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 skills add bostonaholic/rpikit --skill parallel-agentsgit clone --depth 1 https://github.com/bostonaholic/rpikitWrote 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/skills/bostonaholic/rpikit/parallel-agents)<a href="https://agentmods.dev/skills/bostonaholic/rpikit/parallel-agents"><img src="https://agentmods.dev/badge/skills/bostonaholic/rpikit/parallel-agents.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 266 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00038 | $0.01570 |
| Opus 5 | $0.00019 | $0.00785 |
| Sonnet 5 | $0.00008 | $0.00314 |
| Haiku 4.5 | $0.00004 | $0.00157 |
Grade A, and why
parallel-agents 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 8d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Agents
Dispatch multiple agents concurrently for independent problems.
Purpose
Sequential investigation of independent problems wastes time. When multiple issues have different root causes and don't affect each other, dispatching agents in parallel reduces total resolution time significantly.
When to Use
Use parallel agents when:
- 3+ test files failing with different root causes
- Multiple subsystems broken independently
- Independent tasks in a plan that don't share state
- Bulk operations across unrelated files
Do NOT use when:
- Failures might be related (fixing one might fix others)
- Tasks have sequential dependencies
- Changes could conflict with each other
- Shared state exists between tasks
Decision Framework
Before parallelizing, ask:
1. Are these problems truly independent?
- Different files?
- Different subsystems?
- No shared data or state?
2. Could fixing one affect another?
- Shared dependencies?
- Common configuration?
- Overlapping code paths?
3. Will changes conflict?
- Same file modifications?
- Related API changes?
- Interconnected tests?
If any answer suggests dependency, work sequentially instead.
The Parallel Process
Step 1: Identify Independent Problems
Group failures or tasks by independence:
Test failures example:
- auth.test.js: Login validation errors (auth subsystem)
- api.test.js: Endpoint routing issues (api subsystem)
- db.test.js: Connection pool exhaustion (database subsystem)
Assessment: Independent subsystems, can parallelize
Step 2: Create Focused Agent Prompts
Each agent needs a self-contained, focused prompt:
Good prompt structure:
- ONE clear problem to solve
- ALL necessary context included
- SPECIFIC about expected output
- NO dependencies on other agents
Example prompts:
Agent 1 - Auth fixes:
"Fix the login validation errors in auth.test.js.
The tests expect [specific behavior].
Current error: [error message].
Do not modify files outside src/auth/."
Agent 2 - API fixes:
"Fix the endpoint routing issues in api.test.js.
Routes should map to [expected handlers].
Current error: [error message].
Do not modify files outside src/api/."
Agent 3 - Database fixes:
"Fix the connection pool exhaustion in db.test.js.
Pool should handle [expected load].
Current error: [error message].
Do not modify files outside src/db/."
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.
- 8d ago First seen · 296 lines · 38 tokens per session scan A ade36013c175
parallel-agents is a skill published in the GitHub repository bostonaholic/rpikit (20 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,570 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.
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