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Get Started Free →Classifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.
.claude/skills/athola-risk-classification/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 99% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 45% | 0% |
Provides inline risk classification for agent tasks using a 4-tier model (GREEN/YELLOW/RED/CRITICAL). Uses fast heuristic file-pattern matching for low-risk tiers and delegates to Skill(attune:war-room-checkpoint) for high-risk tiers requiring full reversibility scoring.
Skill(attune:war-room) instead)| Tier | Color | Scope | Example | Verification | |------|-------|-------|---------|-------------| | GREEN | Safe | Single file, trivial revert | Test files, docs, utils | None required | | YELLOW | Caution | Module-level, user-visible | Components, routes, views | Conflict check and test pass | | RED | Danger | Cross-module, security/data | Migrations, auth, database schema | War-room RS, full test, and review | | CRITICAL | Stop | Irreversible, regulated | Data deletion, production deploy | War-room RS and human approval |
Task received
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v
Heuristic classifier (file patterns)
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├── GREEN/YELLOW → Apply tier, continue
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└── RED/CRITICAL → Invoke Skill(attune:war-room-checkpoint)
for reversibility scoring (RS)
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└── RS confirms or adjusts tierWhy hybrid: GREEN/YELLOW classification is fast and deterministic (file pattern matching). RED/CRITICAL tasks warrant the overhead of full reversibility analysis because the cost of getting them wrong is high.
Add risk tier to task metadata for downstream consumption:
json{ "id": "5", "subject": "Add user authentication", "metadata": { "risk_tier": "YELLOW", "risk_reason": "Modifies src/components/LoginForm.tsx (user-visible component)", "classified_at": "2026-02-07T22:00:00Z" } }
Tasks without risk_tier metadata default to GREEN (backward compatible).
The 4-tier Readiness Levels system provides clear risk classification with required controls per tier:
| Level | Name | When | Required Controls | |-------|------|------|-------------------| | 0 | Routine | Low blast radius, easy rollback | Basic validation, rollback step | | 1 | Watch | User-visible changes | Review, negative test, rollback note | | 2 | Elevated | Security/compliance/data | Adversarial review, risk checklist | | 3 | Critical | Irreversible, regulated | Human confirmation, two-step verification |
See modules/readiness-levels.md for full level definitions, selection decision tree, and integration guidance.
Risk classification sets how carefully a change is verified. Automation tiers set how autonomously the agent acts and when it must hand control back. Each risk tier carries a default automation tier (GREEN to A3 autonomous, CRITICAL to A0 manual), and a pre-licensed downgrade trigger drops the agent one tier on repeated failure, confidence loss, a stakes spike, or repo-state mismatch rather than re-prompting at the same level. See modules/automation-tiers.md for the tier table and the downgrade trigger, and imbue:assisted-mastery for the explain/produce mode selection that reads from it.
yaml# In your skill's frontmatter dependencies: [leyline:risk-classification]
Append [R:TIER] marker to task format:
markdown- [ ] T012 [P] [US1] [R:YELLOW] Create LoginForm component in src/components/LoginForm.tsx
Check risk tier before task assignment:
if task.risk_tier in ["RED", "CRITICAL"]:
invoke Skill(attune:war-room-checkpoint) for RS scoring
if CRITICAL: require human approval before proceedingwith a reason when a trigger fires
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 11,562 | 10,826 | -6% | 1 | 1 | 0% | 2,064 | 3,356 | +63% | 0 | 0 | — |
case-01 | fail→pass | 6,904 | 6,144 | -11% | 1 | 1 | 0% | 1,203 | 2,388 | +99% | 0 | 0 | — |
case-03 | fail→pass | 12,493 | 9,411 | -25% | 1 | 1 | 0% | 1,973 | 2,913 | +48% | 0 | 0 | — |
case-04 | pass→pass | 19,482 | 23,753 | +22% | 1 | 1 | 0% | 3,280 | 5,373 | +64% | 0 | 0 | — |
case-05 | pass→pass | 8,637 | 3,320 | -62% | 1 | 1 | 0% | 1,245 | 1,838 | +48% | 0 | 0 | — |
case-06 | fail→fail | 1,876 | 6,441 | +243% | 1 | 1 | 0% | 215 | 1,398 | +550% | 0 | 0 | — |
case-07 | fail→pass | 9,504 | 6,640 | -30% | 1 | 1 | 0% | 1,416 | 2,386 | +69% | 0 | 0 | — |
case-08 | fail→pass | 8,700 | 5,490 | -37% | 1 | 1 | 0% | 1,441 | 2,094 | +45% | 0 | 0 | — |
case-09 | fail→pass | 11,337 | 9,493 | -16% | 1 | 1 | 0% | 1,882 | 2,834 | +51% | 0 | 0 | — |
case-10 | pass→pass | 13,251 | 8,093 | -39% | 1 | 1 | 0% | 2,106 | 2,629 | +25% | 0 | 0 | — |
case-11 | fail→pass | 11,085 | 4,140 | -63% | 1 | 1 | 0% | 2,148 | 2,001 | -7% | 0 | 0 | — |
case-12 | fail→pass | 5,667 | 3,095 | -45% | 1 | 1 | 0% | 822 | 1,712 | +108% | 0 | 0 | — |
case-13 | fail→pass | 7,897 | 2,439 | -69% | 1 | 1 | 0% | 1,292 | 1,504 | +16% | 0 | 0 | — |
case-14 | fail→pass | 13,213 | 4,944 | -63% | 1 | 1 | 0% | 1,929 | 2,044 | +6% | 0 | 0 | — |
case-15 | fail→pass | 14,273 | 7,434 | -48% | 1 | 1 | 0% | 2,137 | 2,486 | +16% | 0 | 0 | — |
case-16 | fail→pass | 12,811 | 3,004 | -77% | 1 | 1 | 0% | 1,997 | 1,750 | -12% | 0 | 0 | — |
case-17 | pass→pass | 6,100 | 1,483 | -76% | 1 | 1 | 0% | 840 | 1,461 | +74% | 0 | 0 | — |
case-18 | fail→pass | 5,345 | 4,154 | -22% | 1 | 1 | 0% | 704 | 1,923 | +173% | 0 | 0 | — |
case-19 | fail→pass | 15,269 | 8,172 | -46% | 1 | 1 | 0% | 2,417 | 2,641 | +9% | 0 | 0 | — |
case-20 | fail→pass | 16,784 | 10,818 | -36% | 1 | 1 | 0% | 2,647 | 3,063 | +16% | 0 | 0 | — |
case-21 | fail→pass | 6,706 | 5,173 | -23% | 1 | 1 | 0% | 1,192 | 2,159 | +81% | 0 | 0 | — |
case-22 | fail→pass | 13,216 | 9,052 | -32% | 1 | 1 | 0% | 2,008 | 2,617 | +30% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +77 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.