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/warpdotdev/common-skills/saganpx skills add warpdotdev/common-skills --skill sagagit clone --depth 1 https://github.com/warpdotdev/common-skillsWrote 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/warpdotdev/common-skills/saga)<a href="https://agentmods.dev/skills/warpdotdev/common-skills/saga"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/saga.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00175 | $0.03563 |
| Opus 5 | $0.00088 | $0.01782 |
| Sonnet 5 | $0.00035 | $0.00713 |
| Haiku 4.5 | $0.00017 | $0.00356 |
Grade A, and why
saga 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 4d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Saga
Saga is an autonomous, spec-driven development workflow for medium-to-large features that should be implemented mostly without human intervention, except at a few discrete touch points. You act as the orchestrator: you turn a rough prompt into an airtight spec, then delegate implementation to a fleet of worker subagents while keeping your own context window clean.
The whole method rests on one bet: if the spec defines every task with validation criteria tight enough to form a contract, then workers can execute in parallel and self-verify, and the saga succeeds with almost no human babysitting. The quality of the saga is therefore decided in Phase 1, before a single line is written.
Core principles
- Airtight contracts over good intentions. A task is only ready to delegate when its validation criteria are so explicit that meeting them leaves little-to-no possibility the task was done wrong. Ambiguity is the enemy; resolve it during planning, not during implementation.
- No whitespace. During planning, make every requirement explicit. Do not leave decisions to a worker's discretion unless the user has explicitly granted that discretion. Workers should never have to guess what "done" means.
- Protect the orchestrator's context. You are the long-lived coordinator. Push heavy reading, research, and implementation onto workers; receive compact reports back. Keep state on disk (in the saga directory's spec tree and
PROGRESS.md) so your understanding survives compaction and you can re-read rather than re-hold. This maximizes time-to-compaction and keeps you coherent across the whole run. - Validation is first-class. Every task and the saga as a whole carries verification criteria defined up front, and a concrete method for checking them (computer use, interactive CLI, or tests). See
references/validation-strategies.md. - A few human touch points, not zero. The human approves the spec (end of Phase 1), is consulted only when the spec genuinely cannot resolve a blocker (Phase 2), and does the final manual acceptance (Phase 3).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 163 lines · 175 tokens per session scan A 5485e1fbe3d2
saga is a skill published in the GitHub repository warpdotdev/common-skills (520 stars, last pushed today), licensed MIT. It adds 175 tokens to every session and 3,563 once invoked, about $0.0009 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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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.
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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.
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