agent-prompt-evolve

name: agent-prompt-evolve description: >- Self-improvement of agent system prompts through skill absorption. Analyzes agent performance, extracts missing capabilities from available skills, and enriches agent prompts with new abilities. Works with sub-agents.directory agents and custom agents. Validates improvements from multiple sources before applying. Triggers: 'improve agent prompt', 'agent self-improve', 'evolve agent', 'обучи агента', 'улучши промпт агента', 'agent learning', 'agent skill absorption', 'приклей скилл к агенту', 'agent enrichment', 'наделить агента', 'прокачай агента' user-invocable: true disable-model-invocation: false allowed-tools: Read Write Edit Bash Grep Glob Agent WebSearch metadata: author: artvision version: "1.0" category: agent-infrastructure based-on: "Voyager (NVIDIA), SAGE, 4-Gate Validation model"

Agent Prompt Evolve

Improve agent system prompts by discovering and absorbing capabilities from skills, session history, and external sources.

Core Concept

Agent Prompt (who I am)
    +
Skill (what I can do)     →  ENRICHED Agent Prompt (who I am + new abilities)
    +
Validation (is it safe?)

An agent's system prompt defines its identity and capabilities. This skill analyzes gaps and enriches the prompt with new abilities extracted from skills, patterns, and verified sources.

Modes

Mode 1: Gap Analysis

/agent-prompt-evolve analyze <agent-name>

1. Read agent's current prompt (from ~/.claude/agents/ or sub-agents.directory)
2. Run agent on a test task
3. Identify where the agent struggled or produced weak output
4. Search available skills for capabilities that would help
5. Report gaps with recommended skill absorptions

Mode 2: Skill Absorption

/agent-prompt-evolve absorb <agent-name> <skill-name>

1. Read the target agent's current system prompt
2. Read the skill's SKILL.md
3. Extract candidate capability (what agent would gain)
4. Run 5-Gate Proficiency Validation (see below):
   • Gate 1: Purpose alignment (internal reasoning)
   • Gate 2: Factual accuracy (WebSearch 2+ sources per claim)
   • Gate 3: Best practices (search official docs, current standards)
   • Gate 4: Contradiction check (diff with existing prompt)
   • Gate 5: Behavioral test (3 tasks, before vs after)
5. If ALL gates pass → extract and adapt instructions
6. Rewrite agent prompt with new capability in "Absorbed" section
7. Save with changelog + validation report + source URLs

Mode 3: Auto-Evolve

/agent-prompt-evolve auto <agent-name>

1. Read last 3 session logs where this agent was used
2. Extract patterns: what worked, what failed, what was missing
3. Search skills library for matching capabilities
4. Run 3-source validation on each candidate
5. Apply all validated improvements
6. Generate diff report

Mode 4: Create From Skills

/agent-prompt-evolve create <new-agent-name> <skill1> <skill2> ...

1. Read each skill
2. Synthesize a new agent prompt combining capabilities
3. Validate: no conflicts between skills, coherent identity
4. Write to ~/.claude/agents/<new-agent-name>.md

5-Gate Proficiency Validation Pipeline

NEVER absorb a capability into an agent prompt without passing ALL 5 gates. Each gate uses EXTERNAL reliable sources, not the skill's own claims.

GATE 1: PURPOSE ALIGNMENT (internal check)
├── Does the capability serve the agent's role?
├── Would a human expert in this role use this?
├── Is it within the agent's domain or overreach?
└── Score: 0-10 (threshold: 7+)
└── Method: LLM reasoning

GATE 2: FACTUAL ACCURACY (external sources)
├── Extract all factual claims from the capability
├── For EACH claim, verify against 2+ reliable sources:
│   ├── Official documentation (MDN, Python docs, RFC, etc.)
│   ├── Academic papers (arxiv, Google Scholar)
│   ├── Industry reports (Statista, SensorTower, data.ai)
│   ├── Official APIs / changelogs
│   └── Reputable tech publications (not blogs/forums)
├── Flag unverifiable claims as [UNVERIFIED]
├── Remove or mark claims verified by only 1 source as [WEAK]
└── Score: % of claims verified by 2+ sources (threshold: 80%+)
└── Method: WebSearch + WebFetch per claim, log sources

GATE 3: BEST PRACTICES CHECK (professional standards)
├── Does the approach match industry best practices?
├── Search for:
│   ├── Official style guides (Google, Airbnb, PEP8, etc.)
│   ├── Framework documentation (latest stable version)
│   ├── Security advisories (OWASP, CVE databases)
│   └── Conference talks / expert opinions (last 12 months)
├── Check for deprecated patterns or outdated advice
├── Verify version numbers and compatibility
└── Score: 0-10 (threshold: 7+)
└── Method: WebSearch for "[topic] best practices 2026"

GATE 4: CONTRADICTION CHECK (existing prompt)
├── Does new capability contradict existing agent abilities?
├── Does it duplicate something already in the prompt?
├── Does it change the agent's personality/tone?
├── Would merging create ambiguous instructions?
└── Score: PASS/FAIL
└── Method: Diff analysis between old and new prompt

GATE 5: BEHAVIORAL TEST (live proof)
├── Run agent WITHOUT new capability on 3 test tasks → baseline
├── Run agent WITH new capability on same 3 tasks → enriched
├── Compare:
│   ├── Quality: is output measurably better?
│   ├── Regression: did anything that worked before break?
│   ├── Token usage: did prompt bloat slow things down?
│   └── Coherence: does agent still have clear identity?
└── Score: enriched must beat baseline on 2/3 tasks, 0 regressions
└── Method: Agent tool with before/after comparison

Gate Results Format

VALIDATION REPORT: [capability] → [agent]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Gate 1 (Purpose):     8/10  PASS
Gate 2 (Facts):       12/15 claims verified (80%)  PASS
  - "Zalo has 75M MAU in Vietnam" → verified: TechInAsia, Statista
  - "BiP has 25M in Turkey" → verified: Turkcell IR, SimilarWeb
  - "UPI handles 85% payments" → [WEAK] only NPCI source
Gate 3 (Best Practice): 9/10  PASS
Gate 4 (Contradiction): PASS (no conflicts)
Gate 5 (Behavioral):   3/3 tasks improved, 0 regressions  PASS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
VERDICT: ABSORB (all gates passed)
Sources: 8 external, 0 unverified claims remaining

Source Reliability Tiers

Rule: Every absorbed fact must have at least 1 Tier A/B source. If only Tier C/D available → mark as [UNVERIFIED] in agent prompt.

ALL five gates must pass. If any fails, DO NOT absorb. Report which gate failed and why.

Agent Prompt Structure

When enriching, maintain this structure:

# Agent: <name>

## Identity (WHO — never change without permission)
<role, expertise, personality>

## Core Capabilities (WHAT — original abilities)
<existing capabilities>

## Absorbed Capabilities (WHAT — from skills)
<new capabilities with source attribution>
### From: <skill-name> (absorbed <date>)
<extracted and adapted instructions>

## Tools & Methods (HOW)
<tools available, preferred approaches>

## Constraints (WHAT NOT)
<boundaries, limitations, safety rules>

## Changelog
| Date | Skill | What Changed | Validation Score |

Key Rules

DO:

• Keep agent identity intact — skills ADD abilities, don't replace personality
• Attribute every absorbed capability to its source skill
• Version every change with date and validation scores
• Test before and after on real tasks
• Prefer extracting PRINCIPLES over copying INSTRUCTIONS verbatim
• Merge overlapping capabilities (don't duplicate)

DON'T:

• Absorb a skill that contradicts the agent's core role
• Copy entire SKILL.md into agent prompt (extract, don't copy)
• Skip validation — even internal skills can degrade performance
• Grow prompt beyond 2000 tokens (diminishing returns, proven by research)
• Absorb without testing on sample tasks first

Unbounded Growth Prevention

Research (Voyager, SAGE papers) shows skill libraries degrade past a critical size.

Limits:

• Max 5 absorbed capabilities per agent
• Each absorption must improve measurable output quality
• Quarterly review: remove capabilities with <20% usage
• If prompt exceeds 2000 tokens → compress or remove least-used

Integration with Existing Skills

This skill works with:

• /ai-evolve — evolves SKILL files (this skill evolves AGENT prompts)
• /continuous-learning — extracts patterns from sessions (this skill absorbs them)
• /skill-generator — creates new skills (this skill feeds them to agents)
• /run-agent — loads agents from directory (enriched agents live there)

Example Flow

User: "прокачай агента market-researcher"

1. Read ~/.claude/agents/market-researcher.md (or sub-agents.directory)
2. Read last 3 sessions where market-researcher was used
3. Found: agent didn't know local payment methods per country
4. Search skills: /market-research has country microplan knowledge
5. Validate:
   - Source 1: Purpose alignment = 9/10 (directly relevant)
   - Source 2: Cross-check PIX/UPI/Kaspi data = verified
   - Source 3: Test on "research Vietnam market" = improved output
6. Extract: "For each country, research local payment methods..."
7. Insert into agent prompt under "Absorbed Capabilities"
8. Save + log change

Result: market-researcher now automatically includes local payments

File Locations