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/loopdive/js2/sprint-planningnpx skills add loopdive/js2 --skill sprint-planninggit clone --depth 1 https://github.com/loopdive/js2What 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 | $0.00030 | $0.00736 |
| Opus 5 | $0.00015 | $0.00368 |
| Sonnet 5 | $0.00006 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
sprint-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sprint Planning
Facilitates sprint planning as a collaborative process. Can be run by the tech lead, PO, or any agent.
Participants
Planning should involve multiple perspectives. If dedicated agents are spawned, message them. If not, invoke the relevant skills inline:
- PO perspective: prioritize by value, write acceptance criteria
- Architect perspective: feasibility assessment, specs for hard issues (use
/architect-spec) - SM perspective: process constraints from last retro, capacity limits
- Dev perspective: estimate effort, flag risks
Step 1: Read current state
# Check baseline
cat plan/issues/{N-1}/sprint.md | grep "Final numbers"
# Check what's ready
rg -l '^status: ready$' plan/issues/*.md
# Check dependency graph
cat plan/log/dependency-graph.md
# Check last retro for process constraints
cat plan/issues/{N-1}/sprint.md | grep -A 3 "Action items"
Step 2: Validate candidate issues
For each high-priority issue, smoke-test against current main (use /smoke-test-issue):
- Still fails: candidate for sprint
- Already passes: close it, mark it done
- Partially fixed: update issue with current status
Step 3: Prioritize by value
Order candidates by impact × unblocking potential, not just CE/FAIL count:
- Which fixes unblock the most downstream work?
- Which affect the most test categories?
- Which are quick wins vs deep investigations?
Step 4: Assess feasibility
For each candidate:
- Easy (< 50 lines, clear fix): dispatch directly to dev
- Medium (< 150 lines, known approach): dispatch with guidance in task description
- Hard (> 150 lines, unclear approach, core codegen): invoke
/architect-specfirst, dispatch only after spec is written
Step 5: Check capacity
- Max 3 dev agents (16GB RAM constraint)
- 1 task per dev at a time (wait for merge before next)
- Account for merge/test cycle time (~10-15 min per task)
- Typical sprint: 6-10 tasks across 2-3 devs
Step 6: Create task queue
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 · 91 lines · 30 tokens per session scan A d60dce6a577e
sprint-planning is a skill published in the GitHub repository loopdive/js2 (59 stars, last pushed 2d ago), licensed Apache-2.0. It adds 30 tokens to every session and 736 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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