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/dancolta/subscope/tunenpx skills add dancolta/subscope --skill tunegit clone --depth 1 https://github.com/dancolta/subscopeWhat 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 | $0.00103 | $0.01457 |
| Opus 5 | $0.00051 | $0.00728 |
| Sonnet 5 | $0.00021 | $0.00291 |
| Haiku 4.5 | $0.00010 | $0.00146 |
Grade A, and why
subscope-tune scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/subscope-tune
Targeted feedback. You mark only the surfaces that stand out (wrong or great), the engine nudges per-sub weights, and your config gets sharper without re-running the full /profile interview. Anything you skip is left untouched.
When to use
The daily list (/subscope-run) is mostly fine but a few surfaces are off. Instead of editing YAML by hand: tune. Flag the wrong ones, optionally the great ones, ignore the rest.
Procedure
Step 1: Pull recent surfaces
cd "$CLAUDE_PLUGIN_ROOT" && PYTHONPATH=engine python3 -c "
import json
from subscope.lib import store
with store.connect() as conn:
rows = store.hot_surfaces(conn)[:15]
print(json.dumps([{
'id': r['post_id'],
'subreddit': r['subreddit'],
'title': r['title'],
'url': r['url'],
} for r in rows], indent=2))
"
If the pool is empty, tell the user: "No surfaces in the recent pool yet. Run /subscope-run first, then come back."
Step 2: Present in chat (mark only what stands out)
Render the recent surfaces as a clean numbered list. Make clear that marking is OPTIONAL and partial: the user flags only what is off (and any standouts), and skipping changes nothing.
Your recent surfaces. Mark only the ones that are off, or great. Skip the rest, they stay as-is.
b = surface less of this g = surface more (skip = leave alone)
1. r/RevOps "Anyone else drowning in HubSpot ops debt?"
2. r/SaaS "Looking for a fractional RevOps person"
3. r/Entrepreneur "How to find first 10 customers?"
4. r/RevOps "Mass-update HubSpot deals via API?"
5. r/sales "Apollo just hiked us 40 percent"
Reply with just the ones worth flagging, e.g. 3b or 3b 5g (spaces or commas).
Reply "done" to finish without changes.
Rules:
- Lead with the one-line instruction. State plainly that partial feedback is fine and skipping is the default.
- Do NOT render a
[ ? ]placeholder on every row. That implies the user must rate all of them, which is exactly the friction we are removing. Plain numbered list only. - Do not force a fixed count or rounds. Show what is in the pool (up to ~15).
- If a row was flagged in an earlier pass this session, you may show its mark in brackets (e.g.
[b]), but never require the user to re-confirm it.
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.
- 2d ago First seen · 122 lines · 103 tokens per session scan A 96cef91894dd
subscope-tune is a skill published in the GitHub repository dancolta/subscope (25 stars, last pushed 21d ago), licensed MIT. It adds 103 tokens to every session and 1,457 once invoked, about $0.0005 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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