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 Kastalien-Research/thoughtbox --skill tastegit clone --depth 1 https://github.com/Kastalien-Research/thoughtboxWrote 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/kastalien-research/thoughtbox/taste)<a href="https://agentmods.dev/skills/kastalien-research/thoughtbox/taste"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/taste/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/kastalien-research/thoughtbox/taste"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/taste.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 | $0.00030 | $0.00474 |
| Opus 5.5 | $0.00012 | $0.00190 |
| Sonnet 5.5 | $0.00006 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
taste 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 1mo 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
Evaluate this research direction: $ARGUMENTS
Process
1. Determine Evaluation Depth
Based on the proposal, select the appropriate depth:
| Situation | Operations |
|---|---|
| Vague idea, needs sharpening | Compression test only |
| Specific technical proposal | Landscape + Dead-end estimation |
| Major resource commitment | Full pipeline (all 6 operations) |
| Stuck, need fresh angles | Cross-pollination only |
| Choosing between options | Prediction query only |
2. Run Operations (in order, skip per above)
Compression Test — Force into: "We believe [X] because [Y], and if we're right, [Z] follows." If it can't survive compression, verdict is defer.
Landscape Assessment — Use WebSearch and Exa (web_search_exa) to find adjacent work, abandoned approaches, new capabilities. What exists? What changed recently?
Prediction Query — Simulate success and failure worlds. Is the direction informative under both outcomes?
Dead-End Estimation — What's the most likely failure mode? What's the time-to-signal (not time-to-completion)?
Simplicity Audit — Is there an 80%-value simpler version? Apply recursively.
Cross-Pollination — Check against other domains. Structural resonance = real signal.
3. Stop Early
If the verdict becomes clear at any step, stop. Don't run more operations just to fill out the report.
4. Output
## Taste Evaluation: [Title]
**Verdict:** proceed | simplify | defer | kill
**Rationale:** [One sentence]
**Compression:** [Single-sentence formulation]
**Landscape:** [Position relative to existing work]
**Prediction:** Success -> [X] / Failure -> [Y]
**Time-to-signal:** [Estimate]
**Simplification:** [Cheaper version, if any]
**Cross-domain:** [Analogies, if checked]
**Next step:** [Concrete action]
Omit unchecked sections.
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.
- 1mo ago First seen · 59 lines · 30 tokens per session scan A 6841aee3990c
taste is a skill published in the GitHub repository Kastalien-Research/thoughtbox (65 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 474 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…
unity-yaml-editing
Last-resort guidance for safely hand-editing Unity YAML.
ad-policy-check
Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with…
render-3d-product-showcase
Assemble a premium 3D product-showcase ad from a config — four beat clips (an orbiting hero rotation, a macro push-in, a physics reveal, a typographic close) normalized to the chosen backdrop colour, hard-concatenated in order, closed on a deterministic Playwright brand end card, and mixed under one instrumental bed…
render-airdrop-carousel
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…