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 agentmods add skills/shawnla90/gtm-coding-agent/reply-enginenpx skills add shawnla90/gtm-coding-agent --skill reply-enginegit clone --depth 1 https://github.com/shawnla90/gtm-coding-agentWhat 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 | $0.00147 | $0.01827 |
| Opus 5 | $0.00073 | $0.00914 |
| Sonnet 5 | $0.00029 | $0.00365 |
| Haiku 4.5 | $0.00015 | $0.00183 |
Grade A, and why
reply-engine 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 3d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reply-engine
One gated, ≤18-word reply template per opportunity, and a recorded reason for every thread you skip.
This is the batch pass. ../reddit-engage/ is the interactive mode: full 2–5 sentence comments, approved one by one. This skill runs earlier and wider: every classified op gets either a short template the human edits into a real comment, or a NO-REPLY gate with a note. The skip discipline is half the value. Wraps replies.py in the reddit-buyer-signals starter: the script scaffolds and checks; the agent writes the words.
Inputs
data/ops_classified.json— classified opportunities (op_id,lane,subreddit,summary,permalink)- Optional: a client sheet id (or
data/sheet_url.txt) for the Suggested Replies tab - The client's offer context and voice profile, same as every other drafting surface
How to run
cd starters/reddit-buyer-signals
python3 replies.py scaffold --ops data/ops_classified.json --out data/suggested_replies.json
# the agent writes every empty reply slot, then:
python3 replies.py check data/suggested_replies.json --ops data/ops_classified.json
python3 replies.py sheet --ops data/ops_classified.json --replies data/suggested_replies.json --sheet-id <id>
python3 replies.py angles --ops data/ops_classified.json --replies data/suggested_replies.json --out data/engage_angles.json
The 18-word cap (binding)
Target 15–18 words; 18 is the hard limit (wc -w semantics: whitespace-separated words). Longer templates read contrived, and the human is going to edit anyway. Render the count as <N>/18 next to every draft you present. Check it, don't eyeball it:
printf '%s' "<reply>" | wc -w
check enforces the same count and exits nonzero on 19+.
Reply gates
Deterministic from the action lane; overrides beat the lane. NO-REPLY is a result, not a failure — log it and move on.
| Gate | Lane rule | Note on the row |
|---|---|---|
| GO | engage_now / reply_now |
Timely thread. Reply when ready. |
| REVIEW | every other lane | Check thread age + sub self-promo rules first. |
| NO-REPLY | competitor_intel / competitor_watch |
Log as competitor intel. Do not post. |
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.
- 3d ago First seen · 104 lines · 147 tokens per session scan A 38080cc98bf3
reply-engine is a skill published in the GitHub repository shawnla90/gtm-coding-agent (139 stars, last pushed 13d ago), licensed MIT. It adds 147 tokens to every session and 1,827 once invoked, about $0.0007 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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