mnemosyne-oss/mnemosyne is a local AI memory layer that stores agent memories in SQLite instead of relying on cloud services. It is intended for coding agents and other agent frameworks, and can be used through MCP or a Python SDK. The catalogue skills connect agents to Mnemosyne’s memory features and related workflows.
Borrowing it
Nothing to install: this file belongs to mnemosyne-oss/mnemosyne. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mnemosyne-oss/mnemosyne/main/.agents/skills/no-mistakes/SKILL.mdgit clone --depth 1 https://github.com/mnemosyne-oss/mnemosyneWrote 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/mnemosyne-oss/mnemosyne/no-mistakes)<a href="https://agentmods.dev/skills/mnemosyne-oss/mnemosyne/no-mistakes"><img src="https://agentmods.dev/badge/skills/mnemosyne-oss/mnemosyne/no-mistakes/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/mnemosyne-oss/mnemosyne/no-mistakes"><img src="https://agentmods.dev/badge/skills/mnemosyne-oss/mnemosyne/no-mistakes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 174 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00081 | $0.02815 |
| Opus 5 | $0.00041 | $0.01407 |
| Sonnet 5 | $0.00016 | $0.00563 |
| Haiku 4.5 | $0.00008 | $0.00281 |
Grade A, and why
no-mistakes 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 11d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
no-mistakes
no-mistakes is a local gate that validates your code changes through a pipeline
(intent, rebase, review, test, document, lint, push, PR, CI) before they reach
upstream. You drive it through the no-mistakes axi command family, which prints
machine-readable TOON to stdout and progress to stderr.
When the user invokes /no-mistakes, report the outcome at the end. If the user
asks for something specific, translate that request into the matching axi run
flags yourself - for example, "skip the lint step" becomes --skip=lint. Run
no-mistakes axi run --help to see the available flags.
Two ways to invoke
/no-mistakes works in two modes, depending on whether the user hands you a
task along with the command:
- Validate-only - bare
/no-mistakes(optionally with flag-style requests like "skip the lint step"). The user's code changes are already committed; validate them and report the outcome. - Task-first -
/no-mistakes <task>, e.g./no-mistakes add a --json flag to the status command. First carry out the task yourself, then validate the result through the pipeline:- Check scope. Inspect
git statusbefore you change or commit anything. Preserve unrelated pre-existing uncommitted changes, and when you commit, commit only the changes that belong to the user's task. - Do the work. Make the changes the task describes, then commit them on a feature branch. If the user is on the repository's default branch, create a feature branch first - the gate validates committed history on a non-default branch, so the work must land there before you run.
- Then validate, passing the user's task as your
--intent. The task text is exactly what the user set out to accomplish, in their own words, so it is the intent - pass it through, enriched with the decisions and tradeoffs you made while doing the work (see Intent is required).
- Check scope. Inspect
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.
- 11d ago First seen · 213 lines · 81 tokens per session scan A 1e8bc3843d2c
no-mistakes is a skill published in the GitHub repository mnemosyne-oss/mnemosyne (3,082 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 2,815 once invoked, about $0.0004 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 2026-08-30.
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adversarial-ux-test
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requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.