Researcher Replan And Recovery
Use when the workflow hits contradictions, missing evidence, failed runs, design flaws, or resource shifts. Diagnose the failure class, choose the narrowest safe correction, and escalate to the user when the core plan changes.
Best use case
Researcher Replan And Recovery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when the workflow hits contradictions, missing evidence, failed runs, design flaws, or resource shifts. Diagnose the failure class, choose the narrowest safe correction, and escalate to the user when the core plan changes.
Teams using Researcher Replan And Recovery should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/researcher-replan-recovery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Researcher Replan And Recovery Compares
| Feature / Agent | Researcher Replan And Recovery | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when the workflow hits contradictions, missing evidence, failed runs, design flaws, or resource shifts. Diagnose the failure class, choose the narrowest safe correction, and escalate to the user when the core plan changes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Researcher Replan And Recovery Use this skill when the workflow is no longer safe to continue as planned. ## Diagnose First Classify the failure before acting: - requirement ambiguity; - literature or evidence gap; - experiment design flaw; - implementation defect; - execution or infrastructure failure; - result interpretation mismatch. ## Recovery Policy - Prefer the narrowest correction that preserves the approved objective. - Fix local failures locally when possible. - Use targeted evidence supplementation when a specific missing claim blocks progress. - Only escalate to broad re-planning when the active plan itself is no longer valid. ## User Escalation Rule If recovery changes the core objective, scope, time nodes, or resource assumptions, present a revised plan to the user before dispatching further work. ## Anti-Pattern Do not treat later-stage uncertainty as permission to restart the whole research lifecycle. The default recovery path is targeted repair, not workflow amnesia.
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