Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep

Do inefficient grep/sed behaviors have a secondary benefit in seeding the context with breadcrumbs of code from irrelevant matches?

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Full output below. There's other stuff in there, the "working with unfamiliar data or systems" is maybe slightly risky but (seemingly, after a week or two) much more token efficient and effective.

I also added the plugins directly to Claude Code: ty Plugin · claude-code-lsps · enabled vscode-langservers Plugin · claude-code-lsps · enabled vtsls Plugin · claude-code-lsps · enabled

  ~ cat ~/.claude/CLAUDE.md 

# Python Environment - ALWAYS use uv — never use pip, pip install, python, or python3 directly - Activate venv: `source .venv/bin/activate` - Install deps: `uv sync` - Add a dep: `uv add ` - Run scripts: `uv run ` - Run tools: `uvx `

# Long-running scripts - Any script, command, migration, data job, or test run that may take more than 2-3 seconds should emit regular status updates while it runs. - Prefer progress that is useful for diagnosing where time is going: current phase, item counts, batch numbers, elapsed time, retry/backoff state, or the external service being waited on. - For loops or batch jobs, log progress periodically rather than only at start/end; keep the cadence readable and avoid flooding output.

# Code Intelligence - LSP servers available: ty (Python), vtsls (JS/TS), vscode-langservers (HTML/CSS/JSON) - Use LSP for: - findReferences before any refactor - goToDefinition when navigating unfamiliar code - diagnostics after edits to catch type errors - grep/search is fine for simple lookups in small files - NEVER refactor without findReferences impact analysis first - After every edit, check LSP diagnostics before moving on

# Documentation - Context7 is available for up-to-date library docs - Use `ctx7 docs ` to fetch current documentation - Use `ctx7 library ` to find a library ID first

# Working with unfamiliar data or systems - Prefer experimenting on real data over reasoning about it in the abstract. Your outputs are noticeably better when grounded in a concrete sample than when derived from minutes of speculation. - When a task involves parsing/processing/integrating with some external artifact (a report, an API response, a file format, a third-party tool's output), the FIRST step is to fetch or generate a real example and inspect it. Do not write code against an imagined shape. - Experiments must be non-destructive: read-only fetches, copies into a scratch dir, dry-run flags. Never mutate the user's real data to learn about it. - Before assuming you lack credentials, check the current working directory's `.env` file (and `.env.example` for hints about which keys exist) — API keys, tokens, and connection strings for the relevant service are very often already there. - If you cannot obtain real data on your own (auth genuinely missing, lives on another machine, behind a paywall, etc.), STOP and ask the user to provide a sample rather than guessing. - Example: asked to process an Amazon sales report, the first action is to fetch (or have the user paste) one actual report and look at its columns — not to draft a parser based on what such a report "probably" contains.

Feedback:

codex-cli hangs when calling this through the MCP. The semble process even sticks around as a zombie, forever stalled out. No idea why, logs have nothing.

When called through a skill via CLI style calling, GPT 5.5 loves to give a ton of search terms like it is used to doing with ripgrep. Not sure how effective this is, the short docs in the github and the instructions the agent has isn't clear on what is optimal.

Lastly, I got some errors with external connections to github when I was installing it for bash use. Maybe its related to the hanging? No idea.

edit: My agent also loves to follow-on with ripgrep, which seems redundant. Acts like it has trust issues. I think a more extensive agent skill description could guide the agent into proper use.

The 98% is vs the grep+read loop, not grep output alone. When an agent hits an unfamiliar codebase it typically does "cat file" or reads the whole thing first, at least in my experience. If you're reliably getting agents to do "grep -C N" and stop there I'd genuinely be curious what your setup looks like, because I think the quality of the results is just too low to serve as useful context.