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 skills/understudylabs/understudy-agent-tools/calibrate-difficultynpx skills add understudylabs/understudy-agent-tools --skill calibrate-difficultygit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWhat 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 | $0.00024 | $0.00374 |
| Opus 5 | $0.00012 | $0.00187 |
| Sonnet 5 | $0.00005 | $0.00075 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
calibrate-difficulty 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 2d 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
Calibrate difficulty
Use an existing local run artifact with explicit generic band labels. The
artifact is hashed and the report records that source binding. This is a
deterministic screening step: it makes no provider calls, never reads holdout
or customer data, and does not infer bands from benchmark-specific task IDs.
npm run build
node scripts/difficulty-calibration.mjs --run path/to/synthetic-run.json --out report.json
Scores at or above 0.95 are saturated only when the band has at least 10
scored rows; insufficient sample takes precedence and remains caution.
Only sufficiently sampled bands below the threshold are measurable and
invest. Keep fixture, split, scoring protocol, and source hash visible, and
use dev/train data for decisions—never holdout data.
Safety Gates
- Use only a frozen, source-bound train/dev run. Never use holdout rows or make provider calls from this calibration step.
- Predeclare the saturation threshold and minimum sample. An undersized band is
insufficient_sampleeven when its observed mean is perfect. - Treat the report as a spend-routing screen, not promotion evidence or proof that a model can beat the incumbent.
Resolve CLI
Build before invoking the repository-local script:
npm run build
node scripts/difficulty-calibration.mjs \
--run path/to/source-bound-dev-run.json \
--threshold 0.95 --min-sample 10 --out report.json
The script hashes the exact input bytes and writes a deterministic calibration
report apart from generated_at.
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.
- 2d ago First seen · 46 lines · 24 tokens per session scan A ad302ccaeb4d
calibrate-difficulty is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 24 tokens to every session and 374 once invoked, about $0.0001 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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