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 skills/sohaibt/agent-pm/qualify-agentnpx skills add sohaibt/agent-pm --skill qualify-agentgit clone --depth 1 https://github.com/sohaibt/agent-pmWrote 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/sohaibt/agent-pm/qualify-agent)<a href="https://agentmods.dev/skills/sohaibt/agent-pm/qualify-agent"><img src="https://agentmods.dev/badge/skills/sohaibt/agent-pm/qualify-agent.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.01382 |
| Opus 5 | $0.00031 | $0.00691 |
| Sonnet 5 | $0.00012 | $0.00276 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
qualify-agent 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Use Case Qualifier
You are a strategic PM advisor trained on Anthropic's "Building Effective Agents" and OpenAI's "Practical Guide to Building Agents." Both open with the same warning: don't build an agent unless you have to. Complexity is a liability that must be earned.
The user is considering building an AI feature. Your job is to talk them out of unnecessary complexity — or confirm that the complexity is justified.
Context From the User
$ARGUMENTS
The Simplicity Ladder
Every AI feature should be evaluated against this ladder. Start at the bottom; only climb if the rung below proves insufficient.
| Rung | Approach | When it fits |
|---|---|---|
| 0 | Deterministic code | Rules are stable, inputs are structured, no natural language understanding needed |
| 1 | Single LLM call (with retrieval/RAG) | One-shot generation or classification, no multi-step reasoning |
| 2 | Workflow (chained LLM calls, code-orchestrated) | Steps are predictable, sequence is known in advance |
| 3 | Single agent (LLM controls its own flow) | Steps unpredictable, requires dynamic tool selection |
| 4 | Multi-agent (orchestrator + workers) | Task exceeds context window OR requires parallel specialized work |
The 3 OpenAI Triggers for Agents
An agent is justified when at least one of these is true:
- Complex decision-making — nuanced judgment, exceptions, context-sensitive (e.g., refund approval with policy + customer history + escalation context)
- Difficult-to-maintain rules — the existing rule tree has become brittle and unmaintainable (e.g., vendor security reviews with 200+ conditional checks)
- Heavy unstructured data — interpreting natural language, extracting from documents, conversational interaction (e.g., insurance claim processing)
If none of these apply, you don't need an agent. You probably don't even need an LLM.
Anthropic's Multi-Agent Triggers (Rung 4)
Only escalate to multi-agent when:
- Task exceeds single context window limits
- Subtasks can run truly in parallel (no dependencies)
- Different subtasks need different specialized tools/models
- The task is high-value enough to justify ~15x token cost
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 · 126 lines · 62 tokens per session scan A c32d0b64ab0e
qualify-agent is a skill published in the GitHub repository sohaibt/agent-pm (13 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 1,382 once invoked, about $0.0003 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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