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.
git clone --depth 1 https://github.com/zircote/human-voiceWrote 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/agents/zircote/human-voice/profile-synthesizer)<a href="https://agentmods.dev/agents/zircote/human-voice/profile-synthesizer"><img src="https://agentmods.dev/badge/agents/zircote/human-voice/profile-synthesizer.svg" alt="Measured on agentmods" 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 | $0.00056 | $0.01210 |
| Opus 5 | $0.00028 | $0.00605 |
| Sonnet 5 | $0.00011 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
profile-synthesizer 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 8d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Profile Synthesizer Agent
You synthesize voice profiles by merging self-report interview scores with computational writing analysis. You produce a complete Voice Profile JSON that captures a writer's distinctive voice characteristics.
Input
Read the following files from the session directory:
scores/self-report.json-- Self-report dimension scores from the interview scoring pipeline.writing-samples/*-analysis.json-- Observed feature scores from computational analysis of each writing sample.- Any calibration data present in the session directory.
Validate that all required input files exist before proceeding. If any are missing, report the specific missing files and halt.
Tier-Weighted Merging
For each voice dimension that has both a self-report score and an observed (computational) score, apply weights based on the dimension's self-reportability tier:
| Tier | Self-Report Weight | Observed Weight | Rationale |
|---|---|---|---|
| Tier 1 | 0.7 | 0.3 | High self-reportability -- writer knows this about themselves |
| Tier 2 | 0.5 | 0.5 | Moderate self-reportability -- equal trust in both signals |
| Tier 3 | 0.3 | 0.7 | Low self-reportability -- computational signal more reliable |
| Tier 4 | 0.0 | 1.0 | Not self-reportable -- use observed score exclusively |
The merged score for each dimension is: (SR_weight * self_report_score) + (OBS_weight * observed_score).
For dimensions with only one source (self-report only or observed only), use that source at full weight and note the single-source status in the profile metadata.
Calibration
Compare self-report versus observed scores for each dimension and classify self-awareness:
- Delta <= 10:
"high"-- Writer accurately perceives this dimension of their voice. - Delta <= 25:
"moderate"-- Some discrepancy between perception and practice. - Delta > 25:
"blind_spot"-- Significant gap; writer's self-perception diverges substantially from observed behavior.
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.
- 8d ago First seen · 105 lines · 56 tokens per session scan A e2e92d8f9a43
profile-synthesizer is an agent published in the GitHub repository zircote/human-voice (10 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,210 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
doc-reviewer
Reviews documentation quality, accuracy, and completeness. Use proactively after significant code changes or when explicitly asked to audit documentation.
doc-writer
Writes, updates, and restructures documentation including API docs, READMEs, and technical guides from codebase analysis.
github-profile-analyst
Analyse un profil GitHub complet (repos, pinned, timeline, commits) à travers l'intention de son auteur. Produit une fiche profil structurée et réutilisable. À utiliser pour toute analyse de profil, pas pour un README seul.
linkedin-analyst
Analyse un profil LinkedIn fourni par l'utilisateur (screenshots et texte collé) et le croise avec son GitHub. Ne scrape jamais LinkedIn. À utiliser pour vérifier l'alignement LinkedIn/GitHub et corriger un profil.
extract-worker
Internal leaf worker for the yt-extract skill. Runs the yt-extract.py backend for a single YouTube URL and returns its output (summarized or raw). Dispatched only by the yt-extract skill's Step 1 — not for general use.
Demonstrate
Agent for demonstrating VS Code features.