Agent skill

Estimate Calibrator

Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions.

MITAuto-check passedData & Analytics

Install Estimate Calibrator

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/estimate-calibrator .claude/skills/estimate-calibrator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .claude/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/estimate-calibrator .agents/skills/estimate-calibrator && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .agents/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/estimate-calibrator .cursor/skills/estimate-calibrator && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .cursor/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/Mathews-Tom/armory.git --path skills/estimate-calibrator

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/estimate-calibrator .gemini/skills/estimate-calibrator && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .gemini/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/estimate-calibrator .github/skills/estimate-calibrator && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .github/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Mathews-Tom/armory --skill estimate-calibrator -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Mathews-Tom/armory estimate-calibrator --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/estimate-calibrator .opencode/skills/estimate-calibrator && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "estimate-calibrator" agent skill from https://github.com/Mathews-Tom/armory/tree/main/skills/estimate-calibrator into .opencode/skills/estimate-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "estimate-calibrator", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: estimate-calibrator
GitHub stars: 329
Token cost: ~2.1k tokens
SKILL.md length: 640 words
Files: 6 (incl. references)
Skills in repo: 80
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions.

Works in 5 steps: Decompose Work → Three-Point Estimate → Identify Unknowns → …
: estimate this
SKILL.md covers Reference Files, Prerequisites, Workflow and Output Format, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Estimate Calibrator is an agent skill from Mathews-Tom/armory. Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions. Triggers on: "estimate this", "how long will this take", "effort estimate", "confidence interval", "story points", "t-shirt sizing". NOT for task decomposition, use task-decomposer.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `evals/cases.yaml`, `references/calibration-tips.md` and `references/estimation-methods.md`).

It sits in Data & Analytics, covering Statistics and Task breakdown. The repository describes itself as: Curated, production-grade skills for AI coding agents. Battle-tested workflows for developers who use AI seriously. The licence is MIT.

When your agent uses it

: estimate this
How long will this take
Effort estimate
Confidence interval

Example prompts

“estimate this”
“how long will this take”
“effort estimate”
“/estimate-calibrator”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

Decompose Work
Three-Point Estimate
Identify Unknowns
Calculate Ranges
Assign Confidence

What it can do on your machine

Read from SKILL.md and the folder at commit 4594fb7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Estimate Calibrator loads about 2.1k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 640 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~81
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~7.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Mathews-Tom/armory at commit 4594fb7, republished under its MIT licence (© Mathews-Tom). 640 words, ~2,054 tokens.

Download SKILL.mdSave it as .claude/skills/estimate-calibrator/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: estimate-calibrator
description: Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions. Triggers on: "estimate this", "how long will this take", "effort estimate", "confidence interval", "story points", "t-shirt sizing". NOT for task decomposition, use task-decomposer.
metadata.version: 1.1.1
metadata.category: development
metadata.tags: estimation, pert, confidence-interval, planning
metadata.difficulty: intermediate
metadata.phase: plan

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"

Key rule: Worst case is NOT "everything goes wrong." It's the realistic bad scenario (90th percentile), not the apocalyptic one (99th percentile).

Phase 3: Identify Unknowns

Categorize unknowns that affect estimates:

Category	Example	Impact
Technical	"Never used this library before"	Likely case inflated, worst case much higher
Scope	"Requirements may change"	All estimates may shift
External	"Depends on API access from partner"	Blocking risk — could delay entirely
Integration	"Haven't tested with production data"	Hidden complexity at integration
Organizational	"Need design approval"	Calendar time, not effort time

Phase 4: Calculate Ranges

For individual tasks, use the PERT formula:

text

Expected = (Best + 4 × Likely + Worst) / 6
Std Dev = (Worst - Best) / 6

For aggregate (project) estimates:

Sum of expected values for total expected duration
Root sum of squares of std devs for aggregate uncertainty

Phase 5: Assign Confidence

Confidence	Meaning	When
High	Likely case within ±20%	Well-understood task, team has done it before
Medium	Likely case within ±50%	Some unknowns, moderate familiarity
Low	Likely case within ±100% or more	Significant unknowns, new technology

Output Format

text

## Estimate: {Work Item}

### Summary
| Scenario | Duration |
|----------|----------|
| Best case | {time} |
| Likely case | {time} |
| Worst case | {time} |
| **PERT expected** | **{time}** |
| **Confidence** | **{High/Medium/Low}** |

### Task-Level Estimates

| # | Task | Best | Likely | Worst | PERT | Unknowns |
|---|------|------|--------|-------|------|----------|
| 1 | {task} | {time} | {time} | {time} | {time} | {key unknown or "None"} |
| 2 | {task} | {time} | {time} | {time} | {time} | {key unknown} |
| | **Total** | **{sum}** | **{sum}** | **{sum}** | **{pert}** | |

### Key Unknowns

| # | Unknown | Category | Impact on Estimate | Mitigation |
|---|---------|----------|-------------------|------------|
| 1 | {unknown} | {Technical/Scope/External} | +{time} if realized | {spike, prototype, early test} |

### Assumptions
- {Assumption 1 — what must be true for this estimate to hold}
- {Assumption 2}

### Risk Factors
- {Risk}: If realized, adds {time}. Likelihood: {High/Medium/Low}.

### Confidence Rationale
**{High/Medium/Low}** because:
- {Specific reason — e.g., "Team has built 3 similar features"}
- {Specific reason — e.g., "External API is a new integration"}

### Recommendation
{Commit to PERT expected with {X}% buffer, or spike the top unknown first.}

Show full SKILL.md (267 more words)Show less

Calibration Rules

Three points, not one. Single-point estimates are always wrong. Three points communicate uncertainty — the most important part of any estimate.
Worst case is the 90th percentile, not the 99th. "Asteroid hits the office" is not a useful worst case. "The API documentation is wrong and we need to reverse-engineer the protocol" is realistic worst case.
Unknowns inflate estimates more than known difficulty. A hard but well-understood task is more predictable than an easy but novel one.
Estimates are not commitments. Communicate ranges, not deadlines. If stakeholders need a single number, give the PERT expected plus a buffer for confidence level.
Spike unknowns early. If a single unknown dominates the estimate range, invest 1-2 days spiking it before estimating the rest.

Error Handling

Problem	Resolution
Work item not decomposed	Decompose into 3-8 tasks first (or suggest task-decomposer skill).
No historical reference	Estimate relative to a known task: "This is about 2x the auth feature."
Stakeholder wants a single number	Provide PERT expected with buffer matching confidence level (High: +20%, Medium: +50%, Low: +100%).
Estimate seems too large	Check for scope creep in task list. Remove non-essential tasks. Identify what can be deferred.
Team has never done this type of work	Mark confidence as Low. Recommend a spike before committing to an estimate.

When NOT to Estimate

Push back if:

The work is exploratory (research, spikes) — timebox instead of estimating
Requirements are completely undefined — define scope first
The user wants precision (hours) for a large project — provide ranges, not false precision
The estimate will be used as a commitment without acknowledging uncertainty

Files

SKILL.md and 5 other files (references) in skills/estimate-calibrator of Mathews-Tom/armory.

SKILL.md
evals/cases.yaml
references/calibration-tips.md
references/estimation-methods.md
references/sizing-heuristics.md
references/unknown-categories.md

Open the folder on GitHubat commit 4594fb7

Compare with similar skills

Estimate Calibrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Estimate Calibrator compared with similar skills
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Statistical Analysisspacering-net/codeg	3.9k	3 repos	~5k	Automated safety check: Pass	MIT	today
StatsmodelszLanqing/codex-claude-academic-skills	4.7k	15 repos	~4.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
AI Daily DigestvigorX777/ai-daily-digest	1.6k	—	~1.3k	Automated safety check: Pass	None	7 mo ago

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Questions about Estimate Calibrator

What does Estimate Calibrator do?

Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions. Estimate Calibrator is an agent skill from Mathews-Tom/armory. Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions.

When should I use Estimate Calibrator?

Estimate Calibrator fits situations like: : estimate this; how long will this take; effort estimate; confidence interval.

How do I install Estimate Calibrator in Claude Code?

Run `npx skills add Mathews-Tom/armory --skill estimate-calibrator -a claude-code`. Or copy the skill folder (skills/estimate-calibrator in Mathews-Tom/armory) into .claude/skills/estimate-calibrator in your project. Claude Code loads it when a task matches its description.

How do I install Estimate Calibrator in Codex?

Run `npx skills add Mathews-Tom/armory --skill estimate-calibrator -a codex`. Or copy the skill folder (skills/estimate-calibrator in Mathews-Tom/armory) into .agents/skills/estimate-calibrator in your project. Codex loads it when a task matches its description.

Can I use Estimate Calibrator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mathews-Tom/armory --skill estimate-calibrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/estimate-calibrator, .gemini/skills/estimate-calibrator, .github/skills/estimate-calibrator and .opencode/skills/estimate-calibrator in your project.

What does Estimate Calibrator need to run?

SKILL.md names no scripts, command-line tools or credentials: Estimate Calibrator is instructions for the agent only.

Does Estimate Calibrator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Estimate Calibrator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Estimate Calibrator use?

Estimate Calibrator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Estimate Calibrator use?

About 2.1k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.1k tokens, read only when the agent opens those files.

What are the alternatives to Estimate Calibrator?

Skills that share tags, products or a category with Estimate Calibrator: Querying Indonesian Gov Data (suryast/indonesia-gov-apis, 172 stars), Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars) and Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Estimate Calibrator?

Mathews-Tom (a GitHub user) maintains it in Mathews-Tom/armory, which has 329 GitHub stars. The repository holds 80 skills in this directory. The repository was last updated on October 6, 2026.

Source: Mathews-Tom/armory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Estimate Calibrator

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Estimate Calibrator

Reference Files

Prerequisites

Workflow

Phase 1: Decompose Work

Phase 2: Three-Point Estimate

Phase 3: Identify Unknowns

Phase 4: Calculate Ranges

Phase 5: Assign Confidence

Output Format

Calibration Rules

Error Handling

When NOT to Estimate

Files

Compare with similar skills

Similar skills

More from Mathews-Tom/armory

Related

Questions about Estimate Calibrator