Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/youglin-dev/aha-loop/plan-reviewnpx skills add YougLin-dev/Aha-Loop --skill plan-reviewgit clone --depth 1 https://github.com/YougLin-dev/Aha-LoopWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00040 | $0.01798 |
| Opus 5 | $0.00020 | $0.00899 |
| Sonnet 5 | $0.00008 | $0.00360 |
| Haiku 4.5 | $0.00004 | $0.00180 |
Grade A, and why
plan-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Review Skill
Evaluate research findings and decide whether to adjust upcoming stories in the PRD.
The Job
- Read the research report for the current story
- Evaluate if findings impact the current or future stories
- Decide on plan modifications (if any)
- Update
prd.jsonwith changes - Document all changes in
changeLog - Ensure plan remains coherent and achievable
When to Modify the Plan
MODIFY stories when research reveals:
- A better technical approach than originally planned
- Missing prerequisite steps
- Stories that should be split (too large for one context)
- Stories that can be combined (too small, tightly coupled)
- Changed dependencies requiring reordering
- New edge cases requiring additional acceptance criteria
DO NOT modify when:
- The finding is interesting but doesn't affect implementation
- Changes would invalidate already-completed stories
- The modification is scope creep (outside original PRD goals)
Types of Plan Modifications
1. Modify Existing Story
Update acceptance criteria, description, or research topics.
{
"timestamp": "2026-01-29T12:00:00Z",
"storyId": "US-002",
"action": "modified",
"reason": "Research found that existing badge component supports priority colors, simplifying implementation",
"changes": {
"acceptanceCriteria": {
"removed": ["Create new PriorityBadge component"],
"added": ["Reuse Badge component with priority color variant"]
}
}
}
2. Add New Story
Insert a prerequisite or follow-up story.
{
"timestamp": "2026-01-29T12:00:00Z",
"storyId": "US-001.5",
"action": "added",
"reason": "Research revealed need for database index on priority column for filter performance",
"insertAfter": "US-001"
}
3. Split Story
Break a story into smaller pieces.
{
"timestamp": "2026-01-29T12:00:00Z",
"storyId": "US-003",
"action": "split",
"reason": "Story too large - separating dropdown component from save logic",
"splitInto": ["US-003a", "US-003b"]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 299 lines · 40 tokens per session scan A ba332c33449e
plan-review is a skill published in the GitHub repository YougLin-dev/Aha-Loop (181 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 1,798 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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