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.
git clone --depth 1 https://github.com/jgt87/local-llm-mcpWrote 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/commands/jgt87/local-llm-mcp/local_summarize)<a href="https://agentmods.dev/commands/jgt87/local-llm-mcp/local_summarize"><img src="https://agentmods.dev/badge/commands/jgt87/local-llm-mcp/local_summarize/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/commands/jgt87/local-llm-mcp/local_summarize"><img src="https://agentmods.dev/badge/commands/jgt87/local-llm-mcp/local_summarize.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 | $0.00018 | $0.00399 |
| Opus 5.5 | $0.00007 | $0.00160 |
| Sonnet 5.5 | $0.00004 | $0.00080 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
local_summarize 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 5d 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.
What it actually says
Arguments: $ARGUMENTS
The arguments name the source and may add what to focus on. If the source is a file path, read the file. If it is a shell command, run it and use its output. Otherwise treat it as the text itself. If there are no arguments, the source is the most recent long tool output or text in this conversation.
Summarise the source with local_ask. Send it with a system prompt asking for a short plain summary: what happened, anything that failed, and anything needing action. Use maxTokens around 200. If the text is very long, drop repetitive lines (progress bars, repeated warnings) before sending rather than cutting off the end, where errors usually are. Then check every claim in the summary against the source; a summary that invents an error or misses one is worse than none.
Ground rules:
- Use the local-llm tools for the generation step; do the reading, trimming and checking yourself. The local model has no file access, no repo context and no memory between calls, so every call must carry all the text it needs.
- Keep maxTokens tight: locally it runs at roughly 16 tokens per second.
- Stay on the default provider unless the user asked for OpenRouter. Never send secrets, keys or private data to provider="openrouter".
- If a call fails because Ollama is unreachable, say so and stop; do not silently do the work yourself instead.
- Treat the reply as a draft. Check it against the source before presenting it, fix what is wrong, and say briefly what you changed.
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.
- 5d ago First seen · 21 lines · 0 tokens per session scan A 0289b3ff5256
local_summarize is a command published in the GitHub repository jgt87/local-llm-mcp (0 stars, last pushed 6d ago), licensed MIT. It adds 18 tokens to every session and 399 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-27.
Other commands, from other repositories
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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.