baekenough/adaptive-harness

Adaptive Harness Self-Customization Skill

Automatically detects project context and optimizes the oh-my-customcode harness (agents, skills, rules) to fit the project. Generates a persistent project profile that drives agent activation decisions and records learned patterns over time.

Usage

/omcustom:adaptive-harness              # Full scan + optimize
/omcustom:adaptive-harness --scan       # Scan only (generate/update project profile)
/omcustom:adaptive-harness --optimize   # Deactivate unused, suggest missing
/omcustom:adaptive-harness --learn      # Analyze failure patterns, update profile
/omcustom:adaptive-harness --export     # Export profile as portable bundle
/omcustom:adaptive-harness --import <path>  # Import profile from another project
/omcustom:adaptive-harness --dry-run    # Show what would change without modifying

Default (no flag): runs --scan then --optimize in sequence.

Project Profile Format

The skill generates and maintains .claude/project-profile.yaml. Manual edits to this file are preserved across runs — the skill merges new detections with existing content rather than overwriting.

# Auto-generated by adaptive-harness. Manual edits will be preserved.
project:
  name: detected-project-name
  scanned_at: "2026-04-12T10:00:00Z"

tech_stack:
  languages: [python, typescript]
  frameworks: [fastapi, next.js]
  databases: [postgres, redis]
  infra: [docker, aws]

detection_evidence:
  - indicator: "requirements.txt found"
    confidence: high
    suggests: [lang-python-expert, be-fastapi-expert]
  - indicator: "package.json with next dependency"
    confidence: high
    suggests: [lang-typescript-expert, fe-vercel-agent]

active_agents:
  - lang-python-expert
  - be-fastapi-expert
  - lang-typescript-expert
  - fe-vercel-agent
  - db-postgres-expert
  - db-redis-expert
  - infra-docker-expert
  - infra-aws-expert
  # manager agents always active
  - mgr-creator
  - mgr-gitnerd
  - mgr-sauron
  - mgr-supplier
  - mgr-updater
  - mgr-claude-code-bible

inactive_agents:
  - lang-golang-expert    # no Go files detected
  - lang-rust-expert      # no Rust files detected

usage_stats:
  most_used_agents: []     # populated by --learn
  failure_patterns: []     # populated by --learn

overrides:
  rules: {}                # e.g., R009: { max_parallel: 5 }

last_optimized: "2026-04-12T10:00:00Z"

Workflow: --scan

Scans the TARGET project (the project using oh-my-customcode, not the harness itself) and generates or updates the project profile. Uses Read, Glob, and Grep only — no side effects.

Step 1: Detect Tech Stack

Check for language manifest files and framework indicators:

| Indicator Files | Tech | Suggests Agents | |----------------|------|-----------------| | go.mod, *.go | Go | lang-golang-expert, be-go-backend-expert | | Cargo.toml, *.rs | Rust | lang-rust-expert | | requirements.txt, pyproject.toml, *.py | Python | lang-python-expert | | fastapi in deps/imports | FastAPI | be-fastapi-expert | | django in deps/imports | Django | be-django-expert | | package.json, tsconfig.json, *.ts, *.tsx | TypeScript | lang-typescript-expert | | next in package.json deps | Next.js | fe-vercel-agent | | vue in package.json deps | Vue.js | fe-vuejs-agent | | svelte.config.*, *.svelte | Svelte | fe-svelte-agent | | pubspec.yaml, *.dart | Flutter | fe-flutter-agent | | *.kt, build.gradle.kts | Kotlin | lang-kotlin-expert | | *.java, pom.xml | Java | lang-java21-expert | | spring-boot in deps | Spring Boot | be-springboot-expert | | express in package.json deps | Express | be-express-expert | | @nestjs in package.json deps | NestJS | be-nestjs-expert | | Dockerfile, docker-compose.* | Docker | infra-docker-expert | | cdk.json, template.yaml, .aws/ | AWS | infra-aws-expert | | terraform/, *.tf | Terraform | infra-aws-expert | | .github/workflows/ | CI/CD | mgr-gitnerd | | *.sql, alembic/, pg in deps | PostgreSQL | db-postgres-expert | | redis in deps/config | Redis | db-redis-expert | | supabase in deps/config | Supabase | db-supabase-expert | | prisma/, drizzle/ | ORM | db-postgres-expert | | dags/*.py, airflow in deps | Airflow | de-airflow-expert | | dbt_project.yml | dbt | de-dbt-expert | | kafka in deps/config | Kafka | de-kafka-expert | | spark in deps/config | Spark | de-spark-expert | | snowflake in deps/config | Snowflake | de-snowflake-expert |

Step 2: Build Detection Evidence

For each indicator found, record:

• indicator: human-readable description of what was found
• confidence: high (direct manifest file) | medium (dependency reference) | low (indirect signal)
• suggests: list of agent names this indicator implies

Step 3: Write Project Profile

Delegate write to a subagent (R010). Merge with existing profile if present — preserve overrides, usage_stats, and any manual entries.

Output format:

[adaptive-harness --scan] Target: /path/to/project

Tech Stack Detected:
  - Python     (requirements.txt + pyproject.toml found)      [confidence: high]
  - FastAPI    ("fastapi" in requirements.txt)                [confidence: high]
  - TypeScript (tsconfig.json found)                          [confidence: high]
  - Next.js    ("next" in package.json deps)                  [confidence: high]
  - Docker     (Dockerfile found)                             [confidence: high]
  - PostgreSQL ("psycopg2" in requirements.txt)               [confidence: medium]
  - Redis      ("redis" in requirements.txt)                  [confidence: medium]
  - AWS        (cdk.json found)                               [confidence: high]

Active agents identified: 8
Profile written: .claude/project-profile.yaml

Workflow: --optimize

Reads the project profile and adjusts which agent files are active.

Step 1: Load Profile

Read .claude/project-profile.yaml. If the profile does not exist, run --scan first.

Step 2: Identify Inactive Agents

Compare all agent files in .claude/agents/*.md against active_agents list from the profile. Agents not in the active list (and not in the always-active set below) are candidates for deactivation.

Always-Active Agents (never deactivate)

mgr-creator, mgr-gitnerd, mgr-sauron, mgr-supplier, mgr-updater, mgr-claude-code-bible
sys-memory-keeper, sys-naggy
arch-documenter, arch-speckit-agent

Step 3: Move Inactive Agents

Delegate to subagent (R010):

• Create .claude/agents/.inactive/ directory if it does not exist
• Move inactive agent .md files to .claude/agents/.inactive/
• Update inactive_agents list in project profile

Step 4: Detect Gaps

Check active_agents list against files actually present in .claude/agents/. If an active agent file is missing, flag it as a gap and suggest mgr-creator to fill it.

Step 5: Log Adaptations

Append a record to .claude/outputs/harness-adaptations/YYYY-MM-DD.md:

## Optimization Run — 2026-04-12T10:00:00Z

Deactivated (moved to .inactive/):
  - lang-golang-expert
  - lang-rust-expert
  - de-airflow-expert

Gaps detected (agents needed but missing):
  - (none)

Profile: .claude/project-profile.yaml

Restore

Run --optimize --restore to move all files from .claude/agents/.inactive/ back to .claude/agents/. This reverses the last optimization.

Output format:

[adaptive-harness --optimize]

Always-active agents: 10 (protected)
Active per profile:   8
Candidates for deactivation: 29

Deactivated:
  - lang-golang-expert      → .claude/agents/.inactive/
  - lang-rust-expert        → .claude/agents/.inactive/
  - de-airflow-expert       → .claude/agents/.inactive/
  ... (26 more)

Gaps detected: 0

Log: .claude/outputs/harness-adaptations/2026-04-12.md
Summary: 29 deactivated, 18 active, 0 gaps

--dry-run mode outputs [would deactivate] / [would restore] without moving any files.

Workflow: --learn

Analyzes session history and eval-core data to populate usage_stats and failure_patterns in the project profile.

Step 1: Collect Data Sources

• .claude/outputs/ — session artifacts and eval results
• .claude/agent-memory/ — agent memory files with usage patterns
• Any harness eval output from /omcustom:harness-eval

Step 2: Extract Patterns

Most-used agents:   Count agent invocations across outputs
Failure patterns:   Identify agents that frequently retried or errored
Unused agents:      Active agents with zero invocations in recent N sessions

Step 3: Update Profile

Merge findings into usage_stats and failure_patterns sections of the project profile. Preserve existing entries; append new ones.

Step 4: Generate Suggestions

Based on failure patterns, suggest:

• Rule overrides (e.g., increase max_parallel if timeout patterns detected)
• Agent replacements (e.g., suggest escalation to opus model for frequently failing tasks)
• Additional skills that may reduce failure rate

Output format:

[adaptive-harness --learn]

Sessions analyzed: 12
Agent invocations found: 847

Most-used agents (top 5):
  1. lang-python-expert    (312 invocations)
  2. be-fastapi-expert     (189 invocations)
  3. mgr-gitnerd           (97 invocations)
  4. db-postgres-expert    (84 invocations)
  5. lang-typescript-expert (71 invocations)

Failure patterns:
  - db-postgres-expert: 3 retries in session 2026-04-10 (timeout pattern)
  
Suggestions:
  - db-postgres-expert: consider effort: high for complex query generation
  - de-kafka-expert: 0 invocations — candidate for deactivation

Profile updated: .claude/project-profile.yaml

Workflow: --export / --import

Export

Bundles the project profile and active agent list for sharing with another project or team member.

Output: .claude/outputs/harness-bundle-YYYY-MM-DD.json

{
  "version": "1.0.0",
  "exported_at": "2026-04-12T10:00:00Z",
  "source_project": "detected-project-name",
  "profile": { ... },
  "active_agent_names": [ ... ]
}

Import

/omcustom:adaptive-harness --import .claude/outputs/harness-bundle-2026-04-12.json

Reads the bundle and applies the active_agents list to the current project by running --optimize with the imported profile. Does not overwrite usage_stats or failure_patterns from the current project.

Execution Rules

• --scan uses Read, Glob, Grep only — no writes, safe to run anytime
• All file writes (profile, logs, agent moves) are delegated to subagents (R010)
• --dry-run suppresses all writes; outputs [would ...] for every action
• Profile changes are always logged to .claude/outputs/harness-adaptations/ for auditability
• When profile already exists, the skill merges new detections rather than overwriting
• Parallel Glob/Grep calls are used during --scan for performance (R009)

Integration

| Component | Interaction | |-----------|-------------| | /omcustom:analysis | Calls adaptive-harness --scan after initial tech stack detection to persist the profile | | SessionStart hook | Lightweight profile existence check only — no full scan at startup | | mgr-creator | Invoked when gaps are detected during --optimize to create missing agent files | | R016 (Continuous Improvement) | Failure patterns from --learn may trigger rule updates | | eval-core | Primary data source for --learn invocation and usage pattern extraction | | mgr-sauron | Run after --optimize to verify structural integrity (R017) |

Notes

• Always run --dry-run first on a new project to preview deactivation scope
• --optimize --restore is the safe exit if deactivation causes unexpected routing failures
• The .inactive/ directory is git-tracked so deactivation decisions are visible in history
• Manager and system agents are unconditionally protected from deactivation
• Target directory defaults to the project root where Claude Code is running, not the omcustom harness directory