prism

README

Prism

Keep your AI editor or MCP host as the orchestrator. Offload narrow work to local Ollama specialists.

Prism is an MCP server + CLI that runs tool-specific agents on Ollama — GitHub CI, Kubernetes, Argo, docs lookup, Go codegen — and returns compact JSON summaries. Your paid model sees a short brief, not every skill, constitution, and evidence dump.

Why use it

• Lower orchestrator cost — 94% orchestrator input reduction on a live todo-app coding task (benchmarks)
• Local specialists at $0 — Ollama runs absorb skill bodies and domain context; the orchestrator synthesizes from summaries
• Editor stays in charge — no autonomous swarm; you pick agent + skills per subtask
• Progressive disclosure — only attached Agent Skills per call, enforced by allowlists
• Repo-native specs — agents, skills, and constitutions are versioned Markdown in this tree

Quick start

Requires: Go 1.22+, Ollama at http://127.0.0.1:11434, model from agent specs (default llama3.1:8b).

go install ./cmd/prism
ollama pull llama3.1:8b

cd /path/to/prism          # or pass --root everywhere
prism config doctor        # Ollama + agent registry check
prism agent list

# CLI: one specialist run
echo "Summarize PR #42 CI status" | \
  prism run github-cli --skills gh-pr-triage

Use with an MCP host

Register Prism as an MCP server in your AI editor. Example for Cursor (~/.cursor/mcp.json; other MCP hosts use equivalent config — see docs/usage.md):

Use the full path to your prism binary:

{
  "mcpServers": {
    "prism": {
      "command": "/absolute/path/to/prism",
      "args": ["mcp", "serve", "--root", "/absolute/path/to/prism"],
      "env": { "PRISM_OLLAMA_HOST": "http://127.0.0.1:11434" }
    }
  }
}

Reload MCP servers in your editor, then call **run_agent** with agent_id, task, and skill_names.

Available tools include:

• core: list_agents, run_agent, get_constitution, doctor
• prompt/resource compatibility: list_prompts, get_prompt, list_resources, get_resource

Typical flow: paste a short brief → delegate evidence-heavy subtasks to specialists → synthesize their compact summaries. Do not paste all skills and evidence into chat.

Full setup, flags, troubleshooting: docs/usage.md

Built-in agents

Agent	Use for
github-cli	PR triage, GitHub Actions failures
kubectl	Pod/rollout diagnostics
argo	Argo CD sync, workflow debug
web-docs-search	Docs harvest, release notes
go-helper	Small Go helpers and utilities
go-scaffold	Package boilerplate, test scaffolds

Add your own under agents/ and skills/. See agents/README.md and skills/README.md.

Install skills via skills CLI

You can install Prism skills with the skills.sh CLI:

# List discoverable skills from this repo
npx skills add github.com/bryanbarton525/prism -l --full-depth

# Install a specific skill (example: MCP orchestration playbook)
npx skills add github.com/bryanbarton525/prism --skill prism-mcp-orchestrator --full-depth

Useful skill for parent-model delegation behavior:

• prism-mcp-orchestrator — tells the orchestrator when to delegate and how to call Prism MCP tools/resources/prompts in the correct sequence.

Note: this orchestrator skill is intentionally stored under orchestrator-skills/ (not skills/) so Prism MCP runtime agents do not load it as an attachable specialist skill.

How it compares

Capability	Prism	Claude subagents	Cursor Skills+MCP	CrewAI/LangGraph
Orchestrator stays in editor	✓	✓	✓	✗
Specialist isolation	✓	✓	partial	✓
Local Ollama specialists	✓	✗	✗	optional
Token/cost benchmarks	✓	✗	✗	✗
Ops specialists (gh/k8s/argo)	✓	DIY	DIY	DIY

Full breakdown: docs/comparison.md

Proof it saves tokens

Benchmark view

Executive benchmark view

Workload assumption (per engineer): 20 coding prompts/day, 400 prompts/month, 4800 prompts/year.

Task definition: one completed coding request equal to todo-spa-build (live run on 2026-05-31), including implementation output + README.

Orchestrator token footprint per task: without Prism 6,191 in / 811 out → with Prism 363 in / 1,072 out (94.1% input reduction).

Model	Monthly cost without Prism	Monthly cost with Prism	Monthly savings	Annual savings
gpt-5.4	$11.04	$6.80	$4.24	$50.88
gpt-5.5	$22.12	$13.60	$8.52	$102.24
claude-opus-4.7	$20.48	$11.44	$9.04	$108.48
claude-opus-4.6	$20.48	$11.44	$9.04	$108.48
claude-sonnet-4.6	$12.28	$6.88	$5.40	$64.80

Model	Without ($/task)	With ($/task)	Savings/task	Daily savings
gpt-5.4	$0.0276	$0.0170	$0.0107	$0.2120
gpt-5.5	$0.0553	$0.0340	$0.0213	$0.4260
claude-opus-4.7	$0.0512	$0.0286	$0.0226	$0.4520
claude-opus-4.6	$0.0512	$0.0286	$0.0226	$0.4520
claude-sonnet-4.6	$0.0307	$0.0172	$0.0136	$0.2700

Pricing sources: OpenAI and Anthropic list rates configured in testdata/benchmarks/orchestrator-models.yaml. Token counts come from testdata/benchmarks/results.yaml. Regenerate with prism benchmark project --write.

prism benchmark run todo-spa-build
prism benchmark project --write

More scenarios: docs/benchmark-scale.md

Documentation

Doc	What's inside
docs/usage.md	CLI/MCP reference, examples, troubleshooting
docs/comparison.md	Landscape vs Claude, Cursor, frameworks
docs/benchmark-scale.md	At-scale scenarios, monthly projections
docs/workflow-token-diagrams.md	Why token savings happen (visual diagrams)
docs/blog-prism-launch.md	Launch post / product narrative
docs/implementation-plan.md	Architecture and design
docs/success-metrics.md	Benchmark targets and report format

Develop

go test ./...
go build -o prism ./cmd/prism

Prevent direct pushes to main

Install pre-commit and enable both commit checks and the push guard:

pre-commit install
pre-commit install --hook-type pre-push

Run all commit-stage hooks manually:

pre-commit run --all-files

Commit hooks include go test ./..., go build ./cmd/prism, and basic file checks. The pre-push hook in .pre-commit-config.yaml runs scripts/hooks/pre-push-block-main.sh to block direct pushes to main.

If you need an emergency one-off bypass:

PRISM_ALLOW_MAIN_PUSH=1 git push origin <ref>

License

See LICENSE.