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.
git clone --depth 1 https://github.com/rocky2431/ultra-builder-proWrote 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/rocky2431/ultra-builder-pro/ultra-research)<a href="https://agentmods.dev/commands/rocky2431/ultra-builder-pro/ultra-research"><img src="https://agentmods.dev/badge/commands/rocky2431/ultra-builder-pro/ultra-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/rocky2431/ultra-builder-pro/ultra-research"><img src="https://agentmods.dev/badge/commands/rocky2431/ultra-builder-pro/ultra-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00012 | $0.00720 |
| Opus 5 | $0.00006 | $0.00360 |
| Sonnet 5 | $0.00002 | $0.00144 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
ultra-research 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 12d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ultra Research v2
Step-file architecture: Each research step is a self-contained instruction file loaded just-in-time for maximum LLM attention density.
How This Works
- Read the main skill file:
skills/ultra-research/SKILL.md - Follow the activation sequence (pre-checks → project type detection → step routing)
- For each step: Read
skills/ultra-research/steps/step-{NN}-{name}.mdand follow ALL instructions - Each step writes output immediately to spec files, then presents [C] Continue gate
- Only proceed to next step after user confirms [C]
Step Sequence
| Step | File | Focus |
|---|---|---|
| R0: Product Discovery | ||
| 00 | step-00-problem-validation.md |
Validate the problem is real |
| 01 | step-01-opportunity-discovery.md |
Map opportunity space (OST) |
| 02 | step-02-market-assessment.md |
TAM/SAM/SOM sizing |
| 03 | step-03-competitive-landscape.md |
Competitor analysis |
| 04 | step-04-product-strategy.md |
Vision, segments, trade-offs |
| 05 | step-05-assumptions-validation.md |
Risk assumptions + experiments |
| R1: User & Scenario | ||
| 10 | step-10-user-personas.md |
2-3 personas |
| 11 | step-11-user-scenarios.md |
3-5 user scenarios |
| R2: Feature Definition | ||
| 20 | step-20-user-stories.md |
Stories + acceptance criteria |
| 21 | step-21-features-scope.md |
MVP scope + exclusions |
| 22 | step-22-success-metrics.md |
KPIs + targets |
| R3: Architecture | ||
| 30 | step-30-architecture-context.md |
Quality goals, constraints |
| 31 | step-31-solution-strategy.md |
Tech stack decisions |
| 32 | step-32-building-blocks.md |
Modules + runtime scenarios |
| R4: Quality & Deploy | ||
| 40 | step-40-deployment.md |
Infrastructure + CI/CD |
| 41 | step-41-quality-risks.md |
Quality scenarios + risks |
| Synthesis | ||
| 99 | step-99-synthesis.md |
Distillate + validation |
Critical Rules
- Read SKILL.md first — it has activation logic and project type detection
- One step at a time — never load multiple step files simultaneously
- Write immediately — every step writes to spec file before presenting to user
- Web search mandatory — factual claims need sources, no exceptions
- User gate — halt at [C] Continue, do not auto-proceed
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.
- 12d ago First seen · 59 lines · 12 tokens per session scan A c52156c06ca2
ultra-research is a command published in the GitHub repository rocky2431/ultra-builder-pro (11 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 720 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 commands, from other repositories
checklist
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.