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 Mathews-Tom/armory --skill estimate-calibratorgit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/estimate-calibrator)<a href="https://agentmods.dev/skills/mathews-tom/armory/estimate-calibrator"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/estimate-calibrator/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/mathews-tom/armory/estimate-calibrator"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/estimate-calibrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 152 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00071 | $0.01736 |
| Opus 5 | $0.00036 | $0.00868 |
| Sonnet 5 | $0.00014 | $0.00347 |
| Haiku 4.5 | $0.00007 | $0.00174 |
Grade A, and why
estimate-calibrator 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 9d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Estimate Calibrator
Replaces single-point guesses with structured three-point estimates: decomposes work into atomic units, estimates best/likely/worst case for each, identifies unknowns and assumptions, calculates aggregate ranges using PERT, and assigns confidence levels with explicit rationale.
Reference Files
| File | Contents | Load When |
|---|---|---|
references/estimation-methods.md |
PERT formula, three-point estimation, Monte Carlo basics | Always |
references/unknown-categories.md |
Technical, scope, external, and organizational uncertainty types | Unknown identification |
references/calibration-tips.md |
Cognitive biases in estimation, historical calibration, buffer strategies | Always |
references/sizing-heuristics.md |
Common task size patterns, complexity indicators, reference class data | Quick sizing needed |
Prerequisites
- Work item description (feature, task, project)
- Decomposed tasks (or use task-decomposer skill first)
- Context: team familiarity, tech stack, existing codebase
Workflow
Phase 1: Decompose Work
If the work item is not already decomposed into atomic units:
- Break into tasks — Each task should be estimable independently.
- Right granularity — Tasks should be 1 hour to 3 days. Larger tasks have higher uncertainty; break them down further.
- Identify dependencies — Tasks on the critical path determine the minimum duration.
Phase 2: Three-Point Estimate
For each task, estimate three scenarios:
| Scenario | Definition | Mindset |
|---|---|---|
| Best case | Everything goes right. No surprises. | "If I've done this exact thing before" |
| Likely case | Normal friction. Some minor obstacles. | "Realistic expectation with typical setbacks" |
| Worst case | Significant problems. Not catastrophic. | "Murphy's law but not a disaster" |
What ships with it
5 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.
- 9d ago First seen · 168 lines · 71 tokens per session scan A 8fa90d730539
estimate-calibrator is a skill published in the GitHub repository Mathews-Tom/armory (316 stars, last pushed 3d ago), licensed MIT. It adds 71 tokens to every session and 1,736 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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