Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
5-level error recovery escalation: retry, nudge, replan, fallback, force-done — each with entry criteria and timeout before escalating
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Provide a structured, 5-level escalation ladder for recovering from agent errors. Each level has specific entry criteria, an action, and a timeout before escalating to the next level.
Level 1: RETRY — Same action, transient error
Level 2: NUDGE — Adjust parameters, same approach
Level 3: REPLAN — New approach, same goal
Level 4: FALLBACK — Different agent or model
Level 5: FORCE-DONE — Partial results with explanation
Skill({ skill: 'error-recovery-escalation' });
Invoke when:
behavioral-loop-detection)Entry criteria:
Action:
Timeout: 3 attempts × backoff = max ~30 seconds total Escalate to Level 2 when: 3 retries exhausted without success
Example:
Read('.claude/context/data/index.db') → ENOENT
→ Level 1: Retry 3 times with 1s backoff
→ Still fails → escalate to Level 2
Entry criteria:
Action:
Timeout: 5 minutes total for all nudge variants Escalate to Level 3 when: All nudge variants fail or timeout expires
Example:
Write('output/result.json', ...) → EPERM
→ Level 2: Try .claude/context/tmp/result.json, then /tmp/result.json
→ All fail → escalate to Level 3
Entry criteria:
Action:
planner agent or invoke plan-generator skill for a new planTimeout: 15 minutes for new plan + initial execution Escalate to Level 4 when: Replan also fails, or planner cannot produce a viable plan
Example:
Auth middleware implementation fails repeatedly
→ Level 3: Replan with different auth library (passport → jose)
→ New plan also fails due to dependency conflict → escalate to Level 4
Entry criteria:
Action:
developer → devops-troubleshooter)Timeout: 20 minutes for fallback agent execution Escalate to Level 5 when: Fallback agent also fails or timeout expires
Example:
Developer agent cannot resolve platform-specific build error
→ Level 4: Spawn devops-troubleshooter with full error log
→ Still unresolved after 20min → escalate to Level 5
Entry criteria:
Action:
{ partial: true, escalationLevel: 5, explanation: '...' }Output contract:
{
"status": "partial",
"completedSteps": ["step1", "step2"],
"failedAt": "step3",
"failureReason": "<specific error>",
"escalationPath": [1, 2, 3, 4, 5],
"recommendation": "<what a human should do to finish>"
}
Example:
External API service is down
→ Level 5: Emit partial results (all non-API steps completed)
→ TaskUpdate with partial: true, explanation of API outage
→ Recommend retry when service is restored
| Error Type | Entry Level | Rationale |
|---|---|---|
| Network timeout | 1 (retry) | Transient, safe to retry |
| Rate limit | 1 (retry with backoff) | Transient, wait and retry |
| File not found | 2 (nudge) | Wrong path, try alternatives |
| Permission denied | 2 (nudge) | Wrong path/permissions |
| Wrong output format | 2 (nudge) | Adjust input parameters |
| Goal misalignment (judge FAIL) | 3 (replan) | Need different approach |
| Agent capability mismatch | 4 (fallback) | Need different agent |
| External service down | 5 (force-done) | Cannot resolve programmatically |
| Missing credentials | 5 (force-done) | Requires human intervention |
Every level transition MUST be recorded:
// On entering a level
TaskUpdate({
taskId: '<task-id>',
status: 'in_progress',
metadata: {
recoveryLevel: <1-5>,
recoveryAction: 'retry|nudge|replan|fallback|force-done',
errorType: '<classification>',
previousLevels: [<completed levels>],
enteredAt: new Date().toISOString(),
}
});
// On Level 5 completion
TaskUpdate({
taskId: '<task-id>',
status: 'completed',
metadata: {
partial: true,
escalationLevel: 5,
escalationPath: [1, 2, 3, 4, 5],
completedSteps: ['<step1>', '<step2>'],
failedAt: '<step>',
failureReason: '<specific error>',
recommendation: '<human follow-up action>',
summary: 'Force-done: partial results emitted after 5-level escalation',
}
});
| Skill | Relationship |
|---|---|
behavioral-loop-detection | Feeds escalation triggers (REPLAN at 3, EXPLORE at 5, FORCE-DONE at 8) |
judge-verification | FAIL verdict triggers escalation at appropriate level |
plan-generator | Called at Level 3 (replan) to produce new approach |
verification-before-completion | Gates completion before escalation is declared unnecessary |
debugging | Provides root cause analysis before Level 3 escalation |
Input validated against schemas/input.schema.json before execution.
Output contract defined in schemas/output.schema.json.
Pre-execution hook: hooks/pre-execute.cjs
Post-execution hook: hooks/post-execute.cjs (emits observability event)
Before starting:
Read .claude/context/memory/learnings.md for known error patterns in this codebase.
Check .claude/context/memory/issues.md for known blockers.
After completing:
.claude/context/memory/issues.md with root cause.claude/context/memory/learnings.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
日本語の概要は準備中です。原文の説明を表示しています。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
日本語の概要は準備中です。原文の説明を表示しています。