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 Hoja-Solutions/agent-stdlib --skill agent-reliability-and-change-managementgit clone --depth 1 https://github.com/Hoja-Solutions/agent-stdlibWrote 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/hoja-solutions/agent-stdlib/agent-reliability-and-change-management)<a href="https://agentmods.dev/skills/hoja-solutions/agent-stdlib/agent-reliability-and-change-management"><img src="https://agentmods.dev/badge/skills/hoja-solutions/agent-stdlib/agent-reliability-and-change-management/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/hoja-solutions/agent-stdlib/agent-reliability-and-change-management"><img src="https://agentmods.dev/badge/skills/hoja-solutions/agent-stdlib/agent-reliability-and-change-management.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 | $0.00204 | $0.00875 |
| Opus 5 | $0.00102 | $0.00438 |
| Sonnet 5 | $0.00041 | $0.00175 |
| Haiku 4.5 | $0.00020 | $0.00088 |
Grade A, and why
agent-reliability-and-change-management 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 8d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent reliability and change management
Source: A postmortem of three recent issues, an update on recent Claude Code quality reports, and Project Vend. durable-agent-architecture keeps the service alive when a part crashes; this skill keeps the agent good after you change it and over a long run.
Gate every change behind an eval run
A one-line system-prompt edit can drop quality across the board, and you will not catch it by reading a few transcripts. Run a broad eval suite for every system-prompt or model change before it ships, and ablate to find which change moved the number. A model recovers well from a single mistake, so a regression hides until you measure it at scale.
Scope a change to its target
A change meant for one model can degrade another. Gate model-specific edits to the model they target, route by an exact match, and test that routing on the boundary cases (idle sessions, state transitions, the moment a cache fills) that skip your normal review.
Roll out in stages and watch
Ship to a slice of traffic first, hold it there long enough to read the signal, then widen. Wire a feedback path, such as a /bug command or a thumbs-down, and watch for a spike in reports that lines up with a deploy. A correlated spike is your fastest regression detector.
Treat reasoning state as an invariant
A cache change that dropped the agent's thinking blocks each turn made it forgetful and repetitive. The reasoning history is load-bearing. When you optimize the request path, assert that thinking and tool state survive the change, and add an integration test on the turn-to-turn boundary where caching bugs hide.
Give a long run external memory
A long-running agent that leans on its context window drifts: it loses track of earlier commitments and starts to confuse its own state. Project Vend's shop agent invented details and lost the thread over days. Give it a place to write decisions and read them back, such as a log or a record store it queries, in place of trusting a longer window to hold everything.
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.
- 8d ago First seen · 54 lines · 204 tokens per session scan A 711478631f4d
agent-reliability-and-change-management is a skill published in the GitHub repository Hoja-Solutions/agent-stdlib (1 stars, last pushed 1mo ago), licensed MIT. It adds 204 tokens to every session and 875 once invoked, about $0.0010 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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