OpenCompany is an application for creating AI employees that work on assigned jobs in the background on the user's computer, using teams of specialized agents coordinated by a lead agent. People connect these employees to tools such as email, calendars, messages, and code, then define their workflows through a visual editor.
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 zeenie-ai/OpenCompany --skill compaction-skillgit clone --depth 1 https://github.com/zeenie-ai/OpenCompanyWrote 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/zeenie-ai/opencompany/compaction-skill)<a href="https://agentmods.dev/skills/zeenie-ai/opencompany/compaction-skill"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/compaction-skill/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/zeenie-ai/opencompany/compaction-skill"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/compaction-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01001 |
| Opus 5.5 | $0.00007 | $0.00400 |
| Sonnet 5.5 | $0.00004 | $0.00200 |
| Haiku 4.5 | $0.00002 | $0.00100 |
Grade A, and why
compaction-skill 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Compaction Skill
You have the ability to compact conversation memory when it grows too large. Compaction transforms verbose conversation history into a structured summary that preserves essential context while reducing token usage.
When to Compact
Compact memory when:
- The system indicates token threshold is approaching
- Conversation history becomes repetitive or verbose
- You need to preserve important context but reduce size
- Starting a new phase of work after completing a major task
Compaction Summary Structure
When compacting, create a summary with these 5 sections:
1. Task Overview
What the user is trying to accomplish. Include:
- Primary goal or objective
- Key constraints or requirements
- Scope of the work
2. Current State
What's been completed and what's in progress:
- Completed tasks and their outcomes
- Work currently in flight
- Pending decisions or blockers
3. Important Discoveries
Key findings, decisions, or problems encountered:
- Technical discoveries or insights
- Decisions made and their rationale
- Problems encountered and solutions applied
- User preferences learned
4. Next Steps
What needs to happen next:
- Immediate actions required
- Planned approach for remaining work
- Dependencies or prerequisites
5. Context to Preserve
Critical details that must be retained:
- Specific values, IDs, or references
- User preferences or constraints
- Technical details needed for continuity
- Any warnings or caveats
Compaction Format
Output the compacted summary in this format:
# Conversation Summary (Compacted)
*Generated: [ISO timestamp]*
## Task Overview
[1-3 sentences describing the goal]
## Current State
- [Completed item 1]
- [Completed item 2]
- [In progress: description]
## Important Discoveries
- [Discovery 1 with context]
- [Decision made: rationale]
- [Problem solved: approach]
## Next Steps
1. [Next action]
2. [Following action]
## Context to Preserve
- [Critical detail 1]
- [Critical detail 2]
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 · 159 lines · 18 tokens per session scan A e64aed664433
compaction-skill is a skill published in the GitHub repository zeenie-ai/OpenCompany (1,037 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 1,001 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 2026-08-30.
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