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/synaptiai/synapti-marketplaceWrote 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/synaptiai/synapti-marketplace/score)<a href="https://agentmods.dev/commands/synaptiai/synapti-marketplace/score"><img src="https://agentmods.dev/badge/commands/synaptiai/synapti-marketplace/score.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 | $0.00023 | $0.00914 |
| Opus 5 | $0.00012 | $0.00457 |
| Sonnet 5 | $0.00005 | $0.00183 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
score 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick NCI Score
Get a rapid manipulation assessment score without full analysis.
Usage
/decipon:score <URL or text content>
Purpose
Get a rapid manipulation assessment when you need:
- Quick triage of content
- Score only, not full analysis
- Fast check before deeper analysis
Workflow
-
Process Input
- Detect if URL, file, or text
- Fetch/read content as needed
-
Quick Category Assessment
- Score all 20 categories (brief evidence)
- Focus on most salient indicators
-
Calculate Score
- Compute composite factors
- Generate overall 0-100 score
-
Output Quick Summary
- Score with severity indicator
- Top 3 findings only
- Recommendation for action
Output Format
═══════════════════════════════════════════════════
NCI QUICK SCORE
═══════════════════════════════════════════════════
SCORE: 72/100 [!!]
Severity: HIGH
Confidence: 78%
TOP FINDINGS:
1. Strong emotional manipulation (urgency, fear)
2. Clear us-vs-them framing
3. Missing alternative perspectives
RECOMMENDATION: Cross-reference with independent sources
Run /decipon:analyze for full report
═══════════════════════════════════════════════════
Severity Scale
| Score | Indicator | Level | Quick Guidance |
|---|---|---|---|
| 0-25 | [·] |
LOW | Normal consumption |
| 26-50 | [!] |
MODERATE | Verify key claims |
| 51-75 | [!!] |
HIGH | Cross-reference sources, strong skepticism |
| 76-100 | [!!!] |
SEVERE | Likely manipulation |
Example Invocations
Quick score of URL:
/decipon:score https://example.com/article
Quick score of text:
/decipon:score Everyone agrees this is the most important issue ever!
When to Use Full Analysis
After quick score, run /decipon:analyze when:
- Score > 40 (upper Moderate or higher)
- Need evidence for the score
- Want dual perspectives
- Need to share/document findings
- Score is borderline (40-55 range)
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 · 145 lines · 23 tokens per session scan A 504729229bcc
score is a command published in the GitHub repository synaptiai/synapti-marketplace (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 914 once invoked, about $0.0001 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 commands, from other repositories
create-worktree
Follow these steps to create a git worktree.
paper-trail-review
Review and arbitrate problematic references (cascade-exhausted, unresolved UID, awaiting OCR). Generates an up-to-date report, walks through cases by category, and applies arbitration decisions to the registry.
paper-trail-decide
Inspect a single reference in detail (identity, PDF status, acquisition history with verdicts, citation context in vault, state transitions) and decide its fate. Offers actionable decisions based on the ref's current state.
paper-trail-new-paper
Start writing an academic paper (IMRaD structure) on a topic, with anti-hallucination citation verification at every step. Builds on existing audited SOTAs.
paper-trail-registry-cleanup
Nettoyage historique du registre des fiches bibliographiques (les 900 fiches existantes). Cible les vieilles fiches mal nommées (0000, untitled) ET les duplicates avec suffixes numériques (foo2020bar2, 234) qui sont des artefacts de runs INGEST passés. Délègue les décisions au sub-agent textbook-resolver. Les merge…
paper-trail-cascade
Acquire PDFs via the 8-source cascade (11 with opt-in extended sources) for a single ref by slug, or a batch filtered by state. Validates page 1 anti-homonymy on each download.