Skip to content

Commit 9638f67

Browse files
ihimanssaws-hsungmin
authored andcommitted
Add Kiro skills (18 SKILL.md files) (affaan-m#811)
Co-authored-by: Sungmin Hong <hsungmin@amazon.com>
1 parent 8100e93 commit 9638f67

File tree

18 files changed

+6785
-0
lines changed

18 files changed

+6785
-0
lines changed
Lines changed: 135 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,135 @@
1+
---
2+
name: agentic-engineering
3+
description: >
4+
Operate as an agentic engineer using eval-first execution, decomposition,
5+
and cost-aware model routing. Use when AI agents perform most implementation
6+
work and humans enforce quality and risk controls.
7+
metadata:
8+
origin: ECC
9+
---
10+
11+
# Agentic Engineering
12+
13+
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
14+
15+
## Operating Principles
16+
17+
1. Define completion criteria before execution.
18+
2. Decompose work into agent-sized units.
19+
3. Route model tiers by task complexity.
20+
4. Measure with evals and regression checks.
21+
22+
## Eval-First Loop
23+
24+
1. Define capability eval and regression eval.
25+
2. Run baseline and capture failure signatures.
26+
3. Execute implementation.
27+
4. Re-run evals and compare deltas.
28+
29+
**Example workflow:**
30+
```
31+
1. Write test that captures desired behavior (eval)
32+
2. Run test → capture baseline failures
33+
3. Implement feature
34+
4. Re-run test → verify improvements
35+
5. Check for regressions in other tests
36+
```
37+
38+
## Task Decomposition
39+
40+
Apply the 15-minute unit rule:
41+
- Each unit should be independently verifiable
42+
- Each unit should have a single dominant risk
43+
- Each unit should expose a clear done condition
44+
45+
**Good decomposition:**
46+
```
47+
Task: Add user authentication
48+
├─ Unit 1: Add password hashing (15 min, security risk)
49+
├─ Unit 2: Create login endpoint (15 min, API contract risk)
50+
├─ Unit 3: Add session management (15 min, state risk)
51+
└─ Unit 4: Protect routes with middleware (15 min, auth logic risk)
52+
```
53+
54+
**Bad decomposition:**
55+
```
56+
Task: Add user authentication (2 hours, multiple risks)
57+
```
58+
59+
## Model Routing
60+
61+
Choose model tier based on task complexity:
62+
63+
- **Haiku**: Classification, boilerplate transforms, narrow edits
64+
- Example: Rename variable, add type annotation, format code
65+
66+
- **Sonnet**: Implementation and refactors
67+
- Example: Implement feature, refactor module, write tests
68+
69+
- **Opus**: Architecture, root-cause analysis, multi-file invariants
70+
- Example: Design system, debug complex issue, review architecture
71+
72+
**Cost discipline:** Escalate model tier only when lower tier fails with a clear reasoning gap.
73+
74+
## Session Strategy
75+
76+
- **Continue session** for closely-coupled units
77+
- Example: Implementing related functions in same module
78+
79+
- **Start fresh session** after major phase transitions
80+
- Example: Moving from implementation to testing
81+
82+
- **Compact after milestone completion**, not during active debugging
83+
- Example: After feature complete, before starting next feature
84+
85+
## Review Focus for AI-Generated Code
86+
87+
Prioritize:
88+
- Invariants and edge cases
89+
- Error boundaries
90+
- Security and auth assumptions
91+
- Hidden coupling and rollout risk
92+
93+
Do not waste review cycles on style-only disagreements when automated format/lint already enforce style.
94+
95+
**Review checklist:**
96+
- [ ] Edge cases handled (null, empty, boundary values)
97+
- [ ] Error handling comprehensive
98+
- [ ] Security assumptions validated
99+
- [ ] No hidden coupling between modules
100+
- [ ] Rollout risk assessed (breaking changes, migrations)
101+
102+
## Cost Discipline
103+
104+
Track per task:
105+
- Model tier used
106+
- Token estimate
107+
- Retries needed
108+
- Wall-clock time
109+
- Success/failure outcome
110+
111+
**Example tracking:**
112+
```
113+
Task: Implement user login
114+
Model: Sonnet
115+
Tokens: ~5k input, ~2k output
116+
Retries: 1 (initial implementation had auth bug)
117+
Time: 8 minutes
118+
Outcome: Success
119+
```
120+
121+
## When to Use This Skill
122+
123+
- Managing AI-driven development workflows
124+
- Planning agent task decomposition
125+
- Optimizing model tier selection
126+
- Implementing eval-first development
127+
- Reviewing AI-generated code
128+
- Tracking development costs
129+
130+
## Integration with Other Skills
131+
132+
- **tdd-workflow**: Combine with eval-first loop for test-driven development
133+
- **verification-loop**: Use for continuous validation during implementation
134+
- **search-first**: Apply before implementation to find existing solutions
135+
- **coding-standards**: Reference during code review phase

0 commit comments

Comments
 (0)