SkillOpt v0.1.0: initial release

- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends

skillopt/prompts/__init__.py

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+"""Prompt loading utilities for ReflACT.
+
+Prompts are stored as ``.md`` files and loaded at runtime:
+
+- **Generic** prompts live in ``skillopt/prompts/*.md``
+- **Env-specific** prompts live in ``skillopt/envs/<env>/prompts/*.md``
+
+``load_prompt(name, env)`` tries the env-specific path first, then falls
+back to the generic default.
+"""
+from __future__ import annotations
+
+import os
+
+_PROMPTS_DIR = os.path.dirname(os.path.abspath(__file__))
+_REFLACT_DIR = os.path.dirname(_PROMPTS_DIR)
+
+_cache: dict[str, str] = {}
+
+
+def _read_file(path: str) -> str | None:
+    if path in _cache:
+        return _cache[path]
+    if not os.path.isfile(path):
+        return None
+    with open(path, encoding="utf-8") as f:
+        content = f.read()
+    _cache[path] = content
+    return content
+
+
+def load_prompt(name: str, env: str | None = None) -> str:
+    """Load a prompt by name with env-specific override and generic fallback.
+
+    Lookup order:
+      1. ``skillopt/envs/{env}/prompts/{name}.md``  (if *env* given)
+      2. ``skillopt/prompts/{name}.md``              (generic default)
+
+    Raises ``FileNotFoundError`` if neither path exists.
+    """
+    if env is not None:
+        env_path = os.path.join(_REFLACT_DIR, "envs", env, "prompts", f"{name}.md")
+        content = _read_file(env_path)
+        if content is not None:
+            return content
+
+    generic_path = os.path.join(_PROMPTS_DIR, f"{name}.md")
+    content = _read_file(generic_path)
+    if content is not None:
+        return content
+
+    searched = []
+    if env is not None:
+        searched.append(os.path.join("skillopt/envs", env, "prompts", f"{name}.md"))
+    searched.append(f"skillopt/prompts/{name}.md")
+    raise FileNotFoundError(
+        f"Prompt '{name}' not found. Searched: {', '.join(searched)}"
+    )
+
+
+def clear_cache() -> None:
+    """Clear the prompt file cache (useful for testing)."""
+    _cache.clear()

skillopt/prompts/analyst_error.md

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+You are an expert failure-analysis agent for AI agent tasks.
+
+You will be given MULTIPLE failed agent trajectories from a single minibatch
+and the current skill document.
+Your job is to identify the most important COMMON failure patterns across
+the batch and propose a concise set of skill edits.
+
+## Analysis Process
+1. Read ALL trajectories in the minibatch.
+2. Identify the most prevalent, systematic failure patterns across them.
+3. For each pattern, classify its failure type.
+4. Propose skill edits that address the COMMON patterns — not individual edge cases.
+5. Edits must be generalizable; do not hardcode task-specific values.
+6. Only patch gaps in the skill — do not duplicate existing content.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the highest-impact patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<why these edits address the batch's common failures>",
+    "edits": [
+      {"op": "append",       "content": "<markdown to add at end of skill>"},
+      {"op": "insert_after", "target": "<exact heading/text to insert after>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<exact text to replace>",              "content": "<replacement>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+Only include edits that are needed. "edits" can be an empty list if no patch is warranted.
+
+IMPORTANT: The skill document may contain a section between
+<!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+This is a PROTECTED section managed by a separate slow-update process.
+Do NOT propose any edits that target, modify, or delete content within
+these markers.

skillopt/prompts/analyst_error_full_rewrite.md

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+You will be given several failed agent trajectories from one minibatch and the current skill document.
+
+Summarize the lessons from these trajectories into one complete replacement skill document.
+
+When rewriting from a minibatch, use the current trajectories as the primary
+evidence for updates. Preserve essential task-format instructions, but avoid mechanically carrying over
+stale, redundant, or conflicting rules. Prefer a concise, coherent replacement
+skill over a long document with weakly supported guidance.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<brief summary of the rewrite>",
+    "skill_candidates": [
+      {
+        "title": "<short title>",
+        "change_summary": ["<short change 1>", "<short change 2>"],
+        "new_skill": "<complete rewritten skill document>"
+      }
+    ]
+  }
+}
+
+Return exactly one item in "skill_candidates".

skillopt/prompts/analyst_error_rewrite.md

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+You are an expert failure-analysis agent for AI agent tasks.
+
+You will be given MULTIPLE failed agent trajectories from a single minibatch
+and the current skill document.
+Your job is to identify the most important COMMON failure patterns across
+the batch and propose a concise set of skill-revision suggestions.
+
+## Analysis Process
+1. Read ALL trajectories in the minibatch.
+2. Identify the most prevalent, systematic failure patterns across them.
+3. For each pattern, classify its failure type.
+4. Propose revision suggestions that address the COMMON patterns, not individual edge cases.
+5. Suggestions must be generalizable and should help a later teacher rewrite the full skill document.
+6. Do not hardcode task-specific values.
+
+You will be told the maximum number of suggestions (the budget L). Produce AT MOST L suggestions,
+focusing on the highest-impact patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<why these suggestions address the batch's common failures>",
+    "revise_suggestions": [
+      {
+        "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+        "title": "<short title>",
+        "motivation": "<why this matters>",
+        "instruction": "<what the rewriting teacher should change in the skill>",
+        "priority_hint": "high|medium|low"
+      }
+    ]
+  }
+}
+"revise_suggestions" may be an empty list if no revision is warranted.
+
+IMPORTANT: The skill document may contain a section between
+<!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+This is a PROTECTED section managed by a separate slow-update process.
+Do NOT propose suggestions that target, modify, or delete content within
+these markers.

skillopt/prompts/analyst_success.md

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+You are an expert success-pattern analyst for AI agents.
+
+You will be given MULTIPLE successful agent trajectories from a single minibatch
+and the current skill document. Your job is to identify generalizable behavior
+patterns that are COMMON across the batch and worth encoding in the skill.
+
+## Rules
+- Only propose patches for patterns NOT already covered in the skill.
+- Focus on patterns that appear across MULTIPLE trajectories in the batch.
+- Be concise. Patterns must generalize beyond specific tasks.
+- Prefer reinforcing existing sections over adding new top-level sections.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the most broadly applicable patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "success_patterns": ["<pattern 1>", "<pattern 2>"],
+  "patch": {
+    "reasoning": "<why these patterns are worth encoding>",
+    "edits": [
+      {"op": "append",       "content": "<markdown>"},
+      {"op": "insert_after", "target": "<heading/text>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<old text>",     "content": "<new text>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+"edits" may be empty if the skill already covers all observed patterns.
+
+IMPORTANT: The skill document may contain a section between
+<!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+This is a PROTECTED section managed by a separate slow-update process.
+Do NOT propose any edits that target, modify, or delete content within
+these markers.

skillopt/prompts/analyst_success_full_rewrite.md

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+You will be given several successful agent trajectories from one minibatch and the current skill document.
+
+Summarize any useful lessons from these trajectories into one complete replacement skill document.
+
+When rewriting from a minibatch, use the current trajectories as the primary
+evidence for updates. Preserve essential task-format instructions, but avoid mechanically carrying over
+stale, redundant, or conflicting rules. Prefer a concise, coherent replacement
+skill over a long document with weakly supported guidance.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "success_patterns": ["<pattern 1>", "<pattern 2>"],
+  "patch": {
+    "reasoning": "<brief summary of the rewrite>",
+    "skill_candidates": [
+      {
+        "title": "<short title>",
+        "change_summary": ["<short change 1>", "<short change 2>"],
+        "new_skill": "<complete rewritten skill document>"
+      }
+    ]
+  }
+}
+
+Return exactly one item in "skill_candidates".

skillopt/prompts/analyst_success_rewrite.md

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+You are an expert success-pattern analyst for AI agent tasks.
+
+You will be given MULTIPLE successful agent trajectories from a single minibatch
+and the current skill document. Your job is to identify broadly useful patterns
+worth preserving in a later full-skill rewrite.
+
+## Rules
+- Only propose revise_suggestions for patterns NOT already covered in the skill.
+- Focus on patterns that appear across MULTIPLE trajectories in the batch.
+- Keep suggestions general, concise, and rewrite-friendly.
+- Prefer guidance that improves organization, clarity, or reusable behavior.
+
+You will be told the maximum number of suggestions (the budget L). Produce AT MOST L suggestions,
+focusing on the most broadly applicable patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "success_patterns": ["<pattern 1>", "<pattern 2>"],
+  "patch": {
+    "reasoning": "<why these suggestions are worth encoding>",
+    "revise_suggestions": [
+      {
+        "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+        "title": "<short title>",
+        "motivation": "<why this matters>",
+        "instruction": "<what the rewriting teacher should change in the skill>",
+        "priority_hint": "high|medium|low"
+      }
+    ]
+  }
+}
+"revise_suggestions" may be empty if the skill already captures all useful patterns.

skillopt/prompts/deep_probe.md

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+You are an expert diagnostic-probe designer for reflective skill learning.
+
+You will design one short diagnostic instruction to append to the student prompt
+for a handful of representative cases.
+
+The goal is to expose the student's current intermediate judgment state without
+substantially changing the current skill scaffold.
+
+## Hard Constraints
+1. Do NOT substantially change the student's existing scaffold.
+2. Do NOT prescribe a new multi-step solving procedure.
+3. Do NOT ask for exhaustive enumeration, full chain-of-thought, or a long derivation.
+4. Ask only for a minimal readout of signals already behind the student's current answer.
+5. Keep the diagnostic block brief and structured.
+6. The final answer must still be produced in <answer>...</answer>.
+7. If hidden reference material is provided, use it only to target the right latent gap.
+8. Never copy hidden reference content into the student-facing probe.
+
+## Good Probe Targets
+- top candidate and runner-up
+- decisive cue / decisive constraint
+- why a runner-up was rejected
+- counted unit / suspicious region / compared objects
+
+## Bad Probe Targets
+- full proof or full chain-of-thought
+- dumping every object, cell, or possibility
+- imposing a brand-new solving algorithm
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this probe reveals the latent skill gap>",
+  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+}

skillopt/prompts/deep_probe_codex.md

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+You are an expert diagnostic-probe designer for codex-executed student trajectories.
+
+You will be shown representative trajectories, the current student skill, the student's original prompt context, and numbered Codex trace steps.
+Some trajectories may also include a hidden Reference block. Use hidden reference only to identify the student's missing subgoal, theorem, evidence source, or decisive transformation. Do not reveal or paraphrase that reference directly to the student.
+
+Choose exactly one trajectory and one probe point. The probe point determines how much of the prior Codex trace will be shown back to the student before asking a short diagnostic question.
+
+## Hard Constraints
+1. Do NOT reveal or paraphrase hidden reference content to the student.
+2. Do NOT prescribe a new full solving procedure.
+3. Do NOT ask for a full proof, full chain-of-thought, exhaustive listing, or complete plan.
+4. Ask only for a short readout of the student's intermediate state that should already exist at that point.
+5. The probe instruction must preserve the original output scaffold and final task.
+6. The probe instruction should be ready to append directly to the student's prompt.
+
+## Probe Point Semantics
+- `probe_target_id` must be one of the shown trajectory ids.
+- `probe_after_step` is the last numbered Codex trace step that should remain in the student's context.
+- The student will be re-run with the raw trace up to and including `probe_after_step`, then asked your `probe_instruction`.
+- To probe before a tool call, choose the step immediately before that tool call.
+
+## Good Probe Targets
+- next theorem / subgoal / evidence source
+- strongest-vs-runner-up option distinction
+- decisive constraint or transformation
+- why a tempting alternative is being rejected
+- what code region / spreadsheet region / image cue / passage evidence matters next
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this trajectory and probe point expose the student's intermediate state>",
+  "probe_target_id": "<trajectory id>",
+  "probe_after_step": <integer step number>,
+  "probe_instruction": "<the exact instruction text to append to the student's prompt>"
+}

skillopt/prompts/lr_autonomous.md

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+You are an update-size controller for a skill-learning system.
+
+You will receive:
+1. The current skill document.
+2. A pool of proposed update items distilled from the current training step.
+3. Brief evidence about the current rollout and training step.
+
+Your job is to decide how many update items should be applied in this step.
+Use only the evidence shown in the prompt. Do not assume any default update
+size, previous convention, external preference, or unstated decision rule.
+
+Do not rank the update items. Only decide the count.
+
+Respond ONLY with a valid JSON object:
+{
+  "learning_rate": <non-negative integer>,
+  "reasoning": "<brief evidence-based reason>",
+  "confidence": "low|medium|high",
+  "risk_notes": ["<short note>", "..."]
+}

skillopt/prompts/merge_failure.md

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+You are a skill-edit coordinator. You receive multiple independently-proposed patches
+from FAILURE analysis of agent trajectories. Merge them into ONE coherent, non-redundant patch.
+
+Merge guidelines:
+1. **Deduplicate**: keep the best-worded version of similar edits.
+2. **Resolve conflicts**: if patches contradict on the same point,
+   choose the one with stronger justification or synthesize both.
+3. **Preserve unique insights**: include all non-redundant corrective edits.
+4. **Prevalent-pattern bias**: edits appearing consistently across multiple patches
+   address systematic failures — preserve them with HIGH priority.
+   Edits from only one patch may be discarded if task-specific.
+5. **Independence**: no two edits in the merged patch may target the same text region.
+6. **Support count**: for each merged edit, estimate how many source patches support it.
+7. **PROTECTED SECTION**: The skill may contain a section between
+   <!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+   Do NOT merge or produce any edits that target content within these markers.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary of key consolidation decisions>",
+    "edits": [
+    {
+      "op": "append|insert_after|replace|delete",
+      "target": "<if insert_after or replace or delete>",
+      "content": "<markdown>",
+      "support_count": <integer>,
+      "source_type": "failure"
+    }
+  ]
+}

skillopt/prompts/merge_failure_full_rewrite.md

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+You will be given complete skill candidates written from failed trajectories and the current skill document.
+
+Combine them into one complete replacement skill document.
+
+When merging full-skill candidates, preserve essential task-format instructions,
+but do not mechanically retain stale, redundant, or
+conflicting rules. If candidates disagree, prefer the concise rule with clearer
+trajectory support and better consistency with the replacement skill.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the current skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief summary of how the candidates were combined>",
+  "skill_candidates": [
+    {
+      "title": "<short title>",
+      "change_summary": ["<short change 1>", "<short change 2>"],
+      "new_skill": "<complete merged skill document>",
+      "support_count": <integer>,
+      "source_type": "failure"
+    }
+  ]
+}
+
+Return exactly one item in "skill_candidates".

skillopt/prompts/merge_failure_rewrite.md

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+You are a skill-revision coordinator. You receive multiple independently-proposed
+revision suggestion sets from FAILURE analysis of agent trajectories. Merge them
+into ONE coherent, non-redundant set of revise_suggestions.
+
+Merge guidelines:
+1. Deduplicate overlapping suggestions.
+2. Resolve conflicts by keeping the more general, better-justified direction.
+3. Preserve unique high-impact corrective insights.
+4. Suggestions supported by many source patches should receive higher support_count.
+5. The output suggestions should help a later teacher rewrite the full skill.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary of consolidation decisions>",
+  "revise_suggestions": [
+    {
+      "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+      "title": "<short title>",
+      "motivation": "<why this matters>",
+      "instruction": "<what the rewriting teacher should change in the skill>",
+      "priority_hint": "high|medium|low",
+      "support_count": <integer>,
+      "source_type": "failure"
+    }
+  ]
+}

skillopt/prompts/merge_final.md

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+You are a skill-edit coordinator performing the FINAL merge. You receive two
+pre-merged patch groups:
+1. **Failure-driven patches** (corrective, high priority)
+2. **Success-driven patches** (reinforcement, lower priority)
+
+Merge guidelines:
+1. **FAILURE PATCHES TAKE PRIORITY**: the primary goal of skill reflection is to
+   fix failures. Failure-driven edits should be preserved unless they directly
+   conflict with a well-supported success pattern.
+2. **Deduplicate**: if a failure edit and success edit cover the same point,
+   keep the failure version.
+3. **Preserve success insights**: include success edits that cover patterns
+   NOT addressed by failure edits.
+4. **Higher-level merges represent broader consensus**: edits that survived
+   previous merge rounds (higher level) should be given priority.
+5. **Carry forward support_count and source_type for each edit.**
+6. **PROTECTED SECTION**: The skill may contain a section between
+   <!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+   Do NOT merge or produce any edits that target content within these markers.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary of priority decisions>",
+  "edits": [
+    {
+      "op": "append|insert_after|replace|delete",
+      "target": "<if needed>",
+      "content": "<markdown>",
+      "support_count": <integer>,
+      "source_type": "failure|success"
+    }
+  ]
+}

skillopt/prompts/merge_final_full_rewrite.md

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+You will be given complete skill candidates and the current skill document.
+
+Combine them into one complete replacement skill document.
+
+When merging full-skill candidates, preserve essential task-format instructions,
+but do not mechanically retain stale, redundant, or
+conflicting rules. Prefer concise guidance with clear trajectory support and
+better consistency with the replacement skill.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the current skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief summary of how the candidates were combined>",
+  "skill_candidates": [
+    {
+      "title": "<short title>",
+      "change_summary": ["<short change 1>", "<short change 2>"],
+      "new_skill": "<complete final skill document>",
+      "support_count": <integer>,
+      "source_type": "failure|success|mixed"
+    }
+  ]
+}
+
+Return exactly one item in "skill_candidates".

skillopt/prompts/merge_final_rewrite.md

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+You are a skill-revision coordinator performing the FINAL merge. You receive:
+1. Failure-driven revise_suggestions (higher priority)
+2. Success-driven revise_suggestions (lower priority)
+
+Merge guidelines:
+1. Failure-driven suggestions take priority when they overlap.
+2. Keep success-driven suggestions that add distinct value.
+3. Prefer general, rewrite-friendly, non-redundant suggestions.
+4. Carry forward support_count and source_type.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary of priority decisions>",
+  "revise_suggestions": [
+    {
+      "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+      "title": "<short title>",
+      "motivation": "<why this matters>",
+      "instruction": "<what the rewriting teacher should change in the skill>",
+      "priority_hint": "high|medium|low",
+      "support_count": <integer>,
+      "source_type": "failure|success"
+    }
+  ]
+}

skillopt/prompts/merge_success.md

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+You are a skill-edit coordinator. You receive multiple independently-proposed patches
+from SUCCESS analysis of agent trajectories. Merge them into ONE coherent patch
+that reinforces effective patterns.
+
+Merge guidelines:
+1. **Deduplicate**: keep only the most generalizable version of similar patterns.
+2. **Be conservative**: success-driven patches reinforce existing behavior.
+   Only include edits for patterns NOT already in the skill.
+3. **Prevalent-pattern bias**: patterns seen across many successful trajectories
+   are most worth encoding.
+4. **Support count**: estimate how many source patches support each merged edit.
+5. **PROTECTED SECTION**: The skill may contain a section between
+   <!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> markers.
+   Do NOT merge or produce any edits that target content within these markers.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary>",
+  "edits": [
+    {
+      "op": "append|insert_after|replace|delete",
+      "target": "<if needed>",
+      "content": "<markdown>",
+      "support_count": <integer>,
+      "source_type": "success"
+    }
+  ]
+}

skillopt/prompts/merge_success_full_rewrite.md

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+You will be given complete skill candidates written from successful trajectories and the current skill document.
+
+Combine them into one complete replacement skill document.
+
+When merging full-skill candidates, preserve essential task-format instructions,
+but do not mechanically retain stale, redundant, or
+conflicting rules. If candidates disagree, prefer the concise rule with clearer
+trajectory support and better consistency with the replacement skill.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the current skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief summary of how the candidates were combined>",
+  "skill_candidates": [
+    {
+      "title": "<short title>",
+      "change_summary": ["<short change 1>", "<short change 2>"],
+      "new_skill": "<complete merged skill document>",
+      "support_count": <integer>,
+      "source_type": "success"
+    }
+  ]
+}
+
+Return exactly one item in "skill_candidates".

skillopt/prompts/merge_success_rewrite.md

@@ -0,0 +1,25 @@
+You are a skill-revision coordinator. You receive multiple independently-proposed
+revision suggestion sets from SUCCESS analysis of agent trajectories. Merge them
+into ONE coherent, non-redundant set of revise_suggestions.
+
+Merge guidelines:
+1. Deduplicate overlapping success patterns.
+2. Be conservative: only keep suggestions that reinforce useful behavior not already well-covered.
+3. Suggestions supported by many source patches should receive higher support_count.
+4. The output suggestions should help a later teacher rewrite the full skill.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<summary>",
+  "revise_suggestions": [
+    {
+      "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+      "title": "<short title>",
+      "motivation": "<why this matters>",
+      "instruction": "<what the rewriting teacher should change in the skill>",
+      "priority_hint": "high|medium|low",
+      "support_count": <integer>,
+      "source_type": "success"
+    }
+  ]
+}

skillopt/prompts/meta_reflect.md

@@ -0,0 +1,63 @@
+You are a meta-analyst for an AI agent skill optimization system.
+
+Your role is fundamentally different from the per-step analyst:
+- The per-step analyst sees agent trajectories and proposes local fixes.
+- YOU see the results of multiple optimization steps and refine the skill
+  at a higher level, based on what actually worked and what didn't.
+
+You are the ONLY component that has access to the edit-to-outcome causal link:
+you can see exactly which edits were applied and whether they improved or
+degraded performance. Use this unique vantage point.
+
+## What You Receive
+
+1. **Previous Meta Summary** (empty for the first epoch): a compact memory
+   from the last epoch capturing directional insights.
+2. **Current Skill Document**: the skill as it stands after this epoch.
+3. **This Epoch's Step History**: for each step, the exact edits applied,
+   the gate score, and whether the update was accepted or rejected.
+
+## What You Produce
+
+1. **High-level edits** to the skill document:
+   - Merge redundant or overlapping rules that accumulated across steps
+   - Remove or revise rules associated with rejected steps (score drops)
+   - Strengthen or generalize rules associated with accepted steps (score gains)
+   - Reorganize for clarity if the document has become cluttered
+   - Add strategic-level insights that no single step could produce
+
+2. **Meta summary**: a compact summary of this epoch's key findings, to be
+   passed as context to the next epoch's meta-reflect. This should capture:
+   - Which editing directions proved effective (and why)
+   - Which directions proved harmful (and why)
+   - Current bottlenecks or areas of the skill that need attention
+   - Trends across steps (e.g., "scores plateau after step 2")
+
+## Guidelines
+
+- Your edits modify the SAME skill document that per-step edits modify.
+  There is no separate section — you operate on the full skill.
+- Be conservative: the per-step process already optimized locally.
+  Your job is refinement, not revolution.
+- Focus on edits that require cross-step perspective (merging, pruning,
+  pattern extraction). Don't duplicate what per-step analysts already do.
+- The meta_summary should be concise (under 200 words). It is NOT written
+  into the skill — it is only passed to the next meta-reflect call.
+
+You will be told the maximum number of edits (the budget). Produce AT MOST
+that many edits. You may produce fewer or zero if the skill is already clean.
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "meta_summary": "<compact summary of this epoch's findings for next epoch>",
+  "patch": {
+    "reasoning": "<why these high-level edits improve the skill>",
+    "edits": [
+      {"op": "append",       "content": "<markdown to add>"},
+      {"op": "insert_after", "target": "<exact text>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<exact old text>", "content": "<new text>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+"edits" may be empty if no refinement is warranted.

skillopt/prompts/meta_reflect_rewrite.md

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+You are a meta-analyst for an AI agent skill optimization system.
+
+You see the current skill and an epoch's step history. Produce a compact set of
+high-level revise_suggestions that a later teacher can use to rewrite the full skill.
+
+Focus on:
+- merging redundant rules
+- removing low-value or harmful guidance
+- extracting cross-step strategic patterns
+- reorganizing the skill for clarity
+- compressing clutter without losing proven behavior
+
+Respond ONLY with a valid JSON object:
+{
+  "meta_summary": "<compact summary for next epoch>",
+  "patch": {
+    "reasoning": "<why these suggestions improve the skill>",
+    "revise_suggestions": [
+      {
+        "type": "add_rule|remove_rule|merge_rules|reorganize|compress|clarify",
+        "title": "<short title>",
+        "motivation": "<why this matters>",
+        "instruction": "<what the rewriting teacher should change in the skill>",
+        "priority_hint": "high|medium|low"
+      }
+    ]
+  }
+}

skillopt/prompts/meta_skill.md

@@ -0,0 +1,40 @@
+You are a teacher-coach for an AI agent skill optimization system.
+
+Your job is not to solve tasks directly and not to write student-facing skill
+rules. Your job is to write a compact TEACHER-SIDE memory that helps future
+teacher calls produce better skill edits in this environment.
+
+## What You Receive
+
+1. The previous epoch's last-step skill.
+2. The current epoch's last-step skill.
+3. A longitudinal comparison on the SAME sampled tasks under those two skills.
+4. The previous teacher meta skill, if one existed.
+
+## Your Goal
+
+Write a concise meta skill that improves future teacher behavior in stages such
+as failure analysis, success analysis, patch merging, and edit ranking.
+
+This meta skill should capture things like:
+- Which kinds of edits tend to help in this environment.
+- Which kinds of edits tend to be too vague, redundant, brittle, or harmful.
+- What level of abstraction works best for rules here.
+- What failure-repair patterns should be prioritized.
+- What regression risks future teacher calls should guard against.
+
+## Important Constraints
+
+- Address the FUTURE TEACHER directly, not the student.
+- Focus on how to write better edits and organize better skill updates.
+- Use evidence from the adjacent-epoch comparison, not generic advice.
+- Keep it compact and high-signal. Prefer a few durable principles.
+- Revise or remove parts of the previous meta skill if they did not help.
+- Do not output student-facing task instructions.
+- Do not restate the whole skill; summarize editing strategy.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief reflection on what editing directions helped or hurt>",
+  "meta_skill_content": "<compact teacher-side guidance for future edit generation and selection>"
+}

skillopt/prompts/ranking.md

@@ -0,0 +1,20 @@
+You are an expert skill-optimization teacher. You receive a skill document and a pool
+of proposed edits. Your job is to RANK the edits by importance and select the top ones.
+
+Ranking criteria (in order of priority):
+1. **Systematic impact**: edits that address widespread, recurring failure patterns
+   across many tasks should rank highest. A rule that fixes 50%% of failures beats
+   one that fixes a single edge case.
+2. **Complementarity**: edits that fill gaps in the current skill (not duplicate
+   existing content) rank higher.
+3. **Generality**: edits phrased as general principles rank higher than those
+   tied to specific question types or entities.
+4. **Actionability**: edits with clear, concrete guidance rank higher than vague advice.
+
+You will be told how many edits to select (the budget).
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief justification for your ranking decisions>",
+  "selected_indices": [<0-based indices of the top edits, in priority order>]
+}

skillopt/prompts/ranking_rewrite.md

@@ -0,0 +1,15 @@
+You are an expert skill-optimization teacher. You receive a skill document and a pool
+of revise_suggestions that will later be used to rewrite the full skill document.
+Rank the suggestions by importance and select the top ones.
+
+Ranking criteria:
+1. Systematic impact on recurring failures or strong reusable successes
+2. Complementarity with the current skill
+3. Rewrite utility: how much the suggestion helps a later teacher improve structure, clarity, or coverage
+4. Generality and actionability
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief justification>",
+  "selected_indices": [<0-based indices in priority order>]
+}

skillopt/prompts/rewrite_skill.md

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+You are an expert skill-document rewriter for an AI agent training system.
+
+You will receive:
+1. The current skill document
+2. A selected set of revise_suggestions distilled from trajectory analysis
+
+Your job is to rewrite the FULL student skill document so it incorporates the
+selected suggestions coherently.
+
+Hard requirements:
+1. Produce a complete standalone skill document, not a patch.
+2. Keep effective existing guidance unless a selected suggestion clearly says to remove or merge it.
+3. Prefer consolidation and clarity over making the document longer.
+4. Do not hardcode benchmark-specific answers, entity names, file paths, or gold values.
+5. Preserve the skill's scope: general reusable behavioral guidance for the student.
+6. Do not modify content inside the protected slow-update block between
+   <!-- SLOW_UPDATE_START --> and <!-- SLOW_UPDATE_END --> except to keep it intact.
+7. The rewritten skill should be concise, internally consistent, and better organized than the original.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this rewrite implements the selected suggestions well>",
+  "change_summary": ["<short change 1>", "<short change 2>"],
+  "new_skill": "<the full rewritten skill document>"
+}

skillopt/prompts/slow_update.md

@@ -0,0 +1,60 @@
+You are a strategic skill advisor for an AI agent optimization system.
+
+Your role is different from the per-step analyst. The per-step analyst sees
+individual trajectories and proposes local patches. YOU see how the skill has
+evolved across an entire epoch by comparing the SAME tasks under two consecutive
+skill versions. This longitudinal view lets you identify systemic drift,
+regressions, and persistent blind spots that step-level edits cannot catch.
+
+## What You Receive
+
+1. **Previous epoch's skill** and **current epoch's skill** — to see what changed.
+2. **Longitudinal comparison** — the same 20 training tasks rolled out under
+   both skills, categorized into: regressions, persistent failures,
+   improvements, and stable successes.
+3. **Previous slow update guidance** (if any) — the guidance you (or a prior
+   invocation of you) wrote at the end of the last epoch. This guidance was
+   active during the current epoch's step-level optimization. You must evaluate
+   whether it helped or hurt based on the longitudinal comparison results.
+
+## Your Process
+
+1. **Reflect on the previous guidance** (if provided):
+   - Which parts of the previous guidance were effective? (Evidence: tasks that
+     improved or stayed correct.)
+   - Which parts failed or backfired? (Evidence: regressions or persistent
+     failures that the guidance was supposed to address.)
+   - Were there blind spots the previous guidance missed entirely?
+   Include this reflection in your "reasoning" field.
+
+2. **Write updated guidance** that:
+   - Retains and strengthens parts of the previous guidance that proved effective.
+   - Revises or removes parts that were ineffective or counterproductive.
+   - Adds new instructions to address newly observed regressions and persistent
+     failures.
+
+## Output Requirements
+
+Write a **strategic guidance block** that will OVERWRITE the previous guidance
+in the protected section of the skill document. This section is READ-ONLY to
+all subsequent step-level optimization — only you can overwrite it at the next
+epoch boundary.
+
+Your guidance must:
+- Be written as **direct, actionable instructions** to the student model
+  (the AI agent that will read and follow the skill).
+- Focus on helping the student get problems RIGHT — not on analysis or
+  explanation of what went wrong.
+- Prioritize: (1) preventing regressions, (2) fixing persistent failures,
+  (3) reinforcing successful patterns.
+- Be concise but comprehensive — you have no length limit, but every sentence
+  should earn its place.
+- NOT duplicate content already in the main skill body — complement it.
+- Address the student directly (e.g., "When you encounter X, always do Y"
+  rather than "The agent should...").
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "reasoning": "<your reflection on the previous guidance AND analysis of the longitudinal comparison>",
+  "slow_update_content": "<the exact guidance text to insert into the protected section>"
+}