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Get Started Free →What limits us — identify bottlenecks, quantify constraints, analyze dependencies, resolve conflicts before experiment execution
.claude/skills/yogsoth-ai-constraint-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 106% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 2% | 0% |
Before entering this campaign, the following must be true:
| Gate | Condition | |------|-----------| | Research direction exists | North star or research question is crystallized | | Scope is bounded | Problem space has defined boundaries | | Resources are enumerable | Can list available compute, data, time, people, budget | | Stakeholders identified | Know who cares about the outcome |
If any gate fails, return to Campaign 1 (research-direction) or pre-campaign intake.
Produce a comprehensive constraint profile that identifies:
| Situation | Strategy | When to Use | |-----------|----------|-------------| | Unknown bottleneck | bottleneck-identification | System performance is limited but cause unclear | | Resource uncertainty | resource-constraint | Need to verify feasibility of resource plan | | Assumption risk | assumption-constraint | Key assumptions untested or fragile | | Sequencing unclear | dependency-constraint | Task ordering and prerequisites unknown | | Conflicting demands | conflict-resolution | Two or more constraints oppose each other |
Default execution order: bottleneck-identification → resource-constraint → assumption-constraint → dependency-constraint → conflict-resolution
| Resource | Budget | Escalation | |----------|--------|------------| | Subagent calls | ≤15 per strategy | Pause and report partial | | Wall-clock time | ≤30 min per strategy | Checkpoint and continue | | Context tokens | ≤80k per strategy | Summarize and spawn fresh | | Total campaign | ≤5 strategies | Skip if constraint already resolved |
Campaign is complete when:
研究过程经 context-management 落盘,与最终报告分属不同文件:
constraint-analysis,建立本 campaign 的过程 context 文件。init 幂等——同 Phase 重入返回原文件。
strategy 的过程与中间产出 append 进上一步的过程文件。
另起 constraint-analysis-report 文件落盘(见该 SOP)。
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | assumption-constraint | Which assumptions are most fragile? — Vulnerability ranking + impact assessment of experiment assumptions | | conflict-resolution | How do constraints conflict with each other? — Evaporating Cloud + assumption challenging + injection to resolve constraint conflicts | | dependency-constraint | What must be completed first? — Dependency chain analysis + prerequisite graph construction | | experiment-execution-bottleneck-identification | Where is the system bottleneck? — TOC 5 Focusing Steps + Current Reality Tree to find the binding constraint | | resource-constraint | Are resources sufficient? — Quantify compute, data, time, human, and financial resource constraints |
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | constraint-breaking | Orchestrate the full constraint-breaking cycle: extract conflict, challenge assumptions, project resolution | | constraint-tree-building | Build Current Reality Tree from UDEs through causal chains to core conflicts | | sensitivity-ranking | Rank constraints by sensitivity — which ones most impact the outcome if they shift |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | constraint-synthesis | Synthesize constraint analysis into actionable report with priorities | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. | | experiment-execution-paper-overview | Import SOP: paper landscape scan (from literature-engine skill) | | experiment-execution-paper-research | Import SOP: paper full-text reading (from literature-engine skill) | | experiment-execution-paper-search | Import SOP: paper AI summary reading (from literature-engine skill) | | experiment-execution-quality-gate-check | Shared SOP: verify quality gate criteria are met before proceeding | | experiment-execution-saturation-detection | Shared SOP: detect information saturation — know when to stop searching/analyzing | | experiment-execution-web-research | Import SOP: deep full-page content analysis (from web-browsing skill) | | experiment-execution-web-search | Import SOP: quick web scan discovery (from web-browsing skill) |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 36,379 | 30,826 | -15% | 1 | 1 | 0% | 6,204 | 6,383 | +3% | 0 | 0 | — |
case-02 | fail→fail | 23,769 | 7,945 | -67% | 1 | 1 | 0% | 4,340 | 1,771 | -59% | 0 | 0 | — |
case-03 | fail→fail | 27,006 | 6,007 | -78% | 1 | 1 | 0% | 3,850 | 1,658 | -57% | 0 | 0 | — |
case-04 | fail→pass | 15,768 | 8,396 | -47% | 1 | 1 | 0% | 2,277 | 2,462 | +8% | 0 | 0 | — |
case-05 | fail→pass | 19,113 | 9,481 | -50% | 1 | 1 | 0% | 2,928 | 2,790 | -5% | 0 | 0 | — |
case-06 | fail→pass | 28,509 | 11,504 | -60% | 1 | 1 | 0% | 1,254 | 2,583 | +106% | 0 | 0 | — |
case-07 | pass→pass | 10,760 | 4,394 | -59% | 1 | 1 | 0% | 1,561 | 2,010 | +29% | 0 | 0 | — |
case-08 | pass→pass | 13,519 | 7,003 | -48% | 1 | 1 | 0% | 1,943 | 2,382 | +23% | 0 | 0 | — |
case-09 | pass→pass | 14,726 | 6,647 | -55% | 1 | 1 | 0% | 2,271 | 2,338 | +3% | 0 | 0 | — |
case-10 | fail→pass | 15,834 | 7,213 | -54% | 1 | 1 | 0% | 2,375 | 2,423 | +2% | 0 | 0 | — |
case-11 | fail→fail | 7,635 | 4,601 | -40% | 1 | 1 | 0% | 1,214 | 1,970 | +62% | 0 | 0 | — |
case-12 | pass→pass | 12,924 | 5,485 | -58% | 1 | 1 | 0% | 1,886 | 2,258 | +20% | 0 | 0 | — |
case-13 | pass→pass | 10,929 | 2,569 | -76% | 1 | 1 | 0% | 1,586 | 1,662 | +5% | 0 | 0 | — |
case-14 | pass→pass | 11,323 | 5,808 | -49% | 1 | 1 | 0% | 1,706 | 2,225 | +30% | 0 | 0 | — |
case-15 | fail→pass | 8,218 | 4,466 | -46% | 1 | 1 | 0% | 1,241 | 2,033 | +64% | 0 | 0 | — |
case-16 | fail→pass | 15,024 | 10,352 | -31% | 1 | 1 | 0% | 2,208 | 2,886 | +31% | 0 | 0 | — |
case-17 | fail→pass | 14,051 | 9,869 | -30% | 1 | 1 | 0% | 2,144 | 2,835 | +32% | 0 | 0 | — |
case-18 | pass→pass | 13,021 | 3,912 | -70% | 1 | 1 | 0% | 2,310 | 1,913 | -17% | 0 | 0 | — |
case-19 | fail→pass | 11,608 | 4,729 | -59% | 1 | 1 | 0% | 1,727 | 2,021 | +17% | 0 | 0 | — |
case-20 | pass→pass | 27,272 | 36,203 | +33% | 1 | 1 | 0% | 4,436 | 7,463 | +68% | 0 | 0 | — |
case-21 | pass→fail | 30,446 | 40,785 | +34% | 1 | 1 | 0% | 6,167 | 7,456 | +21% | 0 | 0 | — |
case-22 | pass→pass | 21,535 | 30,410 | +41% | 1 | 1 | 0% | 3,518 | 6,631 | +88% | 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 19 counted toward the lift figure. The other 3 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 +36 percentage points is the difference between those two pass rates over the 19 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.