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Get Started Free →Enforces token quota management at session start with conservation and compression checks. Use at the start of every session or before large context loads.
.claude/skills/athola-token-conservation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 137% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 83% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 14% | 0% |
token-conservation:quota-checktoken-conservation:context-plantoken-conservation:delegation-checktoken-conservation:compression-reviewtoken-conservation:loggingquota-check)/status or notebook).Note the 5-hour rolling cap and weekly cap highlighted in the Claude community notice.
context-plan)Read calland each content-mode Grep as one read. Glob and files-with-matches Grep are free.
imports/interfaces, then stop and start writing.
self-authorize additional reads. Only explicit user approval overrides the budget.
Read with offset/limit params or Grep tool over loading whole files.A Read targeting <50 lines counts as 0.5 reads. Avoid cat/sed/awk via Bash: Claude Code 2.1.21+ steers toward native file tools (Read, Edit, Write, Grep, Glob).
Read with pages: "1-5" for targeted PDF readinginstead of loading entire documents. Large PDFs (>10 pages) return a lightweight reference when @-mentioned, so use the pages parameter to read specific sections. Hard limits: 100 pages max, 20MB max per PDF. Exceeding these previously locked sessions permanently (fixed in 2.1.31).
info hits the model.
delegation-check)tooling (use qwen-delegation skill if needed).
of LLM reasoning when possible.
compression-review)Remove redundant history, collapse logs, and avoid reposting identical code.
prompt caching ideas: reference prior outputs instead of restating themwhen the model has already processed the information (cite snippet IDs).
via the message selector to partially summarize the conversation. This preserves recent context while compressing older portions
instruct the user to run /new
prompt them to run /compact before continuing
memories automatically. This adds minor token overhead but improves cross-session continuity. No action needed: be aware it contributes to baseline context usage.
logging)Document the conservation tactics that were applied and note the remaining token budget. If the budget is low, explicitly warn the user and propose secondary plans. Record any recommendations made regarding the use of /new or /compact, or justify why neither was necessary, to inform future context-handling decisions.
/new or /compact whenever you determine it would savetokens (otherwise state that no reset/compaction is needed yet).
quota-check, context-plan, delegation-check, compression-review, logging
Read call, withthe task type (spec/bug/refactor/exploration) and max-reads limit recorded
"budget low") and includes an explicit /new or /compact recommendation, or a justification for why neither is needed
exhausted without explicit user approval in the same session
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 9,805 | 5,502 | -44% | 1 | 1 | 0% | 1,552 | 2,147 | +38% | 0 | 0 | — |
case-01 | fail→fail | 10,605 | 9,596 | -10% | 1 | 1 | 0% | 1,553 | 2,836 | +83% | 0 | 0 | — |
case-02 | fail→pass | 7,559 | 9,302 | +23% | 1 | 1 | 0% | 1,210 | 2,863 | +137% | 0 | 0 | — |
case-03 | fail→fail | 11,893 | 7,661 | -36% | 1 | 1 | 0% | 1,750 | 1,695 | -3% | 0 | 0 | — |
case-04 | fail→pass | 11,255 | 14,384 | +28% | 1 | 1 | 0% | 1,644 | 3,012 | +83% | 0 | 0 | — |
case-05 | pass→fail | 14,920 | 11,477 | -23% | 1 | 1 | 0% | 2,175 | 1,810 | -17% | 0 | 0 | — |
case-06 | fail→pass | 8,106 | 8,583 | +6% | 1 | 1 | 0% | 1,196 | 2,094 | +75% | 0 | 0 | — |
case-07 | pass→pass | 8,481 | 5,422 | -36% | 1 | 1 | 0% | 1,377 | 2,165 | +57% | 0 | 0 | — |
case-08 | fail→pass | 16,143 | 6,730 | -58% | 1 | 1 | 0% | 2,693 | 2,423 | -10% | 0 | 0 | — |
case-09 | pass→pass | 4,606 | 6,039 | +31% | 1 | 1 | 0% | 781 | 2,247 | +188% | 0 | 0 | — |
case-10 | fail→pass | 11,605 | 9,101 | -22% | 1 | 1 | 0% | 1,762 | 2,013 | +14% | 0 | 0 | — |
case-11 | fail→pass | 12,947 | 3,631 | -72% | 1 | 1 | 0% | 1,398 | 1,837 | +31% | 0 | 0 | — |
case-12 | pass→pass | 14,484 | 14,475 | -0% | 1 | 1 | 0% | 2,129 | 3,425 | +61% | 0 | 0 | — |
case-13 | pass→pass | 18,267 | 17,045 | -7% | 1 | 1 | 0% | 2,780 | 3,889 | +40% | 0 | 0 | — |
case-15 | pass→pass | 12,667 | 11,569 | -9% | 1 | 1 | 0% | 1,958 | 2,922 | +49% | 0 | 0 | — |
case-16 | pass→fail | 9,755 | 3,956 | -59% | 1 | 1 | 0% | 1,725 | 1,892 | +10% | 0 | 0 | — |
case-17 | fail→pass | 15,965 | 9,260 | -42% | 1 | 1 | 0% | 2,421 | 2,579 | +7% | 0 | 0 | — |
case-18 | fail→pass | 13,659 | 6,059 | -56% | 1 | 1 | 0% | 2,127 | 2,243 | +5% | 0 | 0 | — |
case-19 | fail→pass | 12,724 | 8,795 | -31% | 1 | 1 | 0% | 1,954 | 2,803 | +43% | 0 | 0 | — |
case-20 | pass→pass | 9,132 | 2,117 | -77% | 1 | 1 | 0% | 1,315 | 1,555 | +18% | 0 | 0 | — |
case-21 | pass→pass | 2,983 | 3,075 | +3% | 1 | 1 | 0% | 512 | 1,796 | +251% | 0 | 0 | — |
case-22 | fail→fail | 1,580 | 3,991 | +153% | 1 | 1 | 0% | 245 | 1,888 | +671% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 20 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.