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 OZmasterAI/Torus-Framework --skill analyze-errorsgit clone --depth 1 https://github.com/OZmasterAI/Torus-FrameworkWrote 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/ozmasterai/torus-framework/analyze-errors)<a href="https://agentmods.dev/skills/ozmasterai/torus-framework/analyze-errors"><img src="https://agentmods.dev/badge/skills/ozmasterai/torus-framework/analyze-errors/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/ozmasterai/torus-framework/analyze-errors"><img src="https://agentmods.dev/badge/skills/ozmasterai/torus-framework/analyze-errors.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 | $0.00000 | $0.01210 |
| Opus 5.5 | $0.00000 | $0.00484 |
| Sonnet 5.5 | $0.00000 | $0.00242 |
| Haiku 4.5 | $0.00000 | $0.00121 |
Grade A, and why
analyze-errors 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze-errors — Recurring Error Deep Analysis
When to use
When the user says "analyze error", "why failing", "pattern", "recurring", "diagnose", "root cause", or wants to understand why an error keeps happening, find patterns in failures, or build a prevention strategy.
Complements /fix (immediate resolution) by doing historical pattern analysis across all memory to find systemic causes and build prevention playbooks.
Steps
1. DEEP QUERY
search_knowledge("[error pattern or message]", top_k=50)— get all historical instancessearch_knowledge("[error keywords]")— catch keyword matches the semantic search may misssearch_knowledge("[error pattern]", mode="all")— also includes auto-captured observations- Collect every memory entry related to this error for comprehensive analysis
2. TIMELINE
Organize findings chronologically:
- Sort all occurrences by timestamp
- Build a progression view:
Date | Occurrence | Context | Resolution -----------+------------+----------------------+------------------ 2026-01-15 | First seen | After deploying v2.1 | Manual fix 2026-01-22 | Recurred | Same endpoint | Same manual fix 2026-02-01 | Recurred | Different trigger | Attempted new fix 2026-02-10 | Still open | Expanded scope | Under investigation - Assess trend: getting worse, stable, improving, or resolved
- Note any periodicity (daily, weekly, after deploys, after specific operations)
3. ROOT CAUSE
Read full entries with get_memory(id) for each occurrence. Classify the error:
| Category | Indicators | Examples |
|---|---|---|
| Flaky test | Passes sometimes, fails sometimes; no code change between runs | Race condition in async test, timing-dependent assertion |
| Edge case | Specific input triggers it; works for most inputs | Unicode in filenames, empty arrays, null values |
| Environment | Works locally, fails in CI; or vice versa | Missing env var, different Python version, disk space |
| Race condition | Intermittent; depends on timing/ordering | Concurrent writes, async callback ordering |
| Integration failure | External dependency changed or unavailable | API version mismatch, service downtime, schema change |
| Regression | Worked before, broke after specific commit | Side effect of refactor, dependency update |
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 · 122 lines · 0 tokens per session scan A 3c0eda80b48a
analyze-errors is a skill published in the GitHub repository OZmasterAI/Torus-Framework (5 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,210 tokens. 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.
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