Skills are the unit of intelligence
A skill is a packaged capability — attrition risk, sprint autopsy, org health score, what-if simulation. Around 70 ship in the box. Each one runs identically across the surfaces that invoke the runtime — API, chat, reports, and alerts. Author your own; sandbox them; ship them to your team. A community marketplace lands Q1 2027.
One skill, five surfaces
Most analytics platforms ship dashboards. A dashboard lives in one place — the web UI — and rewrites itself when you want it in Slack, in a PDF, in an AI chat, or in an MCP tool. Four implementations of the same logic, four places it can drift.
Gitrevio ships skills. A skill declares its inputs and outputs as JSON Schema and a prompt template. The same skill is callable from the REST API, from any MCP client, from AI chat, scheduled inside a report, or triggered by an alert rule.
This is structural leverage. Every new skill compounds across all five surfaces for every customer — no per-channel rewrite, no drift.
# skill: attrition_risk
name: attrition_risk
version: 1.4.0
prompt_version: 3
inputs:
- contributor_id: string
- horizon_days: int = 90
outputs:
- score: float [0, 1]
- factors: shapley[]
- recommended_actions: string[]
runs_in:
- api
- mcp
- chat
- reports
- alerts
~70 builtin skills
Each ships day-one with golden tests, axiom verification where math applies, and a cost-attributed runtime. Mix and match in chat, schedule in reports, trigger from alerts.
Per-contributor risk score with Shapley factors. Engagement, review bottleneck, peer attrition, tenure plateau.
Sprint commitment vs delivery, drift broken into scope creep, estimation error, blockers, dependencies, PTO.
Retro narrative auto-generated at sprint end. Data-driven root cause analysis.
Signals composed into one drillable score with trend and per-component decomposition.
Per-PR risk score from file hotspots, author history, review coverage, test impact, change complexity.
Lizard complexity + churn + AI-tagged TODO/FIXME. Velocity cost ranking.
File expertise + queue depth + review-quality history + AI-authorship match + timezone.
Per-engineer ramp tracking with intervention recommendations.
Z-score + EWMA on key metrics with Bayesian smoothing. Alerts include attribution.
Chains an anomaly through the metric dependency graph to the upstream signal behind it.
Entropy of commit patterns per developer. Privacy-gated; opt-in by team.
Event-type classification — commit, PR, review, issue, chat, doc — inferred from work patterns.
AI-assistant adoption + acceptance + ROI per contributor and team.
File × contributor expertise reduced to single-points-of-failure. Cross-train recommendations.
Reachability graph of who can ship what. Risk-weighted by criticality.
K-means / GMM clustering yields behavioral archetypes per org. Data-driven, not titles.
Lognormal MLE per team. p50/p75/p90 completion dates, calibrated, with risk-factor decomposition.
Scenario modelling — departure, hiring, reassignment, vacation, AI rollout — over the org graph.
Statistical attribution over registered factors, backed by the axiom-verified Shapley library.
BOCPD change-point detection with calibrated run-length posteriors. No hand-tuned thresholds.
Cox PH + Kaplan-Meier on time-to-merge and time-to-resolve. Per-team hazard ratios.
Doubly-robust AIPW cohort-lift with propensity matching and bootstrap confidence intervals.
Files that consistently change together, surfacing implicit architectural coupling.
High-churn, high-complexity files ranked by refactoring payoff.
Who knows what, and how concentrated that knowledge is, from commit + review history.
Executive rollup: org health, AI impact, delivery, and risk register in one narrative.
Ranked engineering risks with owners and mitigation state for leadership review.
Cross-team portfolio view: velocity, quality trends, dependencies, hiring signals.
Self-service contributor dashboard — privacy-scoped views of your own work, focus, and queue.
Per-team capacity projection with p50/p75/p90 delivery windows.
# Author and install a custom skill
$ cat skills/standup_summary.gskill.yaml
name: standup_summary
version: 0.1.0
inputs: [team_id]
outputs: [highlights, blockers, owners]
prompt: prompts/standup.md
cost_cap_usd: 0.05
$ gitrevio skills package skills/standup_summary
→ standup_summary-0.1.0.gskill (4.2 KB)
$ curl -X POST https://api.gitrevio.com/v1/skills/install \
-H "Authorization: Bearer gr_live_..." \
-F "package=@standup_summary-0.1.0.gskill"
→ installed: standup_summary v0.1.0
# Now visible in MCP, chat, reports, alerts
Custom skills via YAML
Write a manifest, validate it against JSON Schema, install it, run it. No arbitrary code execution — skills are sandboxed and the runtime enforces input/output contracts, content rules, and hallucination checks before anything leaves the box.
Manifest format is YAML, validated against a public JSON Schema. Inputs and outputs are typed; prompt files are referenced, not embedded; cost caps are enforced at runtime and tracked per customer per channel.
.gskill packaging is a tarball with a 10 MB cap and
zip-slip protection. Install via web UI, REST, or
gitrevio_skill_install in MCP.
Versioned per-customer. Skill version and prompt version are independent — iterate on prompts without bumping API contracts. Five example skills + an authoring guide ship with the platform.
12-step runtime, ≥85% coverage gate
Every skill execution flows through a hardened middleware pipeline. Identical for builtin and custom skills, identical across all five surfaces.
Authentication
API key validated; persona extracted; tenant role bound.
Input validation
Inputs parsed against JSON Schema; type coercion; defaults applied.
Rate-limit check
Per-key token bucket; per-skill RPM and concurrent-execution caps.
Cost cap
Per-customer monthly cap; per-skill per-call cap; aborts before AI call if budget exceeded.
Data load
Reader-role queries against per-customer Postgres role; no cross-tenant access path.
Prompt assembly
Prompt template + inputs + data; prompt-version tagged in audit log.
AI call (cached)
Anthropic with prompt caching enabled; cost attributed to customer + channel.
Output validation
Output parsed against JSON Schema; types enforced; missing required fields rejected.
Content rules
PII filters, injection-pattern checks, configurable block-lists per customer.
Hallucination check
Cross-validates output references against data actually loaded in step 5.
Audit log
Tool, params (PII-redacted), output hash, cost, duration written to durable sink.
Response
Structured output returned to caller in whatever surface invoked the skill.
Marketplace v1 → v2
v1 (Q4 2026, internal): Browse the builtin catalog, install custom skills your org has authored, activate or deactivate per-team. Lives inside your Gitrevio dashboard.
v2 (Q1 2027, community): Public marketplace. Three curation tiers — builtin, community-verified, community-experimental. Domain-specific skills published by the community: fintech risk, gaming, B2B SaaS, regulated industries.
Skills are durable leverage. Every new domain that gets a skill published benefits every customer who installs it — across all five surfaces, simultaneously.
Why this is a moat
Competitors ship dashboards. Gitrevio ships a platform whose unit of intelligence is portable across channels and authorable by customers.
Adding a new channel (Teams bot, IDE plugin) doesn't require rewriting analytics. Plug in the adapter; every existing skill works in the new channel on day one.
Adding a new domain (incident attribution, calendar correlation) is a YAML file. Author once, publish, every customer who needs it installs it.