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 paultyng/skill-issue --skill active-readgit clone --depth 1 https://github.com/paultyng/skill-issueWrote 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/paultyng/skill-issue/active-read)<a href="https://agentmods.dev/skills/paultyng/skill-issue/active-read"><img src="https://agentmods.dev/badge/skills/paultyng/skill-issue/active-read/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/paultyng/skill-issue/active-read"><img src="https://agentmods.dev/badge/skills/paultyng/skill-issue/active-read.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02320 |
| Opus 5.5 | $0.00000 | $0.00928 |
| Sonnet 5.5 | $0.00000 | $0.00464 |
| Haiku 4.5 | $0.00000 | $0.00232 |
Grade A, and why
active-read 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Active Read
Drive an interactive study session over a document or topic via active recall: ingest → section-by-section summary → typology-driven understanding questions → user-led pace → synthesis. Honest evaluation, not flattery.
Workflow
1. Intake
Capture four dimensions before ingesting the source:
- source — file path, URL, Notion page, or pasted prose.
- goal — decide / understand / evaluate / brief someone else.
- time budget — 15 min / 1 hour / multi-session.
- familiarity — cold / warm / refresher (warm = some prior exposure; refresher = solid prior exposure, want a recap).
Source detection rules:
- Path-looking arg with a file ext (e.g.
.pdf,.md,.txt) → local file; read via theReadtool. - URL schema (
http://,https://) → web source; fetch viaWebFetch. notion.so/host or Notion URL pattern → Notion page; fetch viamcp__notion__notion-fetch.- Else → pasted prose fallback; treat the user's text in-conversation as the source.
If the user invokes with no source, ask once and stop until they provide one. Do not infer.
2. Ingest & map
Read the source and produce a section map before any questions.
Section detection:
- First-pass: split on
##headings (level-2 markdown). Each heading + its body is one section. - Fallback when no
##headings exist: split into ~2k-char chunks at paragraph boundaries.
Chunking for large sources:
- For sources >15k chars, fan out parallel Haiku subagents on ~6k-char chunks. Each subagent produces a per-section "what + why this matters" one-line summary. Merge results into a single section map.
- Smaller sources: read inline and produce the map directly.
Thesis extraction (one-shot, here):
Extract the source's central thesis — the one sentence the author would write on a sticky note if asked "what's the point?" — and store it in conversation state for use in synthesis (Task 6 / Section 4). Do NOT re-extract at synthesis time. Anchoring risk: re-extracting after the user has written their own summary risks the agent anchoring on the user's phrasing rather than the source's actual claim. Extract once, freeze.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 138 lines · 0 tokens per session scan A 44cbffd3bfde
active-read is a skill published in the GitHub repository paultyng/skill-issue (8 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,320 tokens. 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 2026-08-31.
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