From ritual-dapp-skills
Biases agent to resolve autonomously: search skills/code/docs/context before questioning user. Tracks turn budgets, flags low-confidence decisions, announces routine actions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ritual-dapp-skills:ritual-meta-non-interactive-biasThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Steer the agent toward autonomous resolution over interactive questioning. The default mode is: resolve it yourself. The exception mode is: ask the user, through `skills/ritual-meta-human-in-loop/SKILL.md`.
Steer the agent toward autonomous resolution over interactive questioning. The default mode is: resolve it yourself. The exception mode is: ask the user, through skills/ritual-meta-human-in-loop/SKILL.md.
Before asking the user, exhaust these in order:
Confidence gate: If the self-resolved answer has low confidence (the skill mentions something "might" work, the chain query returns ambiguous data), proceed with it BUT flag it:
I'm going with [approach] based on [source]. This might not be exactly right for your case —
if the result looks off, let me know and I'll adjust.
This is "graduated non-interactivity" — neither fully blocking nor silently wrong.
Every time the agent is about to generate a question, first generate the search that would answer it:
About to ask: "[question]"
Self-resolution attempt:
Skill search: [which skill, what keyword] → [found / not found]
Chain query: [what contract/function] → [result / N/A]
User's code: [which file, what pattern] → [found / not found]
Context inference: [what prior information] → [sufficient / insufficient]
Resolution: [answer found — proceed] or [genuinely unresolvable — ask via human-in-loop]
Token costs are not observable in most harnesses. Turns are. Ask the user early (after the build plan is generated, not before):
The build plan has roughly [N] steps. At current pace, that's about [M] back-and-forths.
Does that sound reasonable, or should I aim to be more concise?
A: That's fine, take your time
B: Try to be more concise
C: I have a hard limit of [X] turns/dollars
D: Don't care — just get it right
Track the ratio: remaining_turns_estimate / remaining_steps.
| Ratio | Behavior |
|---|---|
| > 2.0 | Slack — full verification, exploratory tangents OK |
| 1.0–2.0 | Normal — standard verification, stay on-plan |
| 0.5–1.0 | Tight — lint-only verification, skip nice-to-haves |
| < 0.5 | Critical — stop, summarize what's done, hand off cleanly |
| Data | Cache Duration | Rationale |
|---|---|---|
| Executor list from TEEServiceRegistry | 100 blocks (~35s at ~350ms baseline) | Registrations change infrequently |
| RitualWallet balance | 10 blocks (~3.5s at ~350ms baseline) | Deposits/withdrawals are user-initiated |
| Sender lock state | Never cache | Changes with every async tx submit/settle |
| Contract deployment (cast code) | Permanent once confirmed | Contracts don't un-deploy |
| Block number / chain connectivity | 1 block | Health check — always fresh |
Between "silently do it" and "ask the user," there's a middle state: announce what you're doing without requesting approval.
I'm setting up Scheduler chaining to handle your two-step workflow
(HTTP fetch → LLM analysis). This is the standard approach since
Ritual allows only one short-running async precompile call per transaction.
Use inform-without-asking for:
The user can object ("actually, I'd rather...") but the agent doesn't block waiting for approval.
Despite the non-interactive bias, always ask for:
skills/ritual-meta-human-in-loop/SKILL.md.skills/ritual-meta-circuit-breaker/SKILL.md.npx claudepluginhub ritual-foundation/ritual-dapp-skills --plugin ritual-dapp-skillsBootstraps 10 protocols governing Ritual Chain agent workflows: cost tracking, distrusting Ethereum priors, lazy elicitation, interleaved build/debug cycles, and progress monitoring.
Use when starting any conversation - establishes how to locate and invoke skills, mandating Skill tool usage before ANY response including clarifying questions
Guides AI agents on when to ask clarifying questions vs. proceed autonomously using checklists and decision matrices to reduce unnecessary interaction rounds.