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 tigerless-labs/auto-gtm --skill reddit-subreddit-findergit clone --depth 1 https://github.com/tigerless-labs/auto-gtmWrote 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/tigerless-labs/auto-gtm/reddit-subreddit-finder)<a href="https://agentmods.dev/skills/tigerless-labs/auto-gtm/reddit-subreddit-finder"><img src="https://agentmods.dev/badge/skills/tigerless-labs/auto-gtm/reddit-subreddit-finder/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/tigerless-labs/auto-gtm/reddit-subreddit-finder"><img src="https://agentmods.dev/badge/skills/tigerless-labs/auto-gtm/reddit-subreddit-finder.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 | $0.00083 | $0.00853 |
| Opus 5 | $0.00042 | $0.00426 |
| Sonnet 5 | $0.00017 | $0.00171 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
reddit-subreddit-finder 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 11d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reddit-subreddit-finder — rank subreddits for a topic
Given a GTM object (a product/topic in one line), return a ranked table of candidate subreddits with fit, self-promo safety, and a rules summary. On-demand and live: no central index — fit is computed from what rdt returns now, not from corpus overlap.
Shared contracts: rdt-readonly · guardrails.
When to trigger
Manual only. Run when the user asks where to post, or to find communities/audience for a topic.
Flow
1. Find candidates
Derive query terms from the GTM object. reach fetch-reddit search "<terms>" -s relevance -t year — relevance, not top (top biases to mega-subs and off-topic viral posts). Keep only on-topic posts, and collect the subreddits they recur in as candidates.
2. Profile each candidate
reach fetch-reddit sub-info <sub> for subscribers, restrict_posting, submission_type, public_description. For a rough removal signal, sample reach fetch-reddit sub <sub> -s new and read each post's removed / removed_by_category field (per-post, not a single grep count).
3. Score — multi-axis, relative
Judge three axes per candidate from the data, and rank relatively across candidates (no fixed numbers):
- audience match — how on-topic its content/description is to the GTM object;
- self-promo tolerance — from
submission_type+ the removal signal + the rules summary (restrict_postingis near-universallytrue, so weak on its own); - activity — subscribers plus recent post cadence.
4. Safety + rules
Per candidate emit a hard self-promo: safe / risky flag consistent with its rules, a rules summary (see guardrails; it is an approximation), and a one-line tailored entry angle.
Output
A ranked table, best first:
subreddit | subscribers | fit (audience / tolerance / activity) | self-promo safe? | rules summary | entry angle
Stops here for the human to choose a community.
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.
- 11d ago First seen · 55 lines · 83 tokens per session scan A 9cde073d906a
reddit-subreddit-finder is a skill published in the GitHub repository tigerless-labs/auto-gtm (363 stars, last pushed 9d ago), licensed MIT. It adds 83 tokens to every session and 853 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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