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.
npx agentmods add skills/existential-birds/beagle/adr-decision-extractionnpx skills add existential-birds/beagle --skill adr-decision-extractiongit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/skills/existential-birds/beagle/adr-decision-extraction)<a href="https://agentmods.dev/skills/existential-birds/beagle/adr-decision-extraction"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/adr-decision-extraction.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 | $0.00113 | $0.01285 |
| Opus 5 | $0.00056 | $0.00642 |
| Sonnet 5 | $0.00023 | $0.00257 |
| Haiku 4.5 | $0.00011 | $0.00128 |
Grade A, and why
adr-decision-extraction 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 6d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Decision Extraction
Extract architectural decisions from conversation context for ADR generation.
Detection Signals
| Signal Type | Examples |
|---|---|
| Explicit markers | [ADR], "decided:", "the decision is" |
| Choice patterns | "let's go with X", "we'll use Y", "choosing Z" |
| Trade-off discussions | "X vs Y", "pros/cons", "considering alternatives" |
| Problem-solution pairs | "the problem is... so we'll..." |
Extraction Rules
Explicit Tags (Guaranteed Inclusion)
Text marked with [ADR] is always extracted:
[ADR] Using PostgreSQL for user data storage due to ACID requirements
These receive confidence: "high" automatically.
AI-Detected Decisions
Patterns detected without explicit tags require confidence assessment:
| Confidence | Criteria |
|---|---|
| high | Clear statement of choice with rationale |
| medium | Implied decision from action taken |
| low | Contextual inference, may need verification |
Output Format
{
"decisions": [
{
"title": "Use PostgreSQL for user data",
"problem": "Need ACID transactions for financial records",
"chosen_option": "PostgreSQL",
"alternatives_discussed": ["MongoDB", "SQLite"],
"drivers": ["ACID compliance", "team familiarity"],
"confidence": "high",
"source_context": "Discussion about database selection in planning phase"
}
]
}
Field Definitions
| Field | Required | Description |
|---|---|---|
title |
Yes | Concise decision summary |
problem |
Yes | Problem or context driving the decision |
chosen_option |
Yes | The selected solution or approach |
alternatives_discussed |
No | Other options mentioned (empty array if none) |
drivers |
No | Factors influencing the decision |
confidence |
Yes | high, medium, or low |
source_context |
No | Brief description of where decision appeared |
Extraction Workflow
- Scan for explicit markers - Find all
[ADR]tagged content - Identify choice patterns - Look for decision language
- Extract trade-off discussions - Capture alternatives and reasoning
- Assess confidence - Rate each non-explicit decision
- Capture context - Note surrounding discussion for ADR writer
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.
- 6d ago First seen · 163 lines · 113 tokens per session scan A 794f37c52db0
adr-decision-extraction is a skill published in the GitHub repository existential-birds/beagle (80 stars, last pushed 27d ago), licensed Apache-2.0. It adds 113 tokens to every session and 1,285 once invoked, about $0.0006 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 2026-08-30.
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