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/ankitclassicvision/ankit_shared_skills/calibernpx skills add AnkitClassicVision/ankit_shared_skills --skill calibergit clone --depth 1 https://github.com/AnkitClassicVision/ankit_shared_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/ankitclassicvision/ankit_shared_skills/caliber)<a href="https://agentmods.dev/skills/ankitclassicvision/ankit_shared_skills/caliber"><img src="https://agentmods.dev/badge/skills/ankitclassicvision/ankit_shared_skills/caliber.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00061 | $0.00671 |
| Opus 5 | $0.00030 | $0.00336 |
| Sonnet 5 | $0.00012 | $0.00134 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
caliber 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 6d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CALIBER
CALIBER is a model-routing skill. It turns a request into a task-surface demand vector, compares that vector against a model capability matrix, and returns the cheapest constructor that can clear the task with the right defender.
A constructor is:
model + deployment path + context + retrieval + tools + verifier + human gate
Core rule
Do not ask “what is the best model?” Ask:
What is the cheapest allowed constructor whose proven capabilities clear this task's required surfaces and whose defender can verify the result?
Demand levels
| Level | Name | Meaning |
|---|---|---|
| -1 | deterministic | No model; use SQL/rules/regex/script/API directly. |
| 0 | tiny | Simple classify/extract/rewrite/routing. |
| 1 | small | Simple reasoning, short summaries, low-stakes drafts. |
| 2 | workhorse | Normal professional work, routine code/specs, common RAG. |
| 3 | strong specialist | Harder domain work, repo work, complex synthesis, multimodal reasoning. |
| 4 | frontier | Expensive-to-fail planning, novel architecture, high-reliability synthesis. |
| 5 | orchestrated frontier | Decomposition + tools + defenders + human gates. |
| 6 | reserved future | Future super-frontier/research-grade tier. |
Workflow
- Gate the deployment pool: regulated/private data, local-only requirements, secrets, production writes, or external sends may restrict allowed models.
- Break the task into surfaces such as reasoning, coding, long context, tool use, media, structured output, local/private, or compliance.
- Assign required level per surface.
- Filter candidate models/deployments from
references/model-capability-matrix.yaml. - Attach defenders from
references/defender-registry.md. - Pick the cheapest viable constructor.
- If the defender fails, escalate one tier or switch specialist lane.
Output shape
Task surfaces:
- <surface>: required level <n> — <why>
Gate pool:
- allowed deployments:
- excluded deployments:
Candidate comparison:
- cheapest viable:
- strongest reliable:
- local/private option:
- specialist option:
Defenders:
- required verifier(s):
- human gate:
Recommendation:
- primary constructor:
- fallback constructor:
- escalation rule:
- evidence confidence:
What ships with it
8 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.
- references/benchmark-source-ledger.md 2.0 KB
- references/defender-registry.md 2.0 KB
- references/demand-card.md 8.3 KB
- references/model-capability-matrix.yaml 5.2 KB
- references/model-capability-schema.json 2.7 KB
- references/model-registry.md 2.4 KB
- references/routing-policy-v0.3.md 2.4 KB
- references/surface-taxonomy.md 5.1 KB
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.
- 6d ago First seen · 90 lines · 61 tokens per session scan A 3748a9750be3
caliber is a skill published in the GitHub repository AnkitClassicVision/ankit_shared_skills (11 stars, last pushed 25d ago), licensed MIT. It adds 61 tokens to every session and 671 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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