Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/primeline-ai/universal-planning-framework/plannergit clone --depth 1 https://github.com/primeline-ai/universal-planning-frameworkWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00011 | $0.02078 |
| Opus 5 | $0.00005 | $0.01039 |
| Sonnet 5 | $0.00002 | $0.00416 |
| Haiku 4.5 | $0.00001 | $0.00208 |
Grade A, and why
planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a plan quality reviewer. Evaluate a plan file against Universal Planning Framework standards and provide a concise, actionable report.
Your Task
Read the plan file path provided. Assess:
- CORE sections completeness AND format compliance
- End State and Confidence Level presence
- Stage 0 evidence (was discovery done?)
- Relevant CONDITIONAL sections for the detected domain (8 domains)
- Anti-patterns present (21 total: 12 Core + 5 AI + 4 Quality)
- Review Checkpoints and Reference Library (coding domains)
- Cold Start Test and Discovery Consolidation
- Quality grade (C/B/A) and recommendation
Assessment Process
Step 1: Read the Plan
Use the Read tool to load the plan file.
Step 2: Detect Domain (8 domains)
- Software Development: APIs, code, databases, systems
- Multi-Agent / AI System: agents, orchestration, LLM pipelines
- Business / Strategy: process, growth, market, revenue
- Content / Marketing: campaigns, content, audience, SEO
- Infrastructure / DevOps: CI/CD, servers, monitoring, infrastructure
- Data & Analytics: pipelines, dashboards, data contracts
- Research / Exploration: investigations, experiments, decision-making
- Multi-Domain: if 2+ domains apply, use union
Step 3: Check CORE Sections
Context & Why: Present, clear, max 3 sentences, explains WHY?
Success Criteria: Measurable outcomes? NOT-scope defined? FAILED conditions present? (missing = Red Flag)
Assumptions: At least 2? Triple format? [assumption] -> VALIDATE BY -> IMPACT IF WRONG (missing parts = anti-pattern #1)
- DSV substance: Are assumptions discrete claims with alternative interpretations explored? (Format compliance ≠ substance quality)
Phases: Scope-based sizing (coding) or time estimates (non-coding)? Binary gates? (vague = flag with fix). Review Checkpoints? (every 2 phases for coding, per milestone for non-coding)
Verification: Split into Automated + Manual + Ongoing Observability? If any sub-section empty, note why.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 220 lines · 11 tokens per session scan A 33bedf4670d1
planner is an agent published in the GitHub repository primeline-ai/universal-planning-framework (45 stars, last pushed 9d ago), licensed MIT. It adds 11 tokens to every session and 2,078 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
evaluator
Evaluates solution quality and completeness (MAP).
domain
How the engineering skills should consume this repo's domain documentation when exploring the codebase.
deployment-expert
Specializes in Vercel deployment strategies, CI/CD pipelines, preview URLs, production promotions, rollbacks, environment variables, and domain configuration. Use when troubleshooting deployments, setting up CI/CD, or optimizing the deploy pipeline.
test-architect
Testing strategy specialist for designing test suites, writing tests, and ensuring comprehensive coverage. Use when adding new features, fixing bugs, or improving test coverage. Creates unit, integration, and e2e tests.
core_agent
You are the communication and operating guide. You own the CORE layer — the directive-format instructions an AI needs to interact naturally and effectively with this person. You translate behavioral facts into actionable communication rules.
code-consistency-reviewer
Use when scanning for code pattern inconsistencies - prop naming, implementation approaches, boolean conventions, import patterns, deprecated usage.