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/abilityai/abilities/sprintnpx skills add Abilityai/abilities --skill sprintgit clone --depth 1 https://github.com/Abilityai/abilitiesWhat 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 | $0.00025 | $0.00722 |
| Opus 5 | $0.00013 | $0.00361 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
sprint 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sprint
ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change — the top entry of
metadata.changelogabove — e.g.sprint vX.Y — recent: <summary>. Then proceed.
Walks through one complete agent development cycle for a single issue. The implement step is intentionally human-driven — this skill frames and bookends it. The autonomous equivalent is /work-loop.
When to Use
- You want to work on a specific backlog issue with full context at each step
- You want autoplan analysis before touching any skill files
- You're onboarding to the agent and want a guided workflow
Process
Step 1: Roadmap Check
Invoke /roadmap to show the skill-grouped backlog. This surfaces which skills have the most open work and helps choose a focus area.
If $ARGUMENTS is provided (a specific issue number), skip roadmap and go directly to Step 2 with that issue.
Step 2: Claim
Invoke /claim (or /claim $ARGUMENTS if an issue number was provided).
If already in-progress from a previous session, continue with that issue and skip to Step 3.
Step 3: Autoplan
Invoke /autoplan on the claimed issue.
The plan will identify:
- Which SKILL.md is affected
- Which section to change
- The right tool to use (adjust-playbook vs create-playbook)
- Any risks
Wait for the plan output before proceeding.
Step 4: Implement (Human Step)
Print clearly:
## Ready to Implement
Based on the autoplan, run one of:
/adjust-playbook $SKILL_NAME — to modify an existing skill
/create-playbook — to scaffold a new skill
Make your changes, then return here and confirm to commit.
Ask the user: "Type 'done' when the implementation is complete, or 'abort' to stop without committing."
If 'abort': do nothing. Leave the issue in-progress for the next session.
Step 5: Commit
Invoke /commit to stage the changed skill files, write the commit message, and close the issue.
Step 6: Finish
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 · 98 lines · 25 tokens per session scan A 67d6832d8d7f
sprint is a skill published in the GitHub repository Abilityai/abilities (11 stars, last pushed 14d ago), licensed MIT. It adds 25 tokens to every session and 722 once invoked, about $0.0001 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.
Other skills, from other repositories
babysit
Same-session monitoring loop for PRs, CI runs, tickets, and deployments using the monitorstart / monitorupdate / autonudgestop MCP tools. The loop re-injects your check instructions into THIS session on an idle interval — same context, same tools — and works from dashboard chat, Slack threads, and Discord DMs. Use…
teamharness-task-delegation
Use when a Leader turns ready Quick Task or Project Work state into Worker task instructions, sends assignment messages, checks submitted results, and defines completion/blocker report contracts. Do not use to create projects, create rooms, or execute Worker tasks.
github-workflow
Use GitHub workflow tools to read work status, draft reports, summarize follow-ups, and execute only approved issue mutations.
flow-next-tracker-sync
Project a flow-next spec to a tracker issue (Linear, GitHub, GitLab, Jira) and reconcile two-way. Use when asked to sync to a tracker. NOT plan-sync.
gsd-executor
Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.
dot-ai-prd-create
Create documentation-first PRDs that guide development through user-facing content.