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/autoplannpx skills add Abilityai/abilities --skill autoplangit 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 | $0.01119 |
| Opus 5 | $0.00013 | $0.00560 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
autoplan 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 3d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoplan
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.autoplan vX.Y — recent: <summary>. Then proceed.
Analyze an open issue before touching any files. Reads the affected skill's SKILL.md, understands the current behavior, and produces a focused implementation plan. Run this after /claim and before /adjust-playbook or /create-playbook.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| GitHub Issues | Current repo | Yes | No | Issue to analyze |
| SKILL.md files | .claude/skills/*/SKILL.md | Yes | No | Current playbook behavior |
| CLAUDE.md | ./CLAUDE.md | Yes | No | Agent identity and constraints |
Process
Step 1: Identify the Issue
If $ARGUMENTS provided: load that issue number.
If no argument: find current in-progress issue:
gh issue list --label "status:in-progress" --state open --json number,title,body,labels --limit 1
If none in-progress, ask user to provide an issue number or run /claim first.
Step 2: Load the Issue
gh issue view $NUMBER --json number,title,body,labels
Step 3: Identify Affected Skill
Look for a skill:* label on the issue. Extract the skill name.
If no skill label:
- Infer from the issue title/body (e.g., "fix claim flow" → likely
claim) - Confirm with user: "This looks like it affects
claim. Is that right, or is it project-level?"
If project-level (no specific skill): note that and skip to Step 6.
Step 4: Read the Affected Skill
cat .claude/skills/$SKILL_NAME/SKILL.md
If skill doesn't exist yet (this is a new skill issue), note that and skip to Step 5b.
Step 5a: Analyze the Change (existing skill)
Compare the issue requirements against the current SKILL.md. Identify:
What section(s) change:
- Frontmatter (name, description, tools, automation type)?
- A specific step in the Process?
- A new step added?
- Output format changes?
- Error handling?
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.
- 3d ago First seen · 136 lines · 25 tokens per session scan A 5511d5c26166
autoplan is a skill published in the GitHub repository Abilityai/abilities (11 stars, last pushed 15d ago), licensed MIT. It adds 25 tokens to every session and 1,119 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
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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