Borrowing it
Nothing to install: this file belongs to zkysar1/Claude-Mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zkysar1/Claude-Mind/main/.claude/skills/sprint-planning/SKILL.mdgit clone --depth 1 https://github.com/zkysar1/Claude-MindWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zkysar1/claude-mind/sprint-planning)<a href="https://agentmods.dev/skills/zkysar1/claude-mind/sprint-planning"><img src="https://agentmods.dev/badge/skills/zkysar1/claude-mind/sprint-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zkysar1/claude-mind/sprint-planning"><img src="https://agentmods.dev/badge/skills/zkysar1/claude-mind/sprint-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00138 | $0.03270 |
| Opus 5 | $0.00069 | $0.01635 |
| Sonnet 5 | $0.00028 | $0.00654 |
| Haiku 4.5 | $0.00014 | $0.00327 |
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 10d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sprint-planning — Fleet Sprint Planning Exercise
Turns the two report skills into a full planning pass: measure → analyze → verify → apply → publish. A plan that changes no queue state is a report, not a plan (asp-353 directive lineage) — but every queue change must survive a live-state verification first. Formalized 2026-08-10 from a user-directed sprint session (74-agent ultracode pass, 276 proposed changes, 28 refuted by adversarial verification — the refutation rate is why Phase 4's verify step is not optional).
Hybrid skill: user-invocable AND agent-callable (the recurring sprint-planning goal invokes it in standard mode). Requires assistant or autonomous mode — it writes queue state.
Sub-commands
/sprint-planning — Standard pass: inline analysis, bounded lanes
/sprint-planning --ultra — USER-INVOKED ONLY: authorizes a multi-agent
Workflow fan-out (per-aspiration analysts +
adversarial verifiers). An agent-initiated
(recurring-goal) run MUST NOT pass --ultra:
Workflow orchestration requires explicit user
opt-in, and a recurring firing is not one.
Phase 0: Load Conventions
Step 0: Load Conventions — Bash: load-conventions.sh with each name from the conventions: front matter. Read only the paths returned (files not yet in context). If output is empty, all conventions already loaded — proceed to next step.
Phase 1: Foundation Reports
1. Invoke Skill(backlog-report) — produces agents/<agent>/BACKLOG.md and the
structural indexes this skill reuses (score map, blocked map, user goals,
recurring health, testable hypotheses).
2. Priority dashboard data (inline — do NOT invoke /priority-review here; its
Phases 3-4 are interactive and this skill's reorder decisions come from
Phase 3 analysis instead):
Bash: load-aspirations-compact.sh → Read returned path
Bash: goal-selector.sh select → scored_goals (NOTE: output is a bare JSON
array, not a dict) → aggregate score per aspiration
Bash: echo '<[{asp_id,priority,score}...]>' | priority-review-mismatch.sh
→ flagged score-priority mismatches (needs 3+ consecutive runs to flag)
3. Snapshot for the ledger: record counts (active aspirations, non-terminal
goals, selectable goals, blocked, user-routed, overdue recurring) BEFORE
any write — these are the plan's before/after evidence.
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.
- 10d ago First seen · 263 lines · 138 tokens per session scan A 951df30ce871
sprint-planning is a skill published in the GitHub repository zkysar1/Claude-Mind (5 stars, last pushed 2d ago), licensed MIT. It adds 138 tokens to every session and 3,270 once invoked, about $0.0007 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 2026-08-31.
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