Borrowing it
Nothing to install: this file belongs to GTM-Strategist/gtm-strategist-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GTM-Strategist/gtm-strategist-skills/master/.claude/skills/collecting-intelligence/SKILL.mdgit clone --depth 1 https://github.com/GTM-Strategist/gtm-strategist-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/gtm-strategist/gtm-strategist-skills/collecting-intelligence)<a href="https://agentmods.dev/skills/gtm-strategist/gtm-strategist-skills/collecting-intelligence"><img src="https://agentmods.dev/badge/skills/gtm-strategist/gtm-strategist-skills/collecting-intelligence/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/gtm-strategist/gtm-strategist-skills/collecting-intelligence"><img src="https://agentmods.dev/badge/skills/gtm-strategist/gtm-strategist-skills/collecting-intelligence.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 | $0.00075 | $0.04376 |
| Opus 5 | $0.00037 | $0.02188 |
| Sonnet 5 | $0.00015 | $0.00875 |
| Haiku 4.5 | $0.00007 | $0.00438 |
Grade A, and why
collecting-intelligence 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 12d 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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 2: Collecting Intelligence — Customer Discovery & Competitive Research
You are executing Phase 2 of the GTM Strategist methodology. This phase transforms the user's assumptions from Phase 1 into evidence-backed market intelligence through structured customer discovery and competitive research.
Before You Start
-
Read
my-gtm-context.mdat the project root. If critical fields (Product/Service, Target Market, Problem & Value) are empty, ask the user to fill them in before proceeding. -
Check
outputs/for Phase 1 deliverables. This phase builds directly on:outputs/01-ope-canvas.md(Opportunity, Product, Execution canvas)outputs/01-swot-analysis.mdoutputs/01-value-proposition.mdoutputs/01-90-day-plan.md
If Phase 1 outputs exist, reference them throughout. If they don't exist, note that the user is working from assumptions — which is fine, but flag it. Phase 2 is specifically designed to test assumptions.
-
Work one task at a time. Present the deliverable, get feedback, then move to the next task. Don't dump all nine tasks at once.
Task 1: Identify Beachhead Segment Candidates
Duration: 1-3 hours | Output: outputs/02-beachhead-candidates.md
"SaaS founders from the US" is NOT a segment. As a new player entering a market, you need critical mass of traction with relatable references. You cannot effectively market to 17,000 SaaS founders — nobody will feel like your product was built for them. A beachhead segment is narrow enough that customers within it reference each other, share context, and create word-of-mouth density.
What to do:
-
Read
my-gtm-context.mdsections 2 (Target Market) and 3 (ICP). Pull any existing market assumptions. -
If Phase 1 outputs exist, extract the initial market/customer assumptions from the OPE canvas and SWOT.
-
Guide the user to brainstorm 5-8 candidate beachhead segments. For each segment, capture:
- Segment label — specific enough that the user could name 10 people in it
- Size estimate — rough order of magnitude (hundreds, low thousands)
- Pain intensity — how urgent is the problem for this group (1-5 scale)
- Reachability — can you actually get in front of them? Through what channels?
- Reference density — do people in this segment talk to each other, follow each other, attend the same events?
- Willingness to pay — is this a budget line item or a nice-to-have?
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.
- 12d ago First seen · 366 lines · 75 tokens per session scan A e6eeaa55705c
collecting-intelligence is a skill published in the GitHub repository GTM-Strategist/gtm-strategist-skills (254 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 4,376 once invoked, about $0.0004 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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