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/proffesor-for-testing/agentic-qeWrote 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/proffesor-for-testing/agentic-qe/examples)<a href="https://agentmods.dev/commands/proffesor-for-testing/agentic-qe/examples"><img src="https://agentmods.dev/badge/commands/proffesor-for-testing/agentic-qe/examples/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/proffesor-for-testing/agentic-qe/examples"><img src="https://agentmods.dev/badge/commands/proffesor-for-testing/agentic-qe/examples.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 | $0.00000 | $0.02575 |
| Opus 5.5 | $0.00000 | $0.01030 |
| Sonnet 5.5 | $0.00000 | $0.00515 |
| Haiku 4.5 | $0.00000 | $0.00258 |
Grade A, and why
examples 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 5d 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 — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pair Programming Examples
Real-world examples and scenarios for pair programming sessions.
Example 1: Feature Implementation
Scenario
Implementing a user authentication feature with JWT tokens.
Session Setup
claude-flow pair --start \
--mode switch \
--agent senior-dev \
--focus implement \
--verify \
--test
Session Flow
👥 Starting pair programming for authentication feature...
[DRIVER: You - 10 minutes]
/explain JWT authentication flow
> AI explains JWT concepts and best practices
/suggest implementation approach
> AI suggests using middleware pattern with refresh tokens
# You write the basic auth middleware structure
[SWITCH TO NAVIGATOR]
[NAVIGATOR: AI - 10 minutes]
/implement JWT token generation with refresh tokens
> AI generates secure token implementation
/test-gen
> AI creates comprehensive test suite
[SWITCH TO DRIVER]
[DRIVER: You - 10 minutes]
# You refine the implementation
/review --security
> AI performs security review, suggests improvements
/commit --message "feat: JWT authentication with refresh tokens"
✅ Truth Score: 0.98 - Committed successfully
Example 2: Bug Fixing Session
Scenario
Debugging a memory leak in a Node.js application.
Session Setup
claude-flow pair --start \
--mode navigator \
--agent debugger-expert \
--focus debug \
--trace
Session Flow
👥 Starting debugging session...
/status
> Analyzing application for memory issues...
/perf --profile
> Memory usage growing: 150MB → 450MB over 10 minutes
/find "new EventEmitter" --regex
> Found 3 instances of EventEmitter creation
/inspect eventEmitters --deep
> Discovering listeners not being removed
/suggest fix for memory leak
> AI suggests: "Add removeListener in cleanup functions"
/implement cleanup functions for all event emitters
> AI generates proper cleanup code
/test
> Memory stable at 150MB ✅
/commit --message "fix: memory leak in event emitters"
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.
- 5d ago First seen · 512 lines · 0 tokens per session scan A b5b2016eef7f
examples is a command published in the GitHub repository proffesor-for-testing/agentic-qe (504 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,575 tokens. 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 2026-10-11.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.