akashi
Decision tracing workflow for AI agents using Akashi
Install / Use
npx skills add ashita-ai/akashiInstalls into whichever agent you are using.
Cursor Rules
Cursor IDE rules (v2)
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of akashi
akashi scores 44/100 on our quality scale, 2817th of 2,892 Automation skills we index.
Its Cursor Rules is 1.6 KB long, split into 6 sections and no code examples: moderately detailed.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- We could not determine when the repository was last updated.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 68/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-10. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
akashi compared with similar skills
All 4 of these similar skills score higher than akashi; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| akashi (this skill)by ashita-ai | 44 | 0 | — | Cursor Rules |
| Agent-Reachby Panniantong | 100 | 95.3k | 2d ago | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 86.7k | today | MCP Server |
| rufloby ruvnet | 100 | 74.2k | today | MCP Server |
| openclawby thedotmack | 100 | 97.1k | 3d ago | SKILL.md |
Frequently asked questions
- How do I install akashi?
- Run
npx skills add ashita-ai/akashi. The install tabs above show the steps for each supported agent. - Which AI agents does akashi work with?
- It is written for Cursor, as a Cursor Rules file. Other agents that read the same format can often use it too.
- Is akashi safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It declares no license and scores 68/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is akashi still maintained?
- We could not determine when the repository was last updated.
Skill content
View source on GitHubdescription: "Decision tracing workflow for AI agents using Akashi" alwaysApply: true
Akashi Decision Tracing
You have access to Akashi, a decision audit trail for AI agents.
Workflow
Follow this for every non-trivial decision:
-
BEFORE deciding: call
akashi_checkto look for prior decisions and active conflicts. Pass a natural language query describing what you're about to decide. Use the results to avoid contradicting prior work and to cite relevant precedents. -
AFTER deciding: call
akashi_tracewith what you decided (outcome), why (reasoning), your confidence (0.0-1.0), and project name. This creates a provable record so other agents can learn from it.
When to Check
- Choosing architecture or technology
- Starting a review or audit
- Making trade-offs between approaches
- Filing issues or PRs
- Changing existing behavior
When to Trace
- Completing a review
- Choosing an approach
- Creating issues or PRs
- Finishing a task that involved choices
- Making security or access judgments
When to Skip
- Pure execution (formatting, typo fixes)
- Reading or exploring code
- Asking the user a question (no decision yet)
Tools
akashi_check— look up precedents and conflicts before decidingakashi_trace— record a decision after making itakashi_query— filter or search the audit trailakashi_conflicts— list open conflicts between agentsakashi_assess— record whether a prior decision was correctakashi_stats— aggregate health metrics
Be honest about confidence. Reference precedents when they influence you.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.