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 TalonT-Org/AutoSkillit --skill arch-lens-error-resiliencegit clone --depth 1 https://github.com/TalonT-Org/AutoSkillitWrote 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/talont-org/autoskillit/arch-lens-error-resilience)<a href="https://agentmods.dev/skills/talont-org/autoskillit/arch-lens-error-resilience"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/arch-lens-error-resilience/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/talont-org/autoskillit/arch-lens-error-resilience"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/arch-lens-error-resilience.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 | $0.00034 | $0.02557 |
| Opus 5 | $0.00017 | $0.01278 |
| Sonnet 5 | $0.00007 | $0.00511 |
| Haiku 4.5 | $0.00003 | $0.00256 |
Grade A, and why
arch-lens-error-resilience 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 10d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error/Resilience Architecture Lens
Cognitive Mode: Diagnostic Primary Question: "How are failures handled?" Focus: Error Propagation, Recovery Mechanisms, Circuit Breakers, Validation Gates
When to Use
- Need to understand error handling architecture
- Documenting recovery and retry mechanisms
- Analyzing validation gates and circuit breakers
- User invokes
/autoskillit:arch-lens-error-resilienceor/autoskillit:make-arch-diag error
Critical Constraints
NEVER:
- Modify any source code files
- Show happy path details (that's process flow lens)
- Ignore validation and fail-fast patterns
- Run subagents in the background (
run_in_background: trueis prohibited)
ALWAYS:
- Focus on FAILURE paths and recovery
- Show validation gates and their failure modes
- Document retry limits and circuit breakers
- Include exception hierarchy if present
- BEFORE creating any diagram, LOAD the
/autoskillit:mermaidskill using the Skill tool - this is MANDATORY - If the Skill tool cannot be used (disable-model-invocation) or refuses this invocation, do NOT proceed with diagram creation. Abort this step and omit the diagram from output.
- After writing the diagram file, emit the absolute path as a structured output
token as your final output. Resolve the relative
temp/arch-lens-error-resilience/...save path to absolute by prepending the full CWD:
This token is MANDATORY — the pipeline cannot proceed without it.diagram_path = /absolute/cwd/temp/arch-lens-error-resilience/{filename}.md
Arguments
/autoskillit:arch-lens-error-resilience [context_path]
- context_path (optional) — Absolute path to a PR context file containing new files (★-prefixed) and modified files (●-prefixed) from the PR diff. When provided, read this file before beginning analysis and focus the diagram on the architectural areas affected by these specific files. When absent, explore the full CWD.
Analysis Workflow
Step 0: Read PR context (when provided)
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.
- 10d ago First seen · 310 lines · 34 tokens per session scan A 3ce5d86e3afa
arch-lens-error-resilience is a skill published in the GitHub repository TalonT-Org/AutoSkillit (5 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 2,557 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.
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