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Get Started Free →Apply action research through Plan-Act-Observe-Reflect cycles and Participatory Action Research (PAR) to generate knowledge while improving practice. Use this skill when the user needs to design practitioner research that integrates inquiry with intervention, facilitate participatory research with stakeholders, structure iterative improvement cycles, or when they ask 'how do I research my own practice', 'how do I involve participants as co-researchers', or 'how do I combine research with practic
.claude/skills/asgard-ai-platform-grad-action-research/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -37% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 64% | 0% |
Action research is a cyclical methodology that integrates research with practice improvement. Originating with Kurt Lewin, it follows iterative Plan-Act-Observe-Reflect cycles where practitioners investigate their own context, implement changes, and generate knowledge simultaneously. Participatory Action Research (PAR) extends this by positioning community members as co-researchers, emphasizing democratic knowledge production and social transformation.
IRON LAW: Action research requires DUAL commitment — to generating
knowledge AND to improving practice. Research without action is
conventional research; action without reflection is just practice.
Both halves are non-negotiable.Key assumptions:
Identify the problem or area for improvement collaboratively. Conduct reconnaissance (fact-finding about the current situation). Develop an action plan based on the diagnosis. In PAR, ensure stakeholders co-design the plan.
Implement the planned intervention or change. Document what was actually done (which may differ from the plan). Keep the intervention scope manageable for one cycle.
Collect data systematically during and after the action. Use multiple methods: fieldnotes, interviews, surveys, documents, student work, video. Focus on both intended and unintended consequences of the action.
Analyze the data collaboratively. Evaluate what happened and why. Identify what worked, what did not, and what was surprising. Feed insights into the next cycle's planning. Document learning for knowledge generation.
Repeat the cycle — each iteration refines both the action and the understanding.
markdown## Action Research Report: [Context] ### Problem and Context - Problem: [what needs to change] - Setting: [where the practice occurs] - Stakeholders: [who is involved as co-researchers] - PAR elements: [how participation was structured, if applicable] ### Cycle Log | Cycle | Plan | Action Taken | Key Observations | Reflections | |-------|------|-------------|-----------------|-------------| | 1 | [intended action] | [actual action] | [data summary] | [lessons learned] | | 2 | [revised plan] | [actual action] | [data summary] | [lessons learned] | | 3 | [revised plan] | [actual action] | [data summary] | [lessons learned] | ### Knowledge Generated - Practical knowledge: [what works in this context and why] - Theoretical contribution: [how findings extend understanding beyond this context] ### Practice Changes Implemented 1. [Change that was adopted and sustained] 2. [Change that was adopted and sustained] ### Validity Criteria - Outcome validity: [did the action resolve the problem?] - Process validity: [was the methodology sound?] - Democratic validity: [were stakeholders meaningfully involved?] - Catalytic validity: [did participants develop deeper understanding?] - Dialogic validity: [was the research subjected to peer scrutiny?]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 27,984 | 23,282 | -17% | 1 | 1 | 0% | 4,914 | 4,357 | -11% | 0 | 0 | — |
case-02 | fail→pass | 92,630 | 18,773 | -80% | 1 | 1 | 0% | 5,699 | 4,068 | -29% | 0 | 0 | — |
case-15 | pass→pass | 17,630 | 9,184 | -48% | 1 | 1 | 0% | 2,213 | 2,556 | +15% | 0 | 0 | — |
case-08 | fail→fail | 9,968 | 10,046 | +1% | 1 | 1 | 0% | 1,346 | 2,598 | +93% | 0 | 0 | — |
case-03 | fail→pass | 49,952 | 25,351 | -49% | 1 | 1 | 0% | 7,061 | 4,417 | -37% | 0 | 0 | — |
case-04 | fail→pass | 22,976 | 17,075 | -26% | 1 | 1 | 0% | 2,919 | 3,244 | +11% | 0 | 0 | — |
case-05 | pass→pass | 18,020 | 12,632 | -30% | 1 | 1 | 0% | 2,175 | 3,179 | +46% | 0 | 0 | — |
case-06 | pass→pass | 13,375 | 12,103 | -10% | 1 | 1 | 0% | 2,112 | 2,794 | +32% | 0 | 0 | — |
case-07 | fail→pass | 10,372 | 12,069 | +16% | 1 | 1 | 0% | 1,702 | 2,795 | +64% | 0 | 0 | — |
case-09 | fail→pass | 16,600 | 22,794 | +37% | 1 | 1 | 0% | 2,568 | 3,810 | +48% | 0 | 0 | — |
case-10 | fail→fail | 6,697 | 12,436 | +86% | 1 | 1 | 0% | 1,030 | 2,521 | +145% | 0 | 0 | — |
case-11 | fail→pass | 20,940 | 14,757 | -30% | 1 | 1 | 0% | 2,584 | 3,055 | +18% | 0 | 0 | — |
case-12 | fail→pass | 17,991 | 12,428 | -31% | 1 | 1 | 0% | 2,318 | 2,626 | +13% | 0 | 0 | — |
case-13 | fail→pass | 16,232 | 14,481 | -11% | 1 | 1 | 0% | 2,394 | 2,961 | +24% | 0 | 0 | — |
case-14 | pass→pass | 13,226 | 31,323 | +137% | 1 | 1 | 0% | 2,115 | 3,506 | +66% | 0 | 0 | — |
case-16 | pass→pass | 8,031 | 4,360 | -46% | 1 | 1 | 0% | 1,362 | 1,736 | +27% | 0 | 0 | — |
case-17 | pass→pass | 13,478 | 8,592 | -36% | 1 | 1 | 0% | 2,097 | 2,417 | +15% | 0 | 0 | — |
case-18 | pass→pass | 5,515 | 4,574 | -17% | 1 | 1 | 0% | 829 | 1,653 | +99% | 0 | 0 | — |
case-19 | pass→pass | 13,216 | 12,402 | -6% | 1 | 1 | 0% | 2,018 | 2,674 | +33% | 0 | 0 | — |
case-20 | pass→pass | 24,725 | 20,113 | -19% | 1 | 1 | 0% | 3,995 | 3,667 | -8% | 0 | 0 | — |
case-21 | pass→pass | 35,907 | 24,682 | -31% | 1 | 1 | 0% | 5,892 | 4,661 | -21% | 0 | 0 | — |
case-22 | pass→pass | 25,933 | 19,651 | -24% | 1 | 1 | 0% | 3,678 | 4,490 | +22% | 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 +41 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.