daemon.cjs · part 318

60 text occurrences from desktop/Cursor.app/Contents/Resources/app/extensions/cursor-agent-host/dist/agent-host-daemon/dist/bin/daemon.cjs, part 318. Every entry preserves the shipped literal and its saved verdict or selection reason.

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Cannot await shell task: terminals folder is unavailable

Failed to read shell output

No shell found for id ${taskId}

Slept briefly.

Slept for ${seconds}s.

Pattern matched: ${value.regexMatch ?? ""}

Pattern did NOT match.

${sleepLine} Sleep released early: a new user message is arriving.

Task completed in ${runtimeMs}ms with exit code: ${exitCodeText}.

Task complete.

${firstLine} ${regexLine}

${firstLine}
output_file_path: ${value.outputFilePath}
output_length: ${value.outputLength.toString()}

Task still running after ${(value.runtimeMs ?? BigInt(0)).toString()}ms...

Task still running.

${firstLine} Wait released early: a new user message is arriving.

${firstLine} ${regexLine}

${firstLineWithRegex}
output_file_path: ${value.outputFilePath}
output_length: ${value.outputLength.toString()}

Error awaiting task: ${normalizedResult.value.error}

Unknown error

Poll a background shell.

Unhandled version: ${_exhaustive}

Use to sleep and check shell progress. Never sleep using shell.

Failed to get AI attribution: ${result.result.value.error}

An unknown error occurred.

Unhandled version: ${_exhaustive}

Trace how code was built with AI for files and commits.

Treat this as git blame for AI-built changes: it reveals which conversations produced the code, why it was implemented a certain way, what alternatives were considered, and the intent behind decisions.

When to use this tool:
- The user asks about a file, module, or area of code and wants to understand it deeply -- use this alongside reading the file to add context about how and why it was built.
- Questions about history, evolution, or recent changes to code (e.g. "tell me about this file", "what's changed here recently", "how has this evolved"). For concept-level questions without a specific file (e.g. "how has plan mode evolved?"), first find the relevant files, then use this tool on them.
- Questions about authorship or decision-making (e.g. "who wrote this", "why is it done this way", "what tradeoffs were made").
- Before planning changes to existing code, to understand the intent behind the current implementation and avoid undoing deliberate design decisions.
- When used during planning, surface deliberate design decisions, past reversions, and intentional tradeoffs as constraints. Explain how the proposed changes relate to those prior decisions.

Output includes conversation context depth:
- Conversation title / tldr / overview / summary bullets
- Models used across the conversation and commit-linked sections

Query shape policy:
- File-first (`file_paths`) when the user anchor is code location. Always pass repository-relative git paths (for example `backend/server/src/app.ts`), not absolute workspace paths like `/Users/.../projects/repo/backend/server/src/app.ts`.
- Commit-first (`commit_hashes`) when the user anchor is a change event or narrative.
- Use both when both anchors are known (high precision, less noise).
- Use `file_paths` to compare a bounded set of files; use `commit_hashes` for stack/series narrative.
- For `file_paths`, pass repository-relative git paths (for example, paths shown in `git diff`), not absolute local filesystem paths.

Output strategy:
- Start with `output_mode: "summary"` + modest `max_commits`.
- Pivot to `output_mode: "detailed"` with line ranges once a target section is identified.
- If file query is empty but activity is expected, pivot to commit query.
- If commit query is noisy, add file filters.

Transcript fallback:
- If transcript pointers/context are available and you need deeper decision narrative, inspect transcript snippets after this tool (read in chunks, do not read entire transcript files at once).

Returns commit history with conversation-linked lineage details suitable for code archaeology and implementation-intent analysis.

use strict

Object expected.

Symbol.asyncDispose is not defined.

Symbol.dispose is not defined.

Object not disposable.

An error was suppressed during disposal.

File paths to get AI attribution for. Use repository-relative git paths (for example: backend/server/src/app.ts), not absolute workspace paths.

Optional start line (1-indexed). If not provided, gets AI attribution for entire file. Applies to all files.

Optional end line (1-indexed). If not provided, gets AI attribution to end of file. Applies to all files.

Commit hashes to get AI attribution for. Returns AI conversation summaries for each commit.

Controls output shape and verbosity. Use "summary" for conversation-level rollups and "detailed" for per-commit/per-range output.

Optional cap on commits returned after filtering/de-duplication. Must be between 1 and 200.

When false, omit detailed line ranges from attribution output.

Either file_paths or commit_hashes must be provided

[ai-attribution] fetching attribution

[ai-attribution] attribution fetched successfully

[ai-attribution] failed to fetch attribution

Failed to fetch AI attribution data. This may be a temporary issue - please try again later.

AI attribution output

Failed to fetch AI attribution data. This may be a temporary issue - please try again later.

A fast, efficient Node.js Worker Thread Pool implementation

tsc && gen-esm-wrapper . dist/esm-wrapper.mjs

standardx "**/*.{ts,mjs,js,cjs}" | snazzy

c8 tap

npm run lint && npm run build && npm run test:coverage

c8 --reporter=lcov tap --cov

npm run build

npm run bench:taskqueue && npm run bench:piscina

npm run benchmark:piscina-default &&npm run benchmark:piscina-fixed-queue && npm run benchmark:piscina-comparison

npm run benchmark:queue-comparison

node benchmark/simple-benchmark.js

node benchmark/simple-benchmark-fixed-queue.js

node benchmark/piscina-queue-comparison.js

node benchmark/queue-comparison.js