Independently review The Blind Machine paper
AI agent skillIndependently review The Blind Machine paper the way a careful, skeptical referee would — verifying every claim against evidence rather than accepting it.
Download & use this skill
Download .tar.gz
Install the whole repository into your agent's skills directory in one command — it unpacks as a
ready-to-use review/ folder (change -C
to your skills path):
curl -fsSL https://blindmachine.org/skills/review/archive | tar xz -C ~/.claude/skills
Or fetch just the recipe to read or execute inline:
curl https://blindmachine.org/skills/review.md
Review dimensions
Work through all six. For each finding, record severity, location, the problem, the
evidence, and a concrete fix.
1. Claim-evidence integrity
Trace every headline number to its source and confirm it matches (see
claim-evidence-map.md). Specifically:
- The synthetic exactness taxonomy: six applications,
max_error0, 128-bit
ciphertext sizes 262,282 / 1,310,882 / 2,621,791 bytes, 5× and 10× premiums. - The real-genome studies (Section 11): E5 mean absolute AF delta ≈ 0.0609; E6 max
F_ST-like 0.1363 and 624 suppressed rows; E7 recovery rates (1.000 under
min-N-20, 0.000 under freeze, 0.125 under query budget 5); E8 exact product
moments and maxr21.0000.
Flag any value that does not match, any transposed digit, and any table whose rows
disagree with its cited CSV/JSON.
2. Security analysis (Section 9)
- Confirm the coverage matrix (Table 4) and the per-attack catalog agree: every
threat in the matrix has a structured subsection, and every subsection maps to a
matrix row. - Confirm every attack subsection uses the same seven-field template (Adversary,
Attack, Assets at risk, Platform control, Verification hook, Status, Residual
risk / evidence) and that each Status is drawn from the fixed vocabulary
(Prevented / Detectable / Mitigated / Residual / Out of scope), consistently with
the matrix.
3. Honesty and overclaiming
- The real-genome studies must be framed as aggregate-only workflow demonstrations
on public reference data — NOT biomedical validation or population-genetics
estimates. - The F_ST-like and LD
r2values must be labeled simplified small-panel contrasts;
the E8r2= 1.0000 pair must be flagged as a small-count artifact; the
below-quorum E8 caveat (25 samples < floor of 30) must be present. - The certificate honesty box must state that the certificate binds artifacts and
policy facts and does NOT by itself prove correct computation, correct decryption,
contributor identity, or bounded output-privacy leakage. - Flag any sentence that claims more than the evidence supports.
4. Citation and cross-reference integrity
- Every reference R1-R37 is both defined and cited, with no gaps or orphans.
- Every “Section N”, “Table N”, and “Figure N” reference resolves to the right
target; tables are numbered 1-16 and figures 1-8; the Appendix A index matches the
body.
5. Reproducibility
- The paper gives a concrete, runnable reproduction path (Appendix B); the commands
exist; the open CLI, open applications, and public data are sufficient to
reproduce every result without the hosted service. Optionally run thereplicate
skill and confirm the numbers.
6. Scope and framing
- The contribution claims (Section 2) are supported and appropriately bounded.
- The threat model’s assumptions are stated.
- The limitations are acknowledged: single keyholder, metadata leakage,
output-privacy as the largest residual risk, and synthetic-plus-small-real
evidence.
Part of the Independently review The Blind Machine paper agent skill.