Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add humanerd-drew/opencode-drewgent --skill semble-researchgit clone --depth 1 https://github.com/humanerd-drew/opencode-drewgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/semble-research)<a href="https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/semble-research"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/semble-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/semble-research"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/semble-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00662 |
| Opus 5.5 | $0.00000 | $0.00265 |
| Sonnet 5.5 | $0.00000 | $0.00132 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
semblent-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semble Semantic Code Search
Semble indexes codebases and answers natural-language queries about code. It returns ranked results with file locations and relevance scores.
Available Tools
semble_search
Search using natural language intent.
semble_search(query="authentication flow", path="/repo", top_k=5)
semble_search(query="where is cache invalidation", path=".", top_k=5)
semble_search(query="how does delegate_task spawn subagents", path=".", top_k=3)
semble_find_related
Find similar code given a file:line from a prior result.
semble_find_related(file_path="tools/delegate_tool.py", line=287, path=".", top_k=5)
Decision Matrix: When to Use Semble
| Situation | Tool |
|---|---|
| Find code by what it does (intent) | semble_search |
| Find code by exact pattern (regex) | search_files (grep) |
| Find code by filename | search_files (glob) |
| Need full file context | read_file |
| Explore unfamiliar codebase | semble_search first |
| Debugging "where did this error come from" | semble_search |
| Refactoring related code | semble_search + semble_find_related |
Workflow
- Start with
semble_searchto find relevant chunks by describing intent - Use
semble_find_relatedwith promising results to discover related implementations - Use
read_fileonly when you need full context of a specific file - Use
search_files(grep) only for exact string matches or exhaustive searches
Index Location
Semble indexes are stored at ~/.semble/. Indexes persist across sessions — no re-indexing needed on each query.
Limitations
- Index updates happen on-demand (server-side, no local control)
- Very large codebases may take longer to return results
- Best for exploratory queries, not exhaustive searches
Examples
Find where AIAgent is initialized:
semble_search(query="AIAgent __init__ constructor", path=".", top_k=5)
Find workflow/integration patterns:
semble_search(query="workflow integration P0 P1 brain", path=".", top_k=3)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 87 lines · 0 tokens per session scan A 9b05b1c99d00
semblent-search is a skill published in the GitHub repository humanerd-drew/opencode-drewgent (3 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 662 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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