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capacity-planner

Use when an ops leader (Director of CX, Head of Support, VP Ops, Head of BizOps, Head of IT ops, Head of Finance ops) is sizing ops capacity, building a headcount plan, modeling utilization risk, planning Q3 capacity or annual support capacity, or designing CS coverage — and needs Erlang-C queueing math, P90 demand sizing, shrinkage-adjusted FTE, manager-trigger thresholds, and a quarterly hiring sequence with ramp + attrition. Apply when sustained team utilization is above 80% or when the team is growing >50% in 12 months. Run before committing the headcount budget. This is NOT engineering capacity (see vpe-advisor for DORA + cycle time) and NOT strategic 3-year workforce planning (see chro-advisor).

69

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, validation-heavy body: clear 5-step workflow with stop-conditions and a pre-commit question checklist, and an appropriately split bundle (references/assets/scripts all real and matching the documented interfaces). The main costs are token weight — the 'Distinct from' section and repeated canon citations duplicate information — and only one of three scripts having a copy-paste example invocation.

Suggestions

Trim the 'Distinct from' section to two or three lines per sibling skill (or fold it into the references) — it re-states differentiation already carried by the frontmatter description, and cut the repeated per-question 'Canon' lines since the canon is already consolidated in references/.

Add copy-paste example invocations for utilization_analyzer.py and hiring_sequencer.py alongside the existing capacity_modeler.py example, ideally with a minimal --input JSON snippet so users can run all three tools immediately.

Drop the 'cd business-operations/skills/capacity-planner && ' prefix from the quick example — it hard-codes a repo path that may not match where the skill is installed; a plain 'python3 scripts/capacity_modeler.py --sample' from the skill root is more robust.

DimensionReasoningScore

Conciseness

The body is dense with domain-specific facts Claude does not reliably know ("Anyone >85% sustained is a throughput-collapse risk", "Q1 35%, Q4 15%", "span of control crosses 7 ICs/manager"), so it avoids the concept-explanation sin. But it could be tightened: the "Distinct from" section largely re-states the frontmatter differentiation, canon citations repeat across questions (Reinertsen and Hopp & Spearman each cited three times), and the 'one at a time, write answers down' discipline is exhorted three times. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than 'minor instances' (4) or 'several padded sections' (2).

3 / 5

Actionability

The quick example is copy-paste executable ("cd business-operations/skills/capacity-planner && python3 scripts/capacity_modeler.py --sample") and the Scripts section documents concrete flags (--profile, --input, --output, --sample) that verifiably exist in the scripts. However, only one of the three scripts gets a ready-to-run invocation, no example --input JSON is shown inline, and the example's `cd` path assumes a repo location that may not match the installed skill. 'Mostly executable guidance with minor gaps' fits.

4 / 5

Workflow Clarity

The 5-step workflow is clearly sequenced with validation checkpoints: a stop-condition in step 1 ("If you only have averages, stop and pull the distribution"), a diagnostic branch in step 3 ("Spread >30 percentage points across team means UNBALANCED — fix that before hiring"), and the seven-question forcing library is an explicit pre-commit checklist with recommended answers and computed thresholds ("If P(breach at P90) > 10% you are understaffed"). This is an analysis skill, not a destructive/batch one, so the ≤3 validation cap does not apply; the content matches the 'explicit validation steps; feedback loops; checklists' anchor.

5 / 5

Progressive Disclosure

The split is appropriate and verified against the actual bundle: canon material lives one level deep in references/ ("queueing_theory_canon.md … The math", "ops_workforce_planning_canon.md … The people factors"), the fill-out template in assets/, and the anti-patterns are summarized inline as a 'Top eight' with the full taxonomy externalized — all referenced files exist. The forcing-question library inline is the skill's core discipline rather than misplaced detail. Clear overview, well-signaled one-level-deep references, easy navigation.

5 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete capabilities, explicit multi-clause triggers including numeric conditions (utilization >80%, growth >50%), and explicit disambiguation from adjacent skills. Voice is third-person/imperative with no first- or second-person phrasing. The only weakness is slightly missing natural synonyms such as 'staffing plan' or 'agent staffing'.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities — "sizing ops capacity, building a headcount plan, modeling utilization risk, planning Q3 capacity or annual support capacity, or designing CS coverage" — and the specific math deliverables ("Erlang-C queueing math, P90 demand sizing, shrinkage-adjusted FTE, manager-trigger thresholds, and a quarterly hiring sequence with ramp + attrition"). This matches the anchor for comprehensive coverage of concrete actions; there are no vague filler phrases, so it is not the level below ('minor gaps in coverage').

5 / 5

Completeness

It explicitly answers both what it does ("needs Erlang-C queueing math, P90 demand sizing, shrinkage-adjusted FTE, manager-trigger thresholds, and a quarterly hiring sequence with ramp + attrition") and when to use it with multiple concrete triggers ("Use when an ops leader … is sizing ops capacity", "Apply when sustained team utilization is above 80% or when the team is growing >50% in 12 months", "Run before committing the headcount budget"). This matches the top anchor; a 'Use when' clause is present, so the ≤3 cap does not apply.

5 / 5

Trigger Term Quality

Natural phrases an ops leader would say are well covered: "headcount plan", "sizing ops capacity", "utilization", "Q3 capacity", "annual support capacity", "CS coverage", plus role titles ("Director of CX, Head of Support, VP Ops"). A few common synonyms such as "staffing plan", "agent staffing", or "FTE model" are missing, which fits the 'good keyword coverage; a few natural terms missing' anchor rather than the comprehensive-synonyms anchor above.

4 / 5

Distinctiveness Conflict Risk

The niche is sharply defined — ops teams handling queued work — with explicit negative boundaries: "This is NOT engineering capacity (see vpe-advisor for DORA + cycle time) and NOT strategic 3-year workforce planning (see chro-advisor)". Clear niche with distinct triggers and minimal conflict risk, matching the top anchor.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

business-operations/skills/capacity-planner/SKILL.md

Reviewed

Table of Contents

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