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 agentmods add skills/impactbrussels/ainativeos/self-healing-fallbacksnpx skills add impactbrussels/AINativeOS --skill self-healing-fallbacksgit clone --depth 1 https://github.com/impactbrussels/AINativeOSWrote 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/impactbrussels/ainativeos/self-healing-fallbacks)<a href="https://agentmods.dev/skills/impactbrussels/ainativeos/self-healing-fallbacks"><img src="https://agentmods.dev/badge/skills/impactbrussels/ainativeos/self-healing-fallbacks.svg" alt="Measured on agentmods" 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 | $0.00151 | $0.01505 |
| Opus 5 | $0.00076 | $0.00753 |
| Sonnet 5 | $0.00030 | $0.00301 |
| Haiku 4.5 | $0.00015 | $0.00151 |
Grade A, and why
self-healing-fallbacks 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 6d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Healing Fallbacks
Your workflow sits behind an API, and that API will sometimes be slow, down, or wrong. The failure that quietly kills pilots is not a crash; it is a confident wrong answer, or a dead end a user hits twice before going back to doing the job by hand. You did not lose them on capability. You lost them on reliability. Self-healing without a hard limit is just an infinite loop in a nicer coat, so the discipline is to retry within bounds, reroute within bounds, then fall to a fixed, boring, non-AI path that cannot guess fluently on a dose, an allergen, or anything irreversible.
The method
Map each critical path to a pattern, size what to automate, set the threshold that fires the floor. Full framework: references/fallback-patterns.md. Source: Handbook Chapter 09.
Step 1: List the critical paths and their reversibility
For each path the workflow depends on, write what it touches and whether a wrong answer can be undone. Reversibility, not value, decides how hard the fallback must be.
| Path touches | Reversibility | Fallback hardness |
|---|---|---|
| A screenshot, a draft, a suggestion | Fully reversible | Retry, then best-effort, log it |
| A stored record, an outbound message | Partly reversible | Retry, reroute, then conservative default |
| A dose, an allergen, a threshold, a trade, a release | Irreversible | No guess ever: deterministic floor + named human |
Step 2: Choose the pattern per path
Pick from the catalogue by risk and reversibility. The full decision table is in references.
| Pattern | Fires when | Behaviour |
|---|---|---|
| Retry-with-backoff | Transient failure (timeout, 429, overload) | Retry N times, widening gaps, then escalate |
| Confidence-gated fallback | Model confidence below threshold | Stop, drop to the floor, do not answer |
| Deterministic floor | High-risk path, any uncertainty | Fixed rule or conservative default, no model |
| Cached-last-good | Live call fails, a recent answer exists | Serve the last verified answer, flag it stale |
| Circuit breaker | Repeated failures on a dependency | Stop calling it, fail fast to the floor |
| Human escalation | Floor reached on an irreversible path | Hand to a named human with the context |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 88 lines · 151 tokens per session scan A 42eac58b8f67
self-healing-fallbacks is a skill published in the GitHub repository impactbrussels/AINativeOS (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 151 tokens to every session and 1,505 once invoked, about $0.0008 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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