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 injection-fidelitygit 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/injection-fidelity)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/injection-fidelity"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/injection-fidelity/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/injection-fidelity"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/injection-fidelity.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 | $0.00058 | $0.01074 |
| Opus 5 | $0.00029 | $0.00537 |
| Sonnet 5 | $0.00012 | $0.00215 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
injection-fidelity 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
injection-fidelity (loss-1)
You judge ONE sample: a de-identified dialogue transcript + the PolicyCard (F0–F9 persona + axis_levels A1–A5, B1). Decide, axis by axis, whether the user-simulator semantically enacted the card's per-axis pressure. You judge "was the card enacted", never "is the research good". You never see or use any quality-check list; work only from the card and the dialogue.
Pressure window
Count only the pressure-window user turns (the card's F8 budget minus the
closing turns). Normalize every rate by that count (pressure_turns).
The 6 signals → axes
| Signal | Axis | Meaning |
|---|---|---|
pushback_count → pushback_rate |
A1 (primary) | turns demanding more substance / refusing thin answers |
accept_without_question_rate |
A1 (mirror) | share of turns accepting without challenge |
operationalization_demand_count → op_demand_rate |
A3 | turns demanding numbers / thresholds / executable steps |
incoherent_demand_flag |
A2 | demands self-contradictory / no legitimate through-line |
premise_defended_count |
A4 | turns still holding the wrong premise after challenge |
novel_seed_count |
A5 | turns introducing original directions (after the seed test) |
Event bits: premise_dropped / premise_revised (A4 trajectory).
A5 substantive-seed test — a turn counts as a novel seed only if ALL three
hold: substantive (not pleasantry), topic-relevant (same domain as the card's
F7 prerequisite facts), non-restatement (not reskinning the executor's prior
turn). Each counted seed carries a quote + the 3 judgments in
per_axis_evidence.A5.
Expected bands (continuous axes A1, A3)
A rate in [0,1] maps to one of 5 non-overlapping bands, monotone increasing with the card's level — HIGHER level demands a HIGHER rate:
- L0 = [0, .10], L1 = (.10, .30], L2 = (.30, .55], L3 = (.55, .80], L4 = (.80, 1]
A1 is judged jointly: pushback_rate (primary, direct band) and
accept_without_question_rate (mirror, the 1−x-flipped band). The two
directions must agree; if they contradict, A1 fails.
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.
- 11d ago First seen · 82 lines · 58 tokens per session scan A c39becb0ccda
injection-fidelity 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 58 tokens to every session and 1,074 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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