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 sun2443/designer-skills --skill contact-researchgit clone --depth 1 https://github.com/sun2443/designer-skillsWrote 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/sun2443/designer-skills/contact-research)<a href="https://agentmods.dev/skills/sun2443/designer-skills/contact-research"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/contact-research/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/sun2443/designer-skills/contact-research"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/contact-research.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 | $0.00048 | $0.01157 |
| Opus 5.5 | $0.00019 | $0.00463 |
| Sonnet 5.5 | $0.00010 | $0.00231 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
contact-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 13d 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.
This is a copy
100% identical to contact-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contact Research
Retrieve a comprehensive contact profile from Common Room. Supports lookup by email, social handle, or name + company. Returns enriched data including activity history, Spark, scores, website visits, and CRM fields.
Step 1: Locate the Contact
Common Room supports multiple lookup methods — use whichever the user has provided:
| What the user gives | Lookup method |
|---|---|
| Email address | Look up by email (most reliable) |
| LinkedIn, Twitter/X, or GitHub handle | Look up by social handle — specify handle type explicitly |
| Name + company | Identity resolution by name + org domain; present matches if ambiguous |
| Name only | Search by name; if multiple matches, show a brief list and ask the user to confirm |
If no match is found, respond: "Common Room doesn't have a record for this person." Do not speculate or fabricate profile data.
Step 2: Fetch Contact Fields
Use the Common Room object catalog to see available field groups and their contents. For full profiles, request all groups. For targeted questions, request only what's relevant.
Key field groups to know about:
- Scores — always return as raw values or percentiles, never labels
- Recent activity — use
Contact Initiatedfilter (last 60 days) for their actions, not your team's - Website visits — total count + specific pages (last 12 weeks)
- Spark — retrieve all Sparks when tracking engagement evolution over time
Step 3: Run Spark Enrichment (If Available)
If Spark is available, use it. Spark provides:
- Professional background and job history
- Social presence and influence signals
- Persona classification: Champion, Economic Buyer, Technical Evaluator, End User, or Gatekeeper
- Inferred role in the buying process
If Spark is unavailable but real activity data exists (recent actions, website visits, community engagement), infer a persona from those signals. If neither Spark nor activity data is available, classify as Unknown — do not guess a persona from title alone.
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.
- 13d ago First seen · 130 lines · 48 tokens per session scan A 3c01f6465244
contact-research is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,157 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to contact-research, differing in 0 lines, and is treated as a copy.
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