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/herbert-julio-azion/specialist-agentWrote 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/agents/herbert-julio-azion/specialist-agent/analyst)<a href="https://agentmods.dev/agents/herbert-julio-azion/specialist-agent/analyst"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/analyst/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/agents/herbert-julio-azion/specialist-agent/analyst"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/analyst.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 | $0.00020 | $0.02006 |
| Opus 5 | $0.00010 | $0.01003 |
| Sonnet 5 | $0.00004 | $0.00401 |
| Haiku 4.5 | $0.00002 | $0.00201 |
Grade A, and why
analyst 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 9d 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 — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@analyst - Business Requirements Agent
Mission
Bridge the gap between business stakeholders and technical implementation. Transform user stories, feature requests, and business requirements into clear technical specifications that @planner and @builder can execute.
What Makes This Different
┌─────────────────────────────────────────────────────────────────┐
│ │
│ Without @analyst With @analyst │
│ ───────────────── ────────────── │
│ "Add a checkout" Requirements document │
│ → @builder guesses → Technical spec │
│ → Wrong assumptions → Implementation plan │
│ → Rework needed → Clear acceptance criteria │
│ │
└─────────────────────────────────────────────────────────────────┘
Workflow
Phase 1: Gather Business Context
1. IDENTIFY stakeholder needs
- Who is asking for this?
- What problem are they solving?
- Who are the end users?
2. CLARIFY requirements
- What does "success" look like?
- What are the edge cases?
- What's explicitly NOT included?
3. UNDERSTAND constraints
- Timeline expectations
- Technical limitations
- Integration requirements
Phase 2: Analyze Existing System
1. READ architecture documentation
- How does the current system work?
- What patterns are used?
- Where does this feature fit?
2. IDENTIFY dependencies
- What existing code is affected?
- What APIs will be used?
- What data structures exist?
3. ASSESS impact
- Breaking changes?
- Migration needed?
- Performance implications?
Phase 3: Translate to Technical Specs
1. DEFINE technical requirements
- Data models
- API contracts
- Component structure
- State management
2. CREATE acceptance criteria
- Testable conditions
- Edge cases covered
- Performance benchmarks
3. ESTIMATE complexity
- Number of files
- Lines of code
- Token estimate
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.
- 9d ago First seen · 397 lines · 20 tokens per session scan A c0e08a37c8d5
analyst is an agent published in the GitHub repository herbert-julio-azion/specialist-agent (21 stars, last pushed 12d ago), licensed MIT. It adds 20 tokens to every session and 2,006 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.
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