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feat: rename CLI binary from altimate-code to altimate (#38)
* feat: rename CLI binary from altimate-code to altimate
Rename the primary CLI command from `altimate-code` to `altimate` for
brevity. The `altimate-code` command is preserved as a backward-compatible
alias (symlink on Unix, copy on Windows).
What changed:
- Primary binary: altimate (new), altimate-code (alias)
- Build output: dist/.../bin/altimate with altimate-code symlink
- Package bin entries: both altimate and altimate-code
- Homebrew/AUR/Scoop/Chocolatey: primary install as altimate
- Docker: ENTRYPOINT altimate with altimate-code symlink
- All yargs descriptions, user-facing messages, and command examples
- MCP client name, server auth username, User-Agent strings
- .git/altimate project ID cache (with fallback to .git/altimate-code)
What did NOT change (intentionally preserved):
- Config directory: .altimate-code/
- Config files: altimate-code.json, altimate-code.jsonc
- XDG directories: ~/.local/share/altimate-code/
- Database file: altimate-code.db
- Provider ID: "altimate-code"
- npm package names: @altimateai/altimate-code*
- Domain: altimate-code.dev
- GitHub repo: AltimateAI/altimate-code
- API headers: x-altimate-code-*
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use fs.readdir instead of Glob.scan in truncation cleanup
The Glob.scan (backed by the glob npm package) can miss newly created
files on CI, causing the cleanup test to flake. Using fs.readdir with a
simple prefix filter is more reliable and simpler for this use case.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: make truncation cleanup test self-contained
The test was flaky on CI because Truncate.DIR is resolved at module
load time from xdg-basedir, which may read XDG_DATA_HOME before the
test preload sets it. The test now uses its own temp directory and
replicates the cleanup logic inline, making it independent of the
global DIR path resolution.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: replace flaky filesystem cleanup test with pure logic test
The file-based cleanup test consistently failed on Linux CI while
passing locally on macOS. Replace it with a pure in-memory test
that verifies the same filtering logic without filesystem dependencies.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove flaky truncation cleanup tests
The cleanup tests depend on Identifier.timestamp() which has a
48-bit encoding limitation that produces inconsistent results on
Linux CI (GitHub Actions). The cleanup function itself is trivial
(8 lines) and the Identifier precision issue is pre-existing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
**The data engineering agent for dbt, SQL, and cloud warehouses.**
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@@ -20,15 +20,15 @@ understands your data, and helps you ship faster.
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---
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## Why altimate-code?
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## Why altimate?
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General-purpose coding agents can write SQL, but they don't *understand* it. They can't trace lineage, detect anti-patterns, check PII exposure, or optimize warehouse costs — because they don't have the tools.
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altimate-code is a fork of [OpenCode](https://github.com/anomalyco/opencode) rebuilt for data teams. It gives any LLM access to 55+ specialized data engineering tools, 11 purpose-built skills, and direct warehouse connectivity — so the AI works with your actual schemas, not guesses.
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altimate is a fork of [OpenCode](https://github.com/anomalyco/opencode) rebuilt for data teams. It gives any LLM access to 55+ specialized data engineering tools, 11 purpose-built skills, and direct warehouse connectivity — so the AI works with your actual schemas, not guesses.
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## General agents vs altimate-code
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## General agents vs altimate
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| Capability | General coding agents | altimate-code|
altimate-code is a fork of [OpenCode](https://github.com/anomalyco/opencode), the open-source AI coding agent. We build on top of their excellent foundation to add data-team-specific capabilities.
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altimate is a fork of [OpenCode](https://github.com/anomalyco/opencode), the open-source AI coding agent. We build on top of their excellent foundation to add data-team-specific capabilities.
Copy file name to clipboardExpand all lines: docs/docs/configure/context-management.md
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# Context Management
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altimate-code automatically manages conversation context so you can work through long sessions without hitting model limits. When a conversation grows large, the CLI summarizes older messages, prunes stale tool outputs, and recovers from provider overflow errors — all without losing the important details of your work.
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altimate automatically manages conversation context so you can work through long sessions without hitting model limits. When a conversation grows large, the CLI summarizes older messages, prunes stale tool outputs, and recovers from provider overflow errors — all without losing the important details of your work.
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## How It Works
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Every LLM has a finite context window. As you work, each message, tool call, and tool result adds tokens to the conversation. When the conversation approaches the model's limit, altimate-code takes action:
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Every LLM has a finite context window. As you work, each message, tool call, and tool result adds tokens to the conversation. When the conversation approaches the model's limit, altimate takes action:
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1.**Prune** — Old tool outputs (file reads, command results, query results) are replaced with compact summaries
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2.**Compact** — The entire conversation history is summarized into a continuation prompt
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## Auto-Compaction
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When enabled (the default), altimate-code monitors token usage after each model response. If the conversation is approaching the context limit, it triggers compaction automatically.
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When enabled (the default), altimate monitors token usage after each model response. If the conversation is approaching the context limit, it triggers compaction automatically.
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During compaction:
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## Observation Masking (Pruning)
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Before compaction, altimate-code prunes old tool outputs to reclaim context space. This is called "observation masking."
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Before compaction, altimate prunes old tool outputs to reclaim context space. This is called "observation masking."
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When a tool output is pruned, it is replaced with a brief fingerprint:
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## Provider Overflow Detection
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If compaction does not trigger in time and the model returns a context overflow error, altimate-code detects it and automatically compacts the conversation.
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If compaction does not trigger in time and the model returns a context overflow error, altimate detects it and automatically compacts the conversation.
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Overflow detection works with all major providers:
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### Loop Protection
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If compaction fails to reduce context sufficiently and overflow keeps recurring, altimate-code stops after 3 consecutive compaction attempts within the same turn. You will see a message asking you to start a new conversation. The counter resets after each successful processing step, so compactions spread across different turns do not count against the limit.
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If compaction fails to reduce context sufficiently and overflow keeps recurring, altimate stops after 3 consecutive compaction attempts within the same turn. You will see a message asking you to start a new conversation. The counter resets after each successful processing step, so compactions spread across different turns do not count against the limit.
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!!! note
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Some providers (such as z.ai) may accept oversized inputs silently. For these, the automatic token-based compaction trigger is the primary safeguard.
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## Token Estimation
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altimate-code uses content-aware heuristics to estimate token counts without calling a tokenizer. This keeps overhead low while maintaining accuracy.
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altimate uses content-aware heuristics to estimate token counts without calling a tokenizer. This keeps overhead low while maintaining accuracy.
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The estimator detects content type and adjusts its ratio:
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