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 shawnla90/gtm-coding-agent --skill reply-enginegit clone --depth 1 https://github.com/shawnla90/gtm-coding-agentWrote 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/shawnla90/gtm-coding-agent/reply-engine)<a href="https://agentmods.dev/skills/shawnla90/gtm-coding-agent/reply-engine"><img src="https://agentmods.dev/badge/skills/shawnla90/gtm-coding-agent/reply-engine/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/shawnla90/gtm-coding-agent/reply-engine"><img src="https://agentmods.dev/badge/skills/shawnla90/gtm-coding-agent/reply-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 101 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00147 | $0.01827 |
| Opus 5.5 | $0.00059 | $0.00731 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (156 stars, last pushed 1mo ago), licensed MIT. It adds 147 tokens to every session and 1,827 once invoked, about $0.0006 per session on Opus 5.5. 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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