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/hoavdc/codexkit/codexkit-kanban-flow-analyzernpx skills add hoavdc/CodexKit --skill codexkit-kanban-flow-analyzergit clone --depth 1 https://github.com/hoavdc/CodexKitWhat 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 | $0.00077 | $0.00998 |
| Opus 5 | $0.00039 | $0.00499 |
| Sonnet 5 | $0.00015 | $0.00200 |
| Haiku 4.5 | $0.00008 | $0.00100 |
Grade A, and why
codexkit-kanban-flow-analyzer 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 2d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kanban Flow Analyzer
Purpose
Transform raw task completion data into actionable flow insights — find bottlenecks, set WIP limits, and forecast delivery dates with confidence intervals.
When to use
- team wants to reduce cycle time or improve delivery predictability
- WIP is growing and tasks are stalling in certain board columns
- management asks "when will this be done?" and the team has no data-driven answer
- transitioning from time-boxed sprints to continuous flow
When not to use
- sprint planning with story points (use sprint-planning-assistant)
- strategic roadmap or portfolio prioritization
- teams with fewer than 4 weeks of historical data
Inputs
- task data with start date and completion date (minimum 4 weeks history)
- board column names and WIP limits (if any)
- team size and working days per week
- specific questions or concerns about flow
Procedure
- Calculate 4 core flow metrics:
- Cycle Time: days from "In Progress" to "Done" — report P50 (median) and P85
- Throughput: items completed per week — report average and trend
- WIP: items currently in progress — compare to team capacity
- Flow Efficiency: (active work time / total elapsed time) × 100%
- Typical: 15–25% | High-performing: > 40%
- Apply Little's Law: Cycle Time = WIP ÷ Throughput
- If cycle time is high but throughput is stable, WIP is too high
- Detect bottlenecks using Theory of Constraints (5 Focusing Steps):
- Identify the column with highest average queue time
- Exploit: maximize throughput at the constraint
- Subordinate: slow upstream to match constraint capacity
- Elevate: add capacity or change process at constraint
- Repeat: new constraint will emerge
- Set WIP limits: initial recommendation = team size × 1.5 per column, then adjust based on data.
- Forecast delivery using Monte Carlo simulation:
- "If avg throughput = 12 items/week, 60-item backlog takes ~5 weeks at P85 confidence"
What ships with it
1 file 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.
- 2d ago First seen · 100 lines · 77 tokens per session scan A b184ff0bb5f4
codexkit-kanban-flow-analyzer is a skill published in the GitHub repository hoavdc/CodexKit (21 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 998 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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