{
 "measurement": "Whether the +50% median saving the author publishes against their own terse control reproduces inside agentic Claude Code under a different arm construction and a different prompt set",
 "ran_on": "2026-08-20",
 "claude_code_version": "2.1.236",
 "claude_code_version_recorded_at_runtime": "2.1.236 (Claude Code)",
 "pack": "JuliusBrussee/caveman",
 "pack_commit": "702da5c",
 "pack_stars_at_measurement": 99332,
 "skill_chars": 6560,
 "the_claim": "The README benchmark table averages 65% fewer output tokens, and the skill's own `description:` field reads 'Cuts output tokens 65% (measured)'.",
 "the_author_addressed_this_confound_himself_and_published_a_result": "This is not a gotcha and must never be written as one. The README says, immediately under the table: \"'Normal' above means an unprompted assistant, not a terse one. Some of that 65% is what any 'answer concisely' instruction would buy you. benchmarks/run.py now runs a terse control arm alongside the other two, so the next regenerated table splits the two apart; the numbers above predate it.\" It also carries an 'Honest number warning' saying the skill only shrinks OUTPUT tokens, adds roughly 1-1.5k input tokens per turn, and can go net-negative on already-terse workloads. The control exists in benchmarks/run.py as TERSE_SYSTEM = 'Answer concisely.', and a terse-controlled result IS ALREADY PUBLISHED, at evals/snapshots/results.json, git-tracked at commit 702da5c: the author's own evals/measure.py reports caveman at +50% median / +46% mean additional reduction on top of terse. An earlier version of this corpus said that control had never been published. That was FALSE and is corrected here. This corpus runs the benchmarks/run.py arm construction instead, which asks a different question.",
 "population_and_estimator": {
  "population": "Claude Code 2.1.236 in an otherwise empty isolated project, one machine. The author's own five prompts from benchmarks/prompts.json, chosen before running to span the full 22-87% range their table reports rather than the top of it. Three arms differing ONLY in the --append-system-prompt string, interleaved within each round. 3 arms x 5 tasks x 4 rounds = 60 runs.",
  "estimator": "NON-OVERLAP of observed ranges, and the reason is in the data rather than in preference. Model output length is stochastic, not deterministic like the context measurements elsewhere on this site: the guarded mode is REFUSED in 15 of 15 cells because no value repeats, and with four rounds the median is a value no round produced in 15 of 15. So arms are compared by whether their observed ranges overlap. Medians appear below labelled descriptive_only and NO percentage from them is a finding."
 },
 "results": {
  "separation_counts_over_5_tasks": {
   "output_caveman_separates_from_normal": "5/5",
   "output_caveman_separates_from_terse": "0/5",
   "output_terse_separates_from_normal": "5/5",
   "cost_caveman_separates_from_normal": "4/5",
   "cost_caveman_separates_from_terse": "1/5",
   "the_one_cost_separation_from_terse": [
    {
     "task": "react-rerender",
     "caveman_cheaper": false
    }
   ]
  },
  "descriptive_means_of_per_task_medians": {
   "output_tokens": {
    "normal": 1908.8,
    "terse": 1036.4,
    "caveman": 1074.7
   },
   "cost_usd": {
    "normal": 0.10299,
    "terse": 0.07922,
    "caveman": 0.08713
   },
   "caution": "These are means of per-task medians and every one of those medians is a value no round produced. They are here to describe magnitude, not to carry a claim. The separation counts above are the finding."
  },
  "what_the_system_prompt_costs": {
   "caveman_minus_normal_context_tokens": {
    "react-rerender": 2517,
    "git-rebase-merge": 2517,
    "microservices-monolith": 2517,
    "async-refactor": 2517
   },
   "distinct_values": [
    2517
   ],
   "note": "Measured on SINGLE-TURN runs only, because each extra turn re-sends the whole conversation and 13 of the 60 runs took more than one turn. On those runs context is identical within every arm, and the caveman system prompt costs exactly 2,517 tokens on every task, with zero variance. That is the fixed price of a 6,560-character system prompt, paid on every call whether it helps or not."
  },
  "by_task": {
   "react-rerender": {
    "category": "debugging",
    "rounds_per_arm": 4,
    "output_tokens": {
     "normal": {
      "all_rounds": [
       1213,
       1252,
       1287,
       1437
      ],
      "range": [
       1213,
       1437
      ],
      "median_descriptive_only": 1269.5
     },
     "terse": {
      "all_rounds": [
       524,
       658,
       662,
       803
      ],
      "range": [
       524,
       803
      ],
      "median_descriptive_only": 660.0
     },
     "caveman": {
      "all_rounds": [
       442,
       570,
       621,
       636
      ],
      "range": [
       442,
       636
      ],
      "median_descriptive_only": 595.5
     }
    },
    "cost_usd": {
     "normal": {
      "all_rounds": [
       0.0979685,
       0.0989435,
       0.0998185,
       0.11877200000000002
      ],
      "range": [
       0.0979685,
       0.11877200000000002
      ],
      "median_descriptive_only": 0.099381
     },
     "terse": {
      "all_rounds": [
       0.0807135,
       0.0840635,
       0.0841635,
       0.0876885
      ],
      "range": [
       0.0807135,
       0.0876885
      ],
      "median_descriptive_only": 0.08411350000000001
     },
     "caveman": {
      "all_rounds": [
       0.1038635,
       0.10706349999999999,
       0.10833849999999999,
       0.10871349999999999
      ],
      "range": [
       0.1038635,
       0.10871349999999999
      ],
      "median_descriptive_only": 0.10770099999999999
     }
    },
    "separation": {
     "out_caveman_vs_normal": true,
     "out_caveman_vs_terse": false,
     "out_terse_vs_normal": true,
     "cost_caveman_vs_normal": false,
     "cost_caveman_vs_terse": true
    }
   },
   "error-boundary": {
    "category": "implementation",
    "rounds_per_arm": 4,
    "output_tokens": {
     "normal": {
      "all_rounds": [
       4047,
       4426,
       4580,
       5595
      ],
      "range": [
       4047,
       5595
      ],
      "median_descriptive_only": 4503.0
     },
     "terse": {
      "all_rounds": [
       1911,
       1971,
       2717,
       4030
      ],
      "range": [
       1911,
       4030
      ],
      "median_descriptive_only": 2344.0
     },
     "caveman": {
      "all_rounds": [
       2267,
       2586,
       2688,
       3003
      ],
      "range": [
       2267,
       3003
      ],
      "median_descriptive_only": 2637.0
     }
    },
    "cost_usd": {
     "normal": {
      "all_rounds": [
       0.18656150000000002,
       0.19541999999999998,
       0.20094100000000004,
       0.23379999999999998
      ],
      "range": [
       0.18656150000000002,
       0.23379999999999998
      ],
      "median_descriptive_only": 0.1981805
     },
     "terse": {
      "all_rounds": [
       0.122135,
       0.122755,
       0.14618150000000002,
       0.1855275
      ],
      "range": [
       0.122135,
       0.1855275
      ],
      "median_descriptive_only": 0.13446825
     },
     "caveman": {
      "all_rounds": [
       0.1361385,
       0.1459315,
       0.149173,
       0.15848099999999998
      ],
      "range": [
       0.1361385,
       0.15848099999999998
      ],
      "median_descriptive_only": 0.14755225
     }
    },
    "separation": {
     "out_caveman_vs_normal": true,
     "out_caveman_vs_terse": false,
     "out_terse_vs_normal": true,
     "cost_caveman_vs_normal": true,
     "cost_caveman_vs_terse": false
    }
   },
   "git-rebase-merge": {
    "category": "explanation",
    "rounds_per_arm": 4,
    "output_tokens": {
     "normal": {
      "all_rounds": [
       1173,
       1194,
       1243,
       1271
      ],
      "range": [
       1173,
       1271
      ],
      "median_descriptive_only": 1218.5
     },
     "terse": {
      "all_rounds": [
       708,
       755,
       888,
       933
      ],
      "range": [
       708,
       933
      ],
      "median_descriptive_only": 821.5
     },
     "caveman": {
      "all_rounds": [
       658,
       756,
       908,
       995
      ],
      "range": [
       658,
       995
      ],
      "median_descriptive_only": 832.0
     }
    },
    "cost_usd": {
     "normal": {
      "all_rounds": [
       0.070034,
       0.070559,
       0.071784,
       0.072424
      ],
      "range": [
       0.070034,
       0.072424
      ],
      "median_descriptive_only": 0.0711715
     },
     "terse": {
      "all_rounds": [
       0.058407499999999994,
       0.0595825,
       0.0629075,
       0.06403249999999999
      ],
      "range": [
       0.058407499999999994,
       0.06403249999999999
      ],
      "median_descriptive_only": 0.061245
     },
     "caveman": {
      "all_rounds": [
       0.0584175,
       0.06086749999999999,
       0.0646675,
       0.06684249999999999
      ],
      "range": [
       0.0584175,
       0.06684249999999999
      ],
      "median_descriptive_only": 0.0627675
     }
    },
    "separation": {
     "out_caveman_vs_normal": true,
     "out_caveman_vs_terse": false,
     "out_terse_vs_normal": true,
     "cost_caveman_vs_normal": true,
     "cost_caveman_vs_terse": false
    }
   },
   "microservices-monolith": {
    "category": "architecture",
    "rounds_per_arm": 4,
    "output_tokens": {
     "normal": {
      "all_rounds": [
       1651,
       1953,
       1985,
       2668
      ],
      "range": [
       1651,
       2668
      ],
      "median_descriptive_only": 1969.0
     },
     "terse": {
      "all_rounds": [
       808,
       1068,
       1077,
       1167
      ],
      "range": [
       808,
       1167
      ],
      "median_descriptive_only": 1072.5
     },
     "caveman": {
      "all_rounds": [
       959,
       1001,
       1038,
       1212
      ],
      "range": [
       959,
       1212
      ],
      "median_descriptive_only": 1019.5
     }
    },
    "cost_usd": {
     "normal": {
      "all_rounds": [
       0.08203400000000001,
       0.08958400000000001,
       0.090384,
       0.10743899999999999
      ],
      "range": [
       0.08203400000000001,
       0.10743899999999999
      ],
      "median_descriptive_only": 0.08998400000000001
     },
     "terse": {
      "all_rounds": [
       0.0609575,
       0.0674375,
       0.06768249999999999,
       0.0699325
      ],
      "range": [
       0.0609575,
       0.0699325
      ],
      "median_descriptive_only": 0.06756
     },
     "caveman": {
      "all_rounds": [
       0.06601249999999999,
       0.0670625,
       0.06794750000000001,
       0.07231749999999999
      ],
      "range": [
       0.06601249999999999,
       0.07231749999999999
      ],
      "median_descriptive_only": 0.06750500000000001
     }
    },
    "separation": {
     "out_caveman_vs_normal": true,
     "out_caveman_vs_terse": false,
     "out_terse_vs_normal": true,
     "cost_caveman_vs_normal": true,
     "cost_caveman_vs_terse": false
    }
   },
   "async-refactor": {
    "category": "refactor",
    "rounds_per_arm": 4,
    "output_tokens": {
     "normal": {
      "all_rounds": [
       466,
       524,
       644,
       694
      ],
      "range": [
       466,
       694
      ],
      "median_descriptive_only": 584.0
     },
     "terse": {
      "all_rounds": [
       257,
       280,
       288,
       312
      ],
      "range": [
       257,
       312
      ],
      "median_descriptive_only": 284.0
     },
     "caveman": {
      "all_rounds": [
       235,
       270,
       309,
       317
      ],
      "range": [
       235,
       317
      ],
      "median_descriptive_only": 289.5
     }
    },
    "cost_usd": {
     "normal": {
      "all_rounds": [
       0.053298,
       0.054748,
       0.057708,
       0.058958
      ],
      "range": [
       0.053298,
       0.058958
      ],
      "median_descriptive_only": 0.056228
     },
     "terse": {
      "all_rounds": [
       0.0480315,
       0.048606500000000004,
       0.0487865,
       0.0494065
      ],
      "range": [
       0.0480315,
       0.0494065
      ],
      "median_descriptive_only": 0.048696500000000004
     },
     "caveman": {
      "all_rounds": [
       0.0487415,
       0.0496165,
       0.050591500000000005,
       0.0508315
      ],
      "range": [
       0.0487415,
       0.0508315
      ],
      "median_descriptive_only": 0.050104
     }
    },
    "separation": {
     "out_caveman_vs_normal": true,
     "out_caveman_vs_terse": false,
     "out_terse_vs_normal": true,
     "cost_caveman_vs_normal": true,
     "cost_caveman_vs_terse": false
    }
   }
  },
  "the_replies_are_shorter_in_characters": {
   "caveman_vs_terse_round_matched_pairs": 20,
   "caveman_shorter_in": 14,
   "geometric_mean_char_ratio": 0.8701,
   "geometric_mean_output_token_ratio": 0.9807,
   "chars_per_output_token": {
    "async-refactor": {
     "terse": 2.26,
     "caveman": 2.17
    },
    "error-boundary": {
     "terse": 1.22,
     "caveman": 1.18
    },
    "git-rebase-merge": {
     "terse": 2.82,
     "caveman": 2.4
    },
    "microservices-monolith": {
     "terse": 2.66,
     "caveman": 2.5
    },
    "react-rerender": {
     "terse": 2.27,
     "caveman": 1.68
    }
   },
   "what_it_means": "The skill DOES compress the prose: 13% fewer characters against terse, shorter in 14 of 20 round-matched pairs, and on react-rerender the character ranges separate outright. But characters per output token are LOWER for caveman on all five tasks, so the shorter prose tokenises more densely and the two roughly cancel. This is an inference from a correlation: no arm here varies tokenisation while holding style fixed."
  },
  "the_test_has_a_floor": {
   "p_separate_under_null_n4": 0.0286,
   "power_at_true_saving": {
    "10%": 0.081,
    "20%": 0.292,
    "30%": 0.648,
    "40%": 0.914
   },
   "pooled_within_cell_log_sd": 0.1488,
   "what_it_means": "Range non-overlap at four rounds a side fires 2.9% of the time under no effect at all. Simulated at the spread these runs show, its power is 0.08 against a true 10% saving and 0.65 against 30%. So '0 of 5' rules out an effect above roughly 40% and does NOT establish equality. It cannot refute a real 10-30% saving."
  }
 },
 "controls": {
  "isolation": "--setting-sources project asserted onto argv in every call.",
  "treatment": "The exact system-prompt string is hashed and published per run, so 'the caveman text was in this call' is measured rather than assumed.",
  "arrival": "Reply must be non-empty. 60/60.",
  "fixture": "SKILL.md asserted at 6,560 characters before any round.",
  "interleaved": "All three arms ran inside every round, so drift cannot land on one arm.",
  "models": "Every model label is published per run. Claude Code routed part of each turn to a second model, so output tokens are SUMMED across models rather than taken from one.",
  "turn_balance": "Single-turn runs by arm: normal 15, terse 16, caveman 16. Three-turn runs: 4 each. One normal run took 2 turns. Turn count is the main confound on the context measure and is why the 2,517 figure uses single-turn runs only.",
  "security_scanned": "Only SKILL.md text was read. Nothing from the repository was installed or executed: the proxy, the Go binaries and the cache engine were never built or run, so no credential surface was touched. The two danger-grep hits in the repository's scripts were read in context and are DEFENSIVE: a hard refuse-before-read blocklist covering .env, .ssh, .aws, credentials and key files, and an ordinary ANTHROPIC_API_KEY read for the official SDK. Positive control on the grep fired 2/2."
 },
 "known_limits": {
  "absolute_numbers_do_not_transfer_to_their_benchmark": "Their benchmarks/run.py calls the raw Anthropic API, where the system prompt is essentially the whole context. This calls Claude Code, which adds its own system prompt, tool definitions and a project directory. Absolute token counts here are NOT comparable to theirs and no comparison is attempted. What transfers is the relation between arms measured under identical conditions, which is what the 65% is a claim about.",
  "five_tasks_is_not_a_workload": "Five prompts, four rounds each, one machine, one release. The tasks were picked to span their reported range, not sampled from anything. A different task mix would move the descriptive percentages; whether it would change the separation counts is untested.",
  "this_measures_tokens_and_nothing_else": "The author states plainly that the real win is readability and speed and that cost is a bonus. Nothing here measures readability, speed, or whether the answers are equally good. An arm that overlaps another on tokens may still be the one you would rather read, and this corpus takes no position on that.",
  "the_empty_project_is_visible_to_the_model": "Runs happen in an empty directory and some replies notice it, for example opening with a note that the working directory is empty. Identical across all three arms in every round, so it cannot land on one arm, but it is not a neutral setting.",
  "no_wrap_benchmark_here": "The repository's other headline, 33.2% fewer provider-reported input tokens, comes from wrapping the agent in a local proxy. That was NOT run: it routes provider traffic and handles API credentials, and this corpus deliberately touched no credential surface. Nothing here bears on that figure."
 },
 "the_author_publishes_a_terse_controlled_result_and_it_disagrees": {
  "where": "evals/snapshots/results.json, git-tracked at commit 702da5c",
  "their_result": {
   "baseline_tokens": 1948,
   "terse_tokens": 2045,
   "caveman_tokens": 1026,
   "median_additional_reduction_vs_terse": "+50%",
   "mean": "+46%"
  },
  "their_conditions": "10 prompts, ONE run per arm, claude-opus-4-6, CLI 2.1.97, tiktoken o200k_base approximation, generated 2026-04-08.",
  "the_vehicle_is_the_SAME_not_different": "Correcting an error a draft of the post made. evals/llm_run.py, the harness that produced the +50%, shells out to `claude -p --system-prompt`: the SAME agentic Claude Code CLI this corpus uses, at default temperature with tools. Only their OTHER harness, benchmarks/run.py, calls the raw Anthropic API at temperature=0 with no tools. So 'a different vehicle' does NOT explain the disagreement and must never be offered as one.",
  "the_prompt_sets_DO_NOT_OVERLAP": "evals/prompts/en.txt, behind the +50%, and benchmarks/prompts.json, from which this corpus takes five of ten, share not one prompt. The two results are measured on disjoint task sets, which is plausibly the largest single difference between them and is not quantified here.",
  "their_arm_construction_differs": "evals/llm_run.py compares 'Answer concisely.\n\n{SKILL.md}' against 'Answer concisely.', i.e. the skill ON TOP OF terse, delivered with --system-prompt which REPLACES. This corpus follows their OTHER harness, benchmarks/run.py, comparing the skill ALONE against terse alone, delivered with --append-system-prompt which APPENDS to Claude Code's own ~22,000-token system prompt. Two of the author's own designs, and they disagree. Neither is refuted here.",
  "a_first_draft_of_this_got_it_wrong": "An unpublished draft claimed this control had never been published. It had. The pointer was in the very file the draft quoted: benchmarks/run.py's comment on TERSE_SYSTEM names evals/llm_run.py twice. Recorded so the error is not repeatable."
 },
 "headline": "Under this design the caveman skill's output range separates from an unprompted baseline on 5 of 5 tasks and from the string 'Answer concisely.' on 0 of 5, with a round-matched paired ratio of 0.98. The replies ARE shorter though: 13% fewer characters, shorter in 14 of 20 pairs. Characters per output token are lower for caveman on all five tasks, so the prose compresses and the tokens do not follow. The author publishes a terse-controlled result of their own, under a DIFFERENT arm construction on an older model, reporting +50%; these two designs disagree and neither is refuted here. The test's floor rules out an effect above roughly 40%, not equality.",
 "runs": [
  {
   "task": "react-rerender",
   "category": "debugging",
   "arm": "normal",
   "round": 0,
   "status": "ok",
   "system_prompt_md5": "943e842fab4e",
   "system_prompt_chars": 28,
   "output_tokens": 1213,
   "input_tokens": 923,
   "cache_creation": 5902,
   "cache_read": 16025,
   "total_context": 22850,
   "cost_usd": 0.0979685,
   "turns": 1,
   "models": [
    "claude-haiku-4-5-20251001",
    "claude-opus-5[1m]"
   ],
   "reply_chars": 2428,
   "reply_nonempty": true
  },
  {
   "task": "react-rerender",
   "category": "debugging",
   "arm": "terse",
   "round": 0,
   "status": "ok",
   "system_prompt_md5": "63b1f41494fb",
   "system_prompt_chars": 17,
   "output_tokens": 658,
   "input_tokens": 923,
   "cache_creation": 5899,
   "cache_read": 16025,
   "total_context": 22847,
   "cost_usd": 0.0840635,
   "turns": 1,
   "models": [
    "claude-haiku-4-5-20251001",
    "claude-opus-5[1m]"
   ],
   "reply_chars": 1539,
   "reply_nonempty": true
  },
  {
   "task": "react-rerender",
   "category": "debugging",
   "arm": "caveman",
   "round": 0,
   "status": "ok",
   "system_prompt_md5": "f4717c3fd9df",
   "system_prompt_chars": 6560,
   "output_tokens": 442,
   "input_tokens": 923,
   "cache_creation": 8419,
   "cache_read": 16025,
   "total_context": 25367,
   "cost_usd": 0.1038635,
   "turns": 1,
   "models": [
    "claude-haiku-4-5-20251001",
    "claude-opus-5[1m]"
   ],
   "reply_chars": 753,
   "reply_nonempty": true
  },
  {
   "task": "error-boundary",
   "category": "implementation",
   "arm": "normal",
   "round": 0,
   "status": "ok",
   "system_prompt_md5": "943e842fab4e",
   "system_prompt_chars": 28,
   "output_tokens": 4426,
   "input_tokens": 926,
   "cache_creation": 5259,
   "cache_read": 62940,
   "total_context": 69125,
   "cost_usd": 0.19541999999999998,
   "turns": 3,
   "models": [
    "claude-haiku-4-5-20251001",
    "claude-opus-5[1m]"
   ],
   "reply_chars": 5361,
   "reply_nonempty": true
  },
  {
   "task": "error-boundary",
   "category": "implementation",
   "arm": "terse",
   "round": 0,
   "status": "ok",
   "system_prompt_md5": "63b1f41494fb",
   "system_prompt_chars": 17,
   "output_tokens": 4030,
   "input_tokens": 926,
   "cache_creation": 5261,
   "cache_read": 62915,
   "total_context": 69102,
   "cost_usd": 0.1855275,
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