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 markoblogo/abvx-agent-skills --skill evidence-ledger-researchgit clone --depth 1 https://github.com/markoblogo/abvx-agent-skillsWrote 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/markoblogo/abvx-agent-skills/evidence-ledger-research)<a href="https://agentmods.dev/skills/markoblogo/abvx-agent-skills/evidence-ledger-research"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/evidence-ledger-research/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/markoblogo/abvx-agent-skills/evidence-ledger-research"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/evidence-ledger-research.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 | $0.00073 | $0.00594 |
| Opus 5 | $0.00036 | $0.00297 |
| Sonnet 5 | $0.00015 | $0.00119 |
| Haiku 4.5 | $0.00007 | $0.00059 |
Grade A, and why
evidence-ledger-research 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evidence Ledger Research
Use this skill when the answer must be tied to exact evidence rather than memory or broad summaries.
Source Discipline
- Prefer primary sources, official data pages, original PDFs, repository files, or provided/oracle documents.
- If a guessed exact-date search fails, broaden to the official series, table name, dataset code, or download page.
- Read only the narrow span needed after locating a promising source, then verify entity, period, basis, unit, and version.
- If a provided document contains the answer, extract from it before searching elsewhere.
- For volatile facts, verify the latest source before answering.
- If evaluating Doc-to-LoRA or another document-memory method, pair this with
doc-to-lora-evaluatorand keep the baseline questions, source answers, and negative controls in the ledger.
Evidence Ledger
Maintain a compact ledger for multi-step answers:
Claim/operand:
Source:
Date/period:
Unit/scale:
Role:
Value/span:
Notes:
Use the ledger before arithmetic, comparisons, ranking, or final formatting. Do not compute from partial evidence unless the user explicitly accepts an estimate.
Table And Document Rules
- Match row labels and exact column headers, not visual proximity alone.
- Watch for repeated month rows, fiscal vs calendar sections, continuation tables, estimates, totals, and footnotes.
- For forms and PDFs, locate the exact field/anchor in the question, then read the adjacent filled value from the same row, box, block, or table cell.
- For visual/document QA, prefer the smallest exact text span that answers the question.
- Preserve spelling, punctuation, capitalization, and numeric formatting when exact extraction matters.
Calculation Rules
- Confirm every operand before calculating.
- Track scale and direction: thousands/millions/billions, currency, exchange-rate orientation, sign, percent vs percentage points.
- Preserve relation direction: "change from A to B" means
B - A; "share accounted for by X" meansX / total. - For time windows, enumerate expected periods first and verify endpoint inclusion.
- Do not round intermediate values unless the source requires it.
What ships with it
2 files 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 · 58 lines · 73 tokens per session scan A 24917d440501
evidence-ledger-research is a skill published in the GitHub repository markoblogo/abvx-agent-skills (16 stars, last pushed 3d ago), licensed MIT. It adds 73 tokens to every session and 594 once invoked, about $0.0004 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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