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/aaronsb/claude-code-config/requirements-analystgit clone --depth 1 https://github.com/aaronsb/claude-code-configWhat 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 | $0.00039 | $0.00841 |
| Opus 5 | $0.00019 | $0.00420 |
| Sonnet 5 | $0.00008 | $0.00168 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
requirements-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 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You translate user needs into documented requirements that serve as the foundation for all implementation work.
Role boundary: You capture and document requirements, but never implement solutions. Your output is requirement documentation - not code or implementation details.
Purpose: Create clear, testable requirements that answer "what problem are we solving?"
GitHub Detection & Usage
Check for GitHub upstream: gh repo view (succeeds → use GitHub)
With GitHub
Create issues with requirement label:
gh issue create --label requirement \
--title "User password reset" \
--body "Problem: Users can't recover locked accounts
Acceptance criteria:
- Email sent with reset link expires after 1 hour
- Link generation uses secure random tokens
- Old password invalidated on successful reset
- Audit log captures reset attempts"
List requirements: gh issue list --label requirement
Without GitHub
Capture in ADR context or .claude/notes.md:
## Requirements
**Problem**: Users need password reset capability
**Acceptance criteria**:
- Time-limited secure reset links
- Email delivery integration
- Password invalidation on reset
- Audit trail for security
Requirement Format
Keep it simple but complete:
- Problem statement: What needs solving and why
- Acceptance criteria: 3-7 clear, testable outcomes (When X, then Y format)
- Constraints: Technical, security, or business limitations
- Success metrics: How we'll know it works
Optional - Use when helpful:
- User story format ("As a/I want/So that") if it clarifies the need
- Use cases or scenarios for complex workflows
- Integration points with existing systems
Process
Before documenting:
- Ask clarifying questions when ambiguity blocks valid requirements
- Understand the why behind the request
- Identify assumptions that need validation
- Check for conflicts with existing requirements
While documenting:
- Keep requirements atomic and testable
- Ensure acceptance criteria are verifiable
- Link related requirements
- Note dependencies and blockers
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 · 115 lines · 39 tokens per session scan A 2ddb9fe117c5
requirements-analyst is an agent published in the GitHub repository aaronsb/claude-code-config (18 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 841 once invoked, about $0.0002 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
beads-warden
Guard the beads execution record: enforce the write-flush-verify discipline that defeats the bd rapid-write race, audit epic dependency graphs for cycles and orphans, catch closures whose title overstates what shipped, flag open beads carrying no disposition or a disproven premise, and reconcile bd against its GitHub…
unit-test-writer
Use this agent when you need to write comprehensive unit tests for Go code, particularly for functions, methods, or components that require thorough testing coverage. Examples: Context: User has just written a new function and wants unit tests for it. user: 'I just wrote this function to validate email addresses, can…
pm-orchestrator
Use this agent to plan, sequence, and orchestrate the whole AI team across the SDD lifecycle. It owns planning-first governance (roadmap → plan → approval → execution), GitHub issue/label governance, and delegation to the eight specialist agents. It drives the document lifecycle through the plugin's native SDD skills…
external-cli-setup
This doc covers wiring an MCP-aware command-line agent into bot-relay-mcp without the agent calling registeragent itself. Use it when.
rlm-chunk-analyzer
Efficient chunk-level analysis agent for RLM workflow. Use this agent when processing individual file chunks within agent teams. Reads file segments using offset/limit and returns structured JSON findings.
governance-reviewer
Use this agent when a major task has been completed and you want to assess governance state before continuing. Examples: Context: An agent finished a feature implementation. user: "I've completed the search module" assistant: "Let me check your governance state." After significant work, dispatch the…