or it came with the repo), ContextAtlas imports it instantly. No API calls. You're ready in seconds.
• If no atlas exists yet, ContextAtlas runs full extraction. Depending on ADR count and size, this takes 1-10 minutes and costs a few dollars in Opus API credits (CLI path) or session tokens (Skills path). The resulting atlas.json can be committed so future contributors skip this step.
• Cost projection note. Script-reported extraction costs use full-token API pricing; platform-billed actuals reflect prompt-cache discount on the shared EXTRACTION_PROMPT prefix and typically run ~3x lower. v0.4 reference measurements: cobra $5.44 → $1.82, httpx $5.53 → $1.85, hono $10.89 → $3.65 (3.0x ratio consistent across targets). Treat projected costs as conservative upper bounds.
• On subsequent runs, only files whose SHAs have changed since the last index get reprocessed. Usually seconds.

Configuration

Create .contextatlas.yml in your repo root:

version: 1
languages:
  - typescript
  - python
  - go
  - ruby
adrs:
  path: docs/adr/
  format: markdown-frontmatter
docs:
  include:
    - README.md
    - docs/**/*.md
git:
  recent_commits: 5
atlas:
  committed: true    # default; commits atlas.json to your repo

Full reference at docs/config.md.

Methodology and Honest Limits

Credibility is built by stating what we don't claim.

Statistical methodology. All quality measurements are paired-t with 95% confidence intervals — no p-values. NHST at n=5 is statistically void; CIs preserve effect-size visibility. Threshold pre-registration honored verbatim (Option α strict three-tier framing locked before precision values computed).

Single-judge model. v0.5 quality measurements use Sonnet 4.6 as the judge with within-judge consistency ≥80% per axis (pass-1 vs pass-2). Cross-vendor judge-panel graduation is post-v1.0 work.

Three benchmark repos. All quantitative claims are bounded to hono (TypeScript, 186 files), httpx (Python, 23 files), and cobra (Go, 19 files), plus our own dogfood. Generalization beyond these is post-launch cohort work.

v0.5 substrate scope. Quality-axis measurements are 5 anchor cells × n≥5 trials × 2 conditions (hono h1 auto-stretch to n=8); not full-matrix replication. Matrix-completion graduation is post-v1.0.

v0.6 cross-cycle replication caveat. A targeted matrix-replication subset at v0.6 (8 cells × n=5) showed attenuation on 2 of 4 quality axes vs the v0.5 anchor cells (factual_correctness CLEAN→BORDERLINE; actionability BORDERLINE→NOT distinguishable). Root cause: the v0.5 measurements were against an earlier atlas version, and the cross-cycle methodology didn't control for atlas-substrate-version. Full causal investigation deferred to post-launch. Detail at Phase-10 reference doc.

v0.3 single-run methodology. Phase 8 reports n=1 per cell; blind-graded quality-axis measurement was added at v0.5. The Beta-vs- Beta+CA reporting at Phase 8 carries the atlas-file-visibility caveat (bias direction conservative — actual CA contribution likely larger than published numbers indicate).

Dogfooding is not a measured benchmark. Throughout development, ContextAtlas indexes its own ADRs and is used by Claude Code during work on ContextAtlas itself. This is a development practice, not part of the four-condition matrix — which runs only against the three external targets.

Favorable and unfavorable results both published. Phase 7's cross-harness asymmetry hypothesis was FALSIFIED on v0.3 substrate. v0.6's atlas-substrate-version confound was surfaced and disclosed. Tie and trick buckets routinely show CA net-negative; we report them inline rather than burying them.

Status and Roadmap

Current: v0.9.0 (shipped 2026-05-16). v1.0 public launch substrate complete; launch execution work folds into v1.0.0 without a separate v0.9.1 tag.

Recent cycle highlights (full per-cycle scope at docs/release-history.md):

• v0.9 (May 16): Ruby adapter ship — fourth supported language via ruby-lsp + ruby-lsp-rails per ADR-21. Repo launch substrate (MIT license + community substrate + cycle docs migration to docs/cycles/v0_X/). Launch positioning work in progress.
• v0.8 (May 14): Substrate-equivalence + path-comparability + BM25 activation. Closed Skill-substrate parity to CLI at 65-83% claim ratio across hono/httpx/cobra benchmarks.
• v0.7 (May 12): Launch-bearing cycle — Path-3 entry-point- determined architecture (CLI/Skills equivalence per ADR-02 graduation) + generate-adrs feature with canonical depth-floor enforcement via validate-adrs.
• v0.6 (May 9): Pipeline mechanics + targeted matrix-replication subset (8 cells × n=5 × 2 conditions). F1 PRIMARY atlas-substrate- version confound surfaced; full causal investigation deferred to post-launch.
• v0.5 (May 4): Quality methodology cycle — LLM-judge harness + paired-mode anonymization per ADR-19 + paired-t statistical primitive (ADR-19 §4 amendment). V1.0 ship-gate criterion #1 parenthetical CLOSED.
• v0.4 (April 29): Substrate hardening — LSP timing-race robustness via two-readiness-signals (ADR-18) + cost-projection disclaimers + dogfood foundation + doctor diagnostic script.
• v0.3 (April 28): Atlas precision cycle — narrower attribution
  • multi-symbol API per ADR-15 + atlas schema v1.3 with contextatlas_commit_sha + Phase 8 cross-target validation (45-72% range).
• v0.2 (April 25): Three-language baseline — Go adapter via gopls per ADR-14 + cross-language cobra/httpx reference runs.
• v0.1 (initial MVP): Three MCP tools + TS/Python adapters + Opus 4.7 extraction pipeline + SQLite incremental reindex + Phase 5 hono reference run (50-71% tool-call reduction on architectural win-bucket prompts).

Roadmap (post-v1.0):

• Cohort exposure execution against carried-forward recruitment infrastructure (v1.0 ship-gate criterion #3)
• Full benchmark matrix completion + cross-vendor judge panel
• Semantic embedding layer for find_by_intent (evidence-gated)
• Task-shaped bundle queries: why_does_this_fail, onboard_to_feature, audit_change
• Additional language adapters by demand: Rust, C#, Java, Kotlin
• Non-markdown intent sources: RST, AsciiDoc

For detailed milestone arc and per-cycle scope: ROADMAP.md, docs/cycles/, docs/release-history.md, and research/v1.1-candidates.md.

Language Support

The language adapter interface is a stable plugin surface — each new language is an additive contribution, not a core change. See docs/language-adapter-guide.md for the contributor onboarding walkthrough.

Language	Adapter	LSP Server	Shipped
TypeScript	TypeScriptAdapter	typescript-language-server	v0.1
Python	PyrightAdapter	Pyright	v0.1
Go	GoAdapter	gopls	v0.2
Ruby	RubyAdapter	ruby-lsp (+ ruby-lsp-rails)	v0.9

Contributing

ContextAtlas is MIT licensed and welcomes contributions. Areas where contribution will be especially valuable:

• New language adapters. The LanguageAdapter interface is small and stable. Adding Java, .NET, Rust, Kotlin, or other language support is a self-contained project. See docs/language-adapter-guide.md.
• Non-markdown intent sources. Currently we support markdown ADRs with YAML frontmatter. RST, AsciiDoc, and other formats are welcome.
• Benchmark repos. Additional benchmark coverage on more codebases strengthens the eval.

Benchmarks and Methodology

Benchmarks and methodology live in a separate repository: github.com/traviswye/ContextAtlas-benchmarks. That repo contains the harness code, locked prompt sets, published measurement results, and the full methodology document (RUBRIC.md). Keeping the harness out of this repo means the benchmarks measure the published contextatlas package's actual behavior rather than an internal monorepo build.

Credits

Built during the "Build anything with Opus 4.7" hackathon.

ContextAtlas uses:

• Claude Opus 4.7 for index-time intent extraction
• typescript-language-server for TypeScript symbol resolution
• Pyright for Python symbol resolution
• gopls for Go symbol resolution
• ruby-lsp for Ruby symbol resolution
• better-sqlite3 for the index store
• @modelcontextprotocol/sdk for MCP server implementation

License

MIT. See LICENSE.