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 skills add ychampion/cskill-agents --skill reserved-summary-output-budgetgit clone --depth 1 https://github.com/ychampion/cskill-agentsWrote 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/ychampion/cskill-agents/reserved-summary-output-budget)<a href="https://agentmods.dev/skills/ychampion/cskill-agents/reserved-summary-output-budget"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/reserved-summary-output-budget/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/ychampion/cskill-agents/reserved-summary-output-budget"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/reserved-summary-output-budget.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00025 | $0.00423 |
| Opus 5 | $0.00013 | $0.00211 |
| Sonnet 5 | $0.00005 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
reserved-summary-output-budget 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 7d 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.
What it actually says
SKILL: Reserved Summary Output Budget
Domain: context-management
Trigger: Apply when proving that proactive compaction must shrink the usable context window to keep the summary output under a known token budget.
Source Pattern: Distilled from reviewed session memory, compaction, and context-budgeting implementations.
Core Method
Before inviting a compaction, shrink the effective context window by reserving room for the post-compaction summary (and any optionally re-injected skills/output). Calculate the base context window for the model, subtract a fixed upper bound on the summary tokens (20k here), and feed that reduced window to downstream budgeting logic so warning thresholds and blocking limits reflect the diminished headroom.
Key Rules
- Base the reservation on the model’s max output tokens so languages with smaller outputs still reserve enough space.
- Keep the reserved summary allowance explicit and documented (e.g., max output tokens for summary 20 000) so future tweaks know why the window shrank.
- Derive warning/error thresholds from the reduced window so percent-left and blocking checks respect the reservation.
- Offer an override path (e.g., claude autocompact pct override or blocking limit overrides) to aid testing without losing the rationale.
- Treat the reserved portion as untouchable – only the remaining tokens count toward auto-compact triggers or manual compaction budgets.
Example Application
When autocompact fires, compute the effective window as context window reserved summary tokens, then generate warnings based on that clipped value so autocompact threshold, warning threshold, and blocking limit stay consistent even though the API still sees the full context.
Anti-Patterns (What NOT to do)
- Don’t ignore the summary tokens when computing thresholds; doing so lets the summary overflow the model’s output limit.
- Don’t hardcode warnings to the raw context window without documenting the inviolable reservation.
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.
- 7d ago First seen · 29 lines · 25 tokens per session scan A 6c2ddfc99c38
reserved-summary-output-budget is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 423 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 2026-09-03.
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