RiskShard
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RiskShard audits public cyber-loss reports, recording what each one actually publishes across four statistical questions, with every answer pinned to a quoted source.
python scripts/riskshard_modules.py tail riskshard validate_evidence riskshard contributor_preflight
Summary
RiskShard is an open-source cyber tool that audits public cyber-loss reports to document what specific statistical measures each source publishes, pinning every answer to the source passage for verification. It does not require installation and operates by reading documentation directly, positioning itself as an alternative to manual source reading in the integrated-risk-management space. It is aimed at those who must cite risk figures, like consultants or risk managers, and the project has seen 9 releases in the tracking period, with the last update occurring 17 days ago.
RiskShard audits public cyber-loss reports, recording what each one actually publishes across four statistical questions, with every answer pinned to a quoted source.
How RiskShard helps
Does the loss figure I am about to cite actually give a most-likely value, or only an average?
looks up the source in the audited set and reports which of the four measures it publishes, quoting the exact passage — or states plainly that the source never says
Is the population behind this number even the one I think it is?
checks whether the source discloses which countries, industries or company sizes were measured, and pins that answer to the quoted text so you can see who was actually counted
Does this report tell me how the risk is spread, or just hand me one number?
records whether the source shows a distribution of losses at all, distinct from giving a single average or median
Can this source tell me how often losses exceed a given size?
records whether the source publishes any exceedance statement, so you know if it can support a "losses bigger than X happen Y% of the time" claim
How much is a shard's answer really resting on the biggest loss in the data?
flags entries whose stated figures lean on the maximum observed value rather than a distribution, so you can see when "up to $X" is standing in for a real estimate
Has anyone already read this report, or am I the first?
looks the source up against the audited list and either returns the pinned, quoted answers already on file or tells you it hasn't been read yet, so you know whether to trust a figure or go read it yourself
Languages
Command line
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Score card
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Shipping velocity releases a month, last 12 months
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Documentation quality release-notes rubric, 0–100
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Breaking-change discipline how rarely a release breaks users, and notice given
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Maintenance risk quiet time against its own cadence, maintainers
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Innovation & surface new capabilities, API and MCP surface
- Community health
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Contributor trend distinct committers, this year against last
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Maintainer concentration bus factor now against a year ago
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Issue backlog trend issues closed per issue opened, 12 months
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Responsiveness median time to a maintainer’s first reply
Nine scores for RiskShard, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.
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Blank means never measured; nothing here is a filled-in zero.
Peer standings are included with Researcher and Business. See how this tool ranks against its competitors, using data we don't publish anywhere else.
See plans →Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.
How this is scored
- Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
- Documentation quality — the release-notes rubric published on the analytics page, unchanged.
- Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
- Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
- Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
- Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
- Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
- Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.
Release-note detail over time
See how much detail RiskShard puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.
Checking release-note history…
Releases
7 entries
No entries match these filters.
- September 20262
- August 20264
- July 20261
Project health
Issue, contributor and maintainer activity in raviaxo/RiskShard, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.
- Contributors 1 +1 vs prior 12 months
- Maintainers 1 +1 joined · −0 left, last 12 months
- Bus factor 1 committers behind half the commits
- Community-filed 0% of issues opened, last 12 months
- Median time to close 53 days first maintainer reply 53 days
- Open backlog 5 of 15 issues ever filed
- Issues opened / month 0 · 0 communitymaintainers
- Open backlog 5
- Contributors / month 1
- Commits / month 6
- Stars 3
- other 100%
Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this
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