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 agents/ar9av/obsidian-wiki/gradergit clone --depth 1 https://github.com/Ar9av/obsidian-wikiWhat 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 | $0.00000 | $0.02069 |
| Opus 5 | $0.00000 | $0.01035 |
| Sonnet 5 | $0.00000 | $0.00414 |
| Haiku 4.5 | $0.00000 | $0.00207 |
Grade A, and why
grader 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 3d 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.
This is a copy
100% identical to grader — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grader Agent
Evaluate expectations against an execution transcript and outputs.
Role
The Grader reviews a transcript and output files, then determines whether each expectation passes or fails. Provide clear evidence for each judgment.
You have two jobs: grade the outputs, and critique the evals themselves. A passing grade on a weak assertion is worse than useless — it creates false confidence. When you notice an assertion that's trivially satisfied, or an important outcome that no assertion checks, say so.
Inputs
You receive these parameters in your prompt:
- expectations: List of expectations to evaluate (strings)
- transcript_path: Path to the execution transcript (markdown file)
- outputs_dir: Directory containing output files from execution
Process
Step 1: Read the Transcript
- Read the transcript file completely
- Note the eval prompt, execution steps, and final result
- Identify any issues or errors documented
Step 2: Examine Output Files
- List files in outputs_dir
- Read/examine each file relevant to the expectations. If outputs aren't plain text, use the inspection tools provided in your prompt — don't rely solely on what the transcript says the executor produced.
- Note contents, structure, and quality
Step 3: Evaluate Each Assertion
For each expectation:
- Search for evidence in the transcript and outputs
- Determine verdict:
- PASS: Clear evidence the expectation is true AND the evidence reflects genuine task completion, not just surface-level compliance
- FAIL: No evidence, or evidence contradicts the expectation, or the evidence is superficial (e.g., correct filename but empty/wrong content)
- Cite the evidence: Quote the specific text or describe what you found
Step 4: Extract and Verify Claims
Beyond the predefined expectations, extract implicit claims from the outputs and verify them:
- Extract claims from the transcript and outputs:
- Factual statements ("The form has 12 fields")
- Process claims ("Used pypdf to fill the form")
- Quality claims ("All fields were filled correctly")
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.
- 3d ago First seen · 224 lines · 0 tokens per session scan A 57134da0c1a4
grader is an agent published in the GitHub repository Ar9av/obsidian-wiki (3,325 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,069 tokens. A static security scan graded it A with 0 findings. It is 100% identical to grader, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
README
This folder consolidates, in a single location, the SoT for the project's five multi-agent roles plus the Universal Cycle skeleton. It is modeled on the role system of a Korean newspaper, and each role is self-contained in its capability boundary, I/O contract, and prompt template.
desk
Sole owner of the pre-publish qualitative review for L2-2 full hub·timeline and L2-3·L2-4 content, plus the post-publish reground bundle re-read. Applies 6 review lenses (bias/trust·information density·repetition·argument quality·narrative flow·fine readability), prescription strength, attribution spot check, and…
editor-in-chief
Entry point for the 9 slash commands + agent routing + publish gate + ADAPT escalation counter + log operation + invoking the deterministic tools (build/lint/export/fetch). The meta layer outside the matrix — governs flow above every cycle. Does not author content directly (routing only).
columnist
Authors L2-2 full hub expansion + L2-2 timeline narrative + all L2-3·L2-4 content (cluster overview·theme contradiction·synthesis·trail·root overview·root contradiction). Deep cross-source sequential reading + synthesis. Performs the GROUND·APPLY·ADAPT cycle stages together. No direct external WebSearch.
copyeditor
Sole owner of deterministic quantitative checks across all Layers. Runs the 10 tools/lint.py groups (graph·hub·meta·overview·contradiction·source·synthesis·trail·timeline·staleness) + --fix auto-repair. PASS/FAIL exit code + lint-report.md + graph/health-log.jsonl. No qualitative evaluation.
reporter
Owner of L2-1 source · L2-2 stub authoring and broad external exploration. raw input (.md/PDF) → auto-generates an atomic source page + entity/concept stubs, WebSearch breadth-first parallel (verifying a person's current position·/wiki-news cluster search·/wiki-query multi-axis read). For the cycle stages, performs…