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SKILL verified MIT Self-run

Aigd Iterate

skill-prodazhang-aigd-aigd-iterate · by ProdaZhang

AIGD Phase 3 · playtest iteration. Use this when, after the user playtests the prototype, they want to optimize the rules or change the config table (can be run repeatedly). Only touches the "cheap" artifacts — rules / config / prototype — not the contract / acceptance (those are produced after finalization). Part of the aigd package; methodology in ../aigd/references/.

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Install

$ agentstack add skill-prodazhang-aigd-aigd-iterate

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

AIGD · iterate (playtest iteration) [minimal skeleton]

> Package contract: install the whole aigd package (the orchestrator aigd/+references/ and the 6 sub-skills (including aigd-ui-capture) placed at the same level in this environment's skills directory, following the host agent, e.g. Claude Code's .claude/skills/), don't copy this skill alone — this package's sub-skills rely on aigd/ at the same level to fetch the methodology, copying it alone breaks the link.

Positioning

Phase 3. Loops between system (2) ↔ iterate (3), repeatable. The design is still fluid, so only change rules / config (test data) / prototype — the proto/acceptance haven't even been produced yet, and zero churn is exactly the essence of how this step saves cost.

Read / produce / write back

  • Read: this system in the manifest, the user's playtest feedback.
  • Produce / change: rules.md / config table (test data) / system prototype.html.
  • Write back: this system's status in the manifest → Playtesting (currently playtesting; a system bounced back from handoff also stays Playtesting, don't overwrite it back to Draft); record this round's iteration points (what changed, why).

Recipe

  1. Collect playtest feedback → locate whether it's a rule problem or a value problem.
  2. Change the corresponding artifact (rules → change -01, numbers → change config test data, presentation → change prototype).
  3. Re-emit the prototype → playtest again → loop, until the user is satisfied → move to aigd-handoff to finalize.

Playtest-feedback format (filled by the user or the AI after playtesting, fed to this skill — the locating decides which artifact to change)

| Dimension | Problem description | Locating (rule/value/presentation/other) | Repro steps | |------|---------|---------------------------|---------| | ` | | | ` |

  • Locating = rule → change -01 rules.md (changing logic / judgment)
  • Locating = value → change config table test data (changing feel parameters)
  • Locating = presentation → change -03 prototype.html (changing feedback / animation / layout)

> A bare "not fun" can't be acted on — first break it down by this table into dimension + locating, then start changing.

Admission / exit

  • Admission: the prototype is clickable, the user already has playtest feedback.
  • Exit: the changed rules/config/prototype + manifest iteration record; user satisfied → move to aigd-handoff to finalize; found the system boundary was drawn wrong → back to aigd-concept to re-split.

Boundary

Doesn't produce contract / acceptance / client-server docs; doesn't touch already-finalized systems (those must be bounced first).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.