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/Oriolshhh/runware-image-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/oriolshhh/runware-image-mcp/compress-context)<a href="https://agentmods.dev/commands/oriolshhh/runware-image-mcp/compress-context"><img src="https://agentmods.dev/badge/commands/oriolshhh/runware-image-mcp/compress-context.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.00317 |
| Opus 5 | $0.00018 | $0.00159 |
| Sonnet 5 | $0.00007 | $0.00063 |
| Haiku 4.5 | $0.00004 | $0.00032 |
Grade A, and why
compress-context 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 6d 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
/compress-context — Summarize without losing critical facts
Purpose
Reduce noisy logs and transcripts to a compact summary that preserves every fact needed to continue.
Invocation
/compress-context
Accepted input
The logs, transcript, or output to compress.
Prerequisites
None.
Procedure
- Preserve exact failures, commands, filenames, line numbers, and assertions.
- Preserve API changes, decisions, and open risks verbatim.
- Summarize noisy or repetitive passages; never drop a unique fact.
- Separate observed facts, inferences, and uncertainty.
- State what was omitted and why.
Approval points
None.
Expected output
A compact summary that preserves every fact needed to continue the work.
Failure behavior
If a fact cannot be safely summarized, keep it verbatim.
Completion checks
- Exact errors, commands, paths, line numbers, decisions, constraints, and open work remain recoverable from the summary.
- Observations, inferences, and unknowns are labelled separately.
- The final section states the current objective, completed work, remaining work, and the next safe action so another agent can resume without the transcript.
Agents and skills
Skill: context-compression.
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.
- 6d ago First seen · 50 lines · 36 tokens per session scan A c7ad3e105105
compress-context is a command published in the GitHub repository Oriolshhh/runware-image-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 317 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.