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 swan-gtm/gtm-skills --skill map-contactsgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/map-contacts)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/map-contacts"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/map-contacts/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/swan-gtm/gtm-skills/map-contacts"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/map-contacts.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 | $0.00040 | $0.01459 |
| Opus 5 | $0.00020 | $0.00730 |
| Sonnet 5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
map-contacts 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 9d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
Setup state. Not yet configured for this org. Load the Setup sub-page to confirm the persona model (buying-committee shape) is defined and identify which relationship-history data sources are available (CRM, email sender, Fireflies, LinkedIn, prospect search) before mapping anything. (After setup is performed, rewrite this paragraph via swan-update-skill to describe the current state — persona model loaded, which of the five data sources are available, and last-refreshed date — so future runs see the current configuration without re-checking.)
Step 1 — Confirm the target account + intent
Get the company domain or name. Get the intent in one line: net-new ABM mapping, renewal prep, champion-tracking, or expansion of a single-threaded deal. Each shifts priority slightly (renewal leans heavy on current users + economic buyers; champion-tracking leans heavy on past contacts who moved).
Step 2 — Pull the persona model
Load the org's defined personas. Hold the named roles, their title aliases, and their JTBDs as the schema for the map. Do not invent personas — the map is keyed to what the org has saved.
Step 3 — Mine known relationships first (highest signal)
Lead with data the org already has. Each source is checked if connected; skip and note if not.
- CRM. Search Companies / Contacts / Deals for the account. Capture every named contact with role, last activity, owner, and status (still here, departed, dormant).
- Sent email. For each connected sender, search sent items for the account's domain. Pull every person ever emailed, last-touch date, and the thread topic in one line.
- Meeting transcripts. Use
FIREFLIES_GET_TRANSCRIPTSfiltered by participant email domain matching the account. Capture who joined, when, and what was discussed. - Prior sequences. Use
swan-search-sequencesfiltered to the account. Note who was reached, who replied, who booked.
Aggregate by person before moving on — one row per human, even if they appear across all four sources.
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.
- 9d ago First seen · 81 lines · 40 tokens per session scan A cb0d77b47d81
map-contacts is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 1,459 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 2026-09-03.
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