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 aAAaqwq/AGI-Super-Team --skill client-discoverygit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/client-discovery)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/client-discovery"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/client-discovery/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/skills/aaaaqwq/agi-super-team/client-discovery"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/client-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.01210 |
| Opus 5 | $0.00010 | $0.00605 |
| Sonnet 5 | $0.00004 | $0.00242 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
client-discovery 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Client Discovery
Take a client's raw list of requests and produce a structured scoping breakdown with categories, hours, pricing, dependencies, and recommended phases.
When to use
- Client sends a list of automation/AI tasks they want built
- "analyze requests from [client]"
- "scope this project"
- "estimate hours for [client]"
- "create proposal breakdown"
- Before a discovery/scoping call — to come prepared with estimates
Dependencies
- Other skills:
query-leads(CRM data),client-workspace(for shared docs) - External: none (this is an analysis skill, no scripts)
How to execute
Step 1: Gather inputs
- Client's raw request list — from TG, email, call notes, or shared doc
- Client's tech stack — CRM, ATS, tools they use (from CRM notes or questionnaire)
- Company context — from CRM: size, industry, budget signals
Step 2: For each request item, analyze
For every item in the client's list, produce:
| Field | Description |
|---|---|
| Name | Short name (2-5 words) |
| Category | agent / automation / integration / knowledge-base / product |
| What client wants | Plain language — what outcome they expect |
| What needs to be built | Technical: APIs, triggers, LLM prompts, data flows |
| Key questions | What we need to clarify before building |
| Integrations | Which tools/APIs: CRM, ATS, LinkedIn, Bluedot, etc. |
| Complexity | low (prompt eng, 4-6h) / medium (integration, 6-10h) / high (multi-system, 10-15h) |
| Hours estimate | Range: low-high |
| Dependencies | Other items that should be built first |
Step 3: Prioritize
Group items into:
- Quick wins (low complexity, high impact) — do first, show value fast
- High ROI (medium complexity, core business impact) — second phase
- Strategic (high complexity, long-term value) — third phase
- Can skip / already exists — tools like NotebookLM that solve it out of the box
Step 4: Check for off-the-shelf solutions
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 · 129 lines · 21 tokens per session scan A 521488361875
client-discovery is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 1,210 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 2026-09-05.
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