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 yogsoth-ai/de-anthropocentric-research-engine --skill keshav-three-passgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/keshav-three-pass)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/keshav-three-pass"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/keshav-three-pass/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/yogsoth-ai/de-anthropocentric-research-engine/keshav-three-pass"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/keshav-three-pass.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 | $0.00056 | $0.00553 |
| Opus 5 | $0.00028 | $0.00277 |
| Sonnet 5 | $0.00011 | $0.00111 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
keshav-three-pass 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.
What it actually says
Keshav Three-Pass
Read one paper in three passes of increasing depth. The outputs accumulate as prose rather than fixed fields; use a different tactic when cross-paper alignment matters more than understanding.
Orchestration Pattern
- Call
paper-fetchwithpaper_ref. Stop onnot_found. Createcontext/papers/<dir>/keshav-three-pass/on success. - Call
first-pass-skimwithsource_pathandmeta_path. Write01-first-pass-skim.md, recordingread_deeperin frontmatter. - If
read_deeperis false, stop by default. Continue only on explicit caller override and recordgate_overridden: true. - Call
second-pass-graspwith the paths andskim_notes; write02-second-pass-grasp.md. - Call
third-pass-deep-readwith the paths andgrasp_summary; write03-third-pass-deep-read.md.
Do not collapse pass 3 into a recap of pass 2. It must surface implicit assumptions, virtual re-implementation mismatches, and concrete improvements.
Output Layout
context/papers/<timestamp>-<title-slug>/
source.md
source.meta.json
keshav-three-pass/
01-first-pass-skim.md
02-second-pass-grasp.md
03-third-pass-deep-read.md
Each output carries sop, tactic, and written_at frontmatter. Report the
gate outcome, core claim, most consequential implicit assumption, unresolved
flags, and all output paths.
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 · 60 lines · 56 tokens per session scan A 744f11342304
keshav-three-pass is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 56 tokens to every session and 553 once invoked, about $0.0003 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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