Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Nisus74/humanisenpx agentmods add commands/nisus74/humanise/improveWrote 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/commands/nisus74/humanise/improve)<a href="https://agentmods.dev/commands/nisus74/humanise/improve"><img src="https://agentmods.dev/badge/commands/nisus74/humanise/improve/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/commands/nisus74/humanise/improve"><img src="https://agentmods.dev/badge/commands/nisus74/humanise/improve.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 | $0.00000 | $0.01231 |
| Opus 5.5 | $0.00000 | $0.00492 |
| Sonnet 5.5 | $0.00000 | $0.00246 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
improve 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 1mo 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/humanise improve
Run one full cycle of the self-improvement loop: benchmark the skill's output, run the blind voice test, mine the accumulated evidence for weaknesses, and gate any proposed engine change. The loop's rules live in evals/self-harness-loop.md; this command executes them. Every stage is skippable, but say which stage was skipped and why in the final report.
Artefacts for the run land in profile/learning/runs/<UTC timestamp>/ (the soul; gitignored, never ships). Flags: --baseline also generates untreated comparison drafts; --skip-benchmark, --skip-indist to run a partial cycle.
a. Preflight
- Held-in must be green before anything:
python3 evals/assertions/selftest.py. Red means fix the engine first; an improvement cycle on a broken engine mines noise. python3 scripts/build_voiceprint.py --status --json. No profile: offer/humanise initand stop. Stale voiceprint: rebuild it (humanise voiceprint --build). Note which channels are eligible for the pairwise test.- Create the run directory:
profile/learning/runs/<timestamp>/.
b. Benchmark (rule compliance, with_skill vs baseline)
For each fixture in evals/evals.json, spawn an agents/eval-generator.md subagent in mode: skill, passing only the fixture's prompt, channel, audience_tag, and medium (never the assertions), with output path <run>/with_skill/eval-<id>.md. With --baseline, spawn a second generator per fixture in mode: baseline writing to <run>/baseline/eval-<id>.md.
Grade each directory: python3 evals/assertions/run_all.py --outputs <dir>. Read the exit codes correctly: baseline red is expected (the baseline exists to lose); with_skill hard failures are not a stop, they are mining signal for stage d. Summarise the delta (pass rates, which channels leak). Note in the report that an in-agent baseline is directional rather than a controlled experiment, and never gates anything.
c. Indistinguishability (the real voice gate)
For each eligible channel from preflight (at least 2 usable samples):
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.
- 1mo ago First seen · 56 lines · 0 tokens per session scan A ca291c0d6787
improve is a command published in the GitHub repository Nisus74/humanise (1 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,231 tokens. 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 2026-08-31.
Other commands, from other repositories
ijfw-audit
Run the IJFW audit gate for the current workflow phase. Usage: /ijfw-audit [phase name].
api-aqa-flow-execution-and-report-analysis
Phase 6 Execution & Report Analysis of api-aqa-flow (USER INTERACTION REQUIRED).
paul:verify
Guide manual user acceptance testing of recently built features.
brief-compliance-check
Check a LaTeX coursework submission against the requirements in a supplied PDF assessment brief. Use when verifying format, required sections, word limits, or deliverables before submission. Not for general prose proofreading; use $proofread.
playwright-execute
Run Playwright tests or suites and upload the resulting report to Katalon True Platform. Use when you need to execute Playwright scripts, package scripts, spec files, projects, or suites, configure or verify @katalon/playwright-reporter, upload Playwright reports with Katalon CLI/reporter commands, and verify uploaded…
test-maintenance
Maintain and evolve a Katalon True Platform/TestOps regression suite as the application changes. Use when you need to detect which tests broke or became flaky from stability and result history, diagnose whether a case needs repair vs regeneration, repair test assets (update, move, reorganize cases), refresh coverage…