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 tahirraufkeeyu/software-development-agent-stack--sdas --skill lead-researchgit clone --depth 1 https://github.com/tahirraufkeeyu/software-development-agent-stack--sdasWrote 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/tahirraufkeeyu/software-development-agent-stack--sdas/lead-research)<a href="https://agentmods.dev/skills/tahirraufkeeyu/software-development-agent-stack--sdas/lead-research"><img src="https://agentmods.dev/badge/skills/tahirraufkeeyu/software-development-agent-stack--sdas/lead-research.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 5.1 | $0.00052 | $0.01687 |
| Opus 5 | $0.00026 | $0.00843 |
| Sonnet 5 | $0.00010 | $0.00337 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
lead-research 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
Trigger this skill when:
- A named account lands in an SDR queue and needs a first-touch brief before outreach.
- A rep is prepping for a net-new cold sequence and has only a company name and a target persona.
- An AE inherits a dormant opportunity and needs a refresh on what has changed at the account in the last 90 days.
- Marketing hands over an MQL and wants the account researched before an SDR reaches out.
Do not use this for pre-meeting preparation when a meeting is already booked — use meeting-prep instead, which adds attendee-level research and an agenda.
Inputs
Required:
company_name— legal or common name of the target company (e.g. "Acme Retail", "Nimbus Logistics").
Optional but recommended:
company_domain— resolves ambiguity and speeds up lookup.target_persona— role being pursued (e.g. "VP of Data", "Head of RevOps").icp_definition— text description of what a fit customer looks like, for scoring.prior_crm_notes— dump from Salesforce or HubSpot if the account has history.
Outputs
A single Markdown document, roughly 400-600 words, structured as:
- Snapshot — company name, domain, HQ, headcount band, revenue band, ownership (private/public/PE-backed), key verticals served.
- Tech stack — observed frontend (from BuiltWith/Wappalyzer-style inference or job-post mentions), cloud, data warehouse, CRM, MarTech.
- Recent triggers (last 90 days) — funding, leadership changes, layoffs, M&A, product launches, regulatory exposure. Dated bullets with source URLs.
- Hiring signals — open roles relevant to the persona (team expanding, new function being built, tooling implied by JDs).
- Pain signals — Glassdoor themes, G2/Capterra reviews of incumbent tools if known, public outage or security reports.
- ICP fit score (1-5) and urgency score (1-5), with one-line justification each.
- Three outreach angles — each tied explicitly to a trigger and the target persona, with a one-sentence hypothesis.
- Risks and unknowns — what the brief could not verify.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 117 lines · 52 tokens per session scan A 202ad410a7e7
lead-research is a skill published in the GitHub repository tahirraufkeeyu/software-development-agent-stack--sdas (18 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 1,687 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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