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Get Started Free →Plan and run programs using the SAFe x AI-DLC fusion. Use when turning an audit, epic, or initiative into tracker structure (initiative, projects, milestones, issues, sub-issues), organizing work as Units of Work and Bolts, wiring a dependency DAG, or running a Bolt swarm with a human-in-the-loop gate. Applies when work spans multiple issues and needs cadence.
.claude/skills/bybren-llc-safe-ai-dlc/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 57% | 0% |
> 📋 TEMPLATE: This skill uses {{TICKET_PREFIX}} and {{MAIN_BRANCH}} as placeholders. > Replace with your project's ticket prefix (e.g., WOR, PROJ) and base branch (e.g., dev, main).
Encode the SAFe x AI-DLC fusion so agents structure multi-issue programs consistently: SAFe supplies the hierarchy and guardrails, AI-DLC supplies the cadence and the human checkpoint. Inside a program that adopts this fusion, sprints give way to Bolts, because agent teams elaborate, build, and verify in hours rather than weeks. Adopting the fusion is a per-program choice; the standard sprint path stays valid, and single isolated tickets route to safe-workflow instead.
Do NOT use it for a single isolated ticket with no program context — use safe-workflow for that.
SAFe gives hierarchy, WSJF, roles, DoD, and dependencies. AI-DLC gives cadence (Bolts) and the human-in-the-loop. See docs/guides/SAFE-AI-DLC-METHODOLOGY.md for the full rationale.
| SAFe | AI-DLC | Linear | | ----------------- | ------------------------------------- | --------------------------------------- | | Portfolio Epic | The Program | Initiative | | Epic/value stream | Program stream | Sub-initiative, or project priority | | Feature | Unit of Work | Project | | PI increment | Bolt | bolt:N label + target date | | Phase gate | Inception / Construction / Operations | Project Milestone (3 per project) | | Story/Enabler | Story | Issue | | Task | Mob task | Sub-issue | | WSJF/Role/DoD | prioritization / mob role / gate | description + agent:* labels + AC/DoD |
The loop: AI plans → AI asks clarifying questions → HUMAN validates business context → AI executes. Never skip the human validation step.
Since Gemini CLI does not have native Linear integration, build the program through the Linear web UI (or the Linear CLI if installed). Build top-down:
Linear web UI → Initiatives → New initiative
Initiative → Add project
per project. Project → Milestones → New milestone
bolt:N and agent:*(lead role). Labels must already exist in the workspace.
Issue → Add sub-issue
blocks / blockedBy edges forming the dependency DAG.Issue → Add relation → Blocks / Blocked by
Program streams map to sub-initiatives where the Linear plan supports them; where it does not, encode the stream as project priority instead (highest-risk stream = Urgent/High).
Use linear-sop for ticket operations and evidence templates.
Every issue description block contains:
textHeader: <ID> — <title> [{{TICKET_PREFIX}}-XXX] WSJF: <score> MoSCoW: <Must|Should|Could|Won't> Finding: <source refs, e.g. audit finding IDs> Phase: <Inception | Construction | Operations> Bolt: <bolt:N> Role: <agent:* lead> AC: - acceptance criterion (testable) DoD: - merged to {{MAIN_BRANCH}}; relevant gate REAL and green; evidence in Linear; no silent suppression Deps: blocks: [...] blockedBy: [...] Sub-issues: (Mob Elaboration tasks, added during Inception)
Wire blocks / blockedBy so the enforcement lands before the thing it enforces:
that check is supposed to catch.
top of it.
the scheduled recurring audit.
Dependency chains are usually rooted at CI hardening — one keystone issue tends to unblock a whole stream. Identify it and sequence its Bolt first.
{{TICKET_PREFIX}}- branches with SAFe commits, rebase-first.linear-sop.evidence posted.
Route to the human, with options and a recommendation, whenever a decision is:
If a decision changes business, security, or risk posture, it is the human's — surface, recommend, stop.
textForcing an ambiguous epic into one Bolt (run a spike instead — elaboration is the hard part) Creating sub-issues before Inception (Mob Elaboration produces them, not the other way round) Bolts as calendar sprints (a Bolt exits on evidence, not on a date) Skipping human validation to move faster (the loop is the method; without it this is just SAFe) Treating a green check as an exit (verify the gate enforces, not that it merely passed)
docs/guides/SAFE-AI-DLC-METHODOLOGY.md -- Methodology guide (rationale, worked example)specs_templates/program_template.md -- Program document scaffoldinglinear-sop — Linear operations, program structure, evidence templatessafe-workflow — branch, commit, and PR conventions; HITL mergeorchestration-patterns — multi-agent swarm coordination for a Boltpattern-discovery — find existing code and doc patterns before constructing| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 25,861 | 23,542 | -9% | 1 | 1 | 0% | 4,031 | 6,033 | +50% | 0 | 0 | — |
case-02 | fail→pass | 37,403 | 22,315 | -40% | 1 | 1 | 0% | 6,216 | 5,539 | -11% | 0 | 0 | — |
case-03 | fail→pass | 35,964 | 22,318 | -38% | 1 | 1 | 0% | 5,450 | 5,412 | -1% | 0 | 0 | — |
case-04 | pass→pass | 12,207 | 4,095 | -66% | 1 | 1 | 0% | 1,828 | 2,483 | +36% | 0 | 0 | — |
case-05 | fail→pass | 12,851 | 10,644 | -17% | 1 | 1 | 0% | 2,172 | 3,374 | +55% | 0 | 0 | — |
case-06 | pass→pass | 14,969 | 8,731 | -42% | 1 | 1 | 0% | 2,368 | 3,196 | +35% | 0 | 0 | — |
case-15 | pass→pass | 7,308 | 2,864 | -61% | 1 | 1 | 0% | 1,027 | 2,281 | +122% | 0 | 0 | — |
case-07 | pass→pass | 11,646 | 5,579 | -52% | 1 | 1 | 0% | 1,629 | 2,521 | +55% | 0 | 0 | — |
case-08 | fail→pass | 11,016 | 4,479 | -59% | 1 | 1 | 0% | 1,565 | 2,453 | +57% | 0 | 0 | — |
case-09 | fail→pass | 10,508 | 7,058 | -33% | 1 | 1 | 0% | 1,432 | 2,828 | +97% | 0 | 0 | — |
case-10 | pass→pass | 15,483 | 10,632 | -31% | 1 | 1 | 0% | 2,124 | 3,367 | +59% | 0 | 0 | — |
case-11 | fail→pass | 15,024 | 5,231 | -65% | 1 | 1 | 0% | 1,968 | 2,579 | +31% | 0 | 0 | — |
case-12 | fail→pass | 11,682 | 5,534 | -53% | 1 | 1 | 0% | 1,570 | 2,619 | +67% | 0 | 0 | — |
case-13 | fail→pass | 13,713 | 5,009 | -63% | 1 | 1 | 0% | 1,810 | 2,451 | +35% | 0 | 0 | — |
case-14 | pass→pass | 12,333 | 5,563 | -55% | 1 | 1 | 0% | 1,753 | 2,613 | +49% | 0 | 0 | — |
case-16 | pass→pass | 12,537 | 7,078 | -44% | 1 | 1 | 0% | 1,675 | 2,888 | +72% | 0 | 0 | — |
case-17 | pass→pass | 8,072 | 4,818 | -40% | 1 | 1 | 0% | 1,233 | 2,554 | +107% | 0 | 0 | — |
case-18 | fail→pass | 20,238 | 5,150 | -75% | 1 | 1 | 0% | 2,927 | 2,668 | -9% | 0 | 0 | — |
case-19 | pass→pass | 14,508 | 8,895 | -39% | 1 | 1 | 0% | 2,014 | 3,086 | +53% | 0 | 0 | — |
case-20 | fail→pass | 14,355 | 3,551 | -75% | 1 | 1 | 0% | 2,049 | 2,346 | +14% | 0 | 0 | — |
case-21 | fail→pass | 15,603 | 5,416 | -65% | 1 | 1 | 0% | 2,335 | 2,669 | +14% | 0 | 0 | — |
case-22 | pass→pass | 17,935 | 8,451 | -53% | 1 | 1 | 0% | 2,832 | 3,191 | +13% | 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. The headline lift of +55 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.