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 commands/primeline-ai/universal-planning-framework/plan-reviewgit clone --depth 1 https://github.com/primeline-ai/universal-planning-frameworkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/primeline-ai/universal-planning-framework/plan-review)<a href="https://agentmods.dev/commands/primeline-ai/universal-planning-framework/plan-review"><img src="https://agentmods.dev/badge/commands/primeline-ai/universal-planning-framework/plan-review.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00008 | $0.02695 |
| Opus 5 | $0.00004 | $0.01347 |
| Sonnet 5 | $0.00002 | $0.00539 |
| Haiku 4.5 | $0.00001 | $0.00269 |
Grade A, and why
plan-review 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 6d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Quality Review
Read the plan file at $ARGUMENTS and evaluate it against Universal Planning Framework standards.
Check Process
1. CORE Sections Check
Verify all 5 CORE sections are present, complete, and compliant:
Context & Why:
- Clear in max 3 sentences? Explains WHY, not just WHAT?
- Would someone unfamiliar understand in 60 seconds?
Success Criteria:
- Measurable, specific outcomes (not "improve" or "better")?
- NOT-scope explicitly defined?
- FAILED conditions present (kill criteria + timeout)? If missing: Red Flag.
Assumptions & Validation:
- Triple format?
[assumption] -> VALIDATE BY: [method] -> IMPACT IF WRONG: [consequence] - At least 2 assumptions? Empty = Red Flag.
- DSV substance check: Are assumptions decomposed into discrete claims (not bundled)? Does each explore an alternative interpretation (not just "VALIDATE BY: check")?
Phases:
- Coding domains: scope-based sizing (files, features, tests)? Non-coding: time estimates OK.
- Binary gates (pass/fail, verifiable - not "code complete" or "looks good")?
- Review Checkpoints present (every 2 phases for coding, per milestone for non-coding)?
Verification:
- Split into 3 sub-sections: Automated + Manual + Ongoing Observability?
- If Automated empty: why can't this be tested?
- If Manual empty: user-facing aspect ignored?
- If Ongoing Observability empty: how do we know it keeps working?
2. End State & Confidence Check
- End State: Is there a paragraph describing the concrete outcome? (Recommended, not required)
- Confidence Level: High / Medium / Low assigned at plan header?
- Low confidence = Phase 1 must be validation sprint
3. Domain Detection (8 domains)
Determine which domain(s) apply:
- 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
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.
- 6d ago First seen · 274 lines · 8 tokens per session scan A aa0ec2ddb7ca
plan-review is a command published in the GitHub repository primeline-ai/universal-planning-framework (45 stars, last pushed 12d ago), licensed MIT. It adds 8 tokens to every session and 2,695 once invoked, about $0.0000 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 commands, from other repositories
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discovery-ping
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validate
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pr-check-and-fix
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speckit.tasks
Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
speckit.taskstoissues
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.