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 agentmods add commands/bborbe/semantic-search/researchgit clone --depth 1 https://github.com/bborbe/semantic-searchWhat 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 | $0.00028 | $0.01161 |
| Opus 5 | $0.00014 | $0.00580 |
| Sonnet 5 | $0.00006 | $0.00232 |
| Haiku 4.5 | $0.00003 | $0.00116 |
Grade A, and why
research scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsS --max-time 10 "http://127.0.0.1:<PORT>/search?q=$(printf %s "<topic>" | jq -sRr @uri)&top_k=10" How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Usage
/semantic-search:research kafka backup strategy
/semantic-search:research "obsidian git workflow"
/semantic-search:research --server=work sentry alerting flow
Process
Step 1: Validate input
- First non-flag argument →
topic --server=<label>(optional) → scope to a single MCP server; omit to query every available server- If no topic: show usage and STOP.
Step 2: Initial semantic search
Discover available MCP servers (must match this command's allowed-tools):
| Tool name | Conventional label |
|---|---|
mcp__semantic-search__search_related |
(default) |
mcp__semantic-search-personal__search_related |
personal |
mcp__semantic-search-work__search_related |
work |
If --server=<label> was passed: keep only the matching tool. Else: attempt all three in parallel and treat any tool that errors with "not available" / "unknown tool" as absent.
Query all available MCP servers with top_k=10 each:
<tool-name>(query=<topic>, top_k=10)
Run in parallel. Each result list is tagged with its server label. Merge by score descending; keep top 10 overall.
Fall back to REST if no MCP server is wired up:
# macOS
launchctl list 2>/dev/null | awk '/com\.github\.bborbe\.semantic-search-http/ {print $3}'
# Linux
systemctl --user list-units 'semantic-search-http*' --no-legend 2>/dev/null | awk '{print $1}'
For each running service, infer port from plist/unit and query:
curl -fsS --max-time 10 "http://127.0.0.1:<PORT>/search?q=$(printf %s "<topic>" | jq -sRr @uri)&top_k=10"
Override default with SEMANTIC_SEARCH_URL if needed.
Last resort if both MCP and REST fail:
Grep pattern="<topic keywords>" path=<content roots> -i --files-with-matches
Note in the final report which transport(s) were used and which servers were queried.
Step 3: Categorize results
Sort merged results into buckets based on path:
- Guides / Reference — paths containing
guide,hub,framework, or located in known knowledge folders - Operational / Runbooks — paths containing
runbook,alert, or in*Runbooks*folders - Notes / Tasks — daily notes, task files, miscellaneous
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.
- 2d ago First seen · 120 lines · 28 tokens per session scan A 198052339c4a
research is a command published in the GitHub repository bborbe/semantic-search (2 stars, last pushed 6d ago), licensed BSD-2-Clause. It adds 28 tokens to every session and 1,161 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.