Borrowing it
Nothing to install: this file belongs to blueman82/ai-counsel. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/blueman82/ai-counsel/main/.claude/skills/decision-graph-analyzer/SKILL.mdgit clone --depth 1 https://github.com/blueman82/ai-counselWrote 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/blueman82/ai-counsel/decision-graph-analyzer)<a href="https://agentmods.dev/skills/blueman82/ai-counsel/decision-graph-analyzer"><img src="https://agentmods.dev/badge/skills/blueman82/ai-counsel/decision-graph-analyzer.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 | $0.00027 | $0.04736 |
| Opus 5 | $0.00014 | $0.02368 |
| Sonnet 5 | $0.00005 | $0.00947 |
| Haiku 4.5 | $0.00003 | $0.00474 |
Grade A, and why
decision-graph-analyzer 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 — 560 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Graph Analyzer Skill
Overview
The decision graph module (decision_graph/) stores completed deliberations and provides semantic similarity-based retrieval for context injection. This skill teaches you how to query, analyze, and troubleshoot the decision graph effectively.
Core Components
Storage Layer (decision_graph/storage.py)
- DecisionGraphStorage: SQLite3 backend with CRUD operations
- Schema:
decision_nodes,participant_stances,decision_similarities - Indexes: Optimized for timestamp (recency), question (duplicates), similarity (retrieval)
- Connection: Use
:memory:for testing, file path for production
Integration Layer (decision_graph/integration.py)
- DecisionGraphIntegration: High-level API facade
- Methods:
store_deliberation(question, result): Save completed deliberationget_context_for_deliberation(question): Retrieve similar past decisionsget_graph_stats(): Get monitoring statisticshealth_check(): Validate database integrity
Retrieval Layer (decision_graph/retrieval.py)
- DecisionRetriever: Finds relevant decisions and formats context
- Key Features:
- Two-tier caching (L1: query results, L2: embeddings)
- Adaptive k (2-5 results based on database size)
- Noise floor filtering (0.40 minimum similarity)
- Tiered formatting (strong/moderate/brief)
Maintenance Layer (decision_graph/maintenance.py)
- DecisionGraphMaintenance: Monitoring and health checks
- Methods:
get_database_stats(): Node/stance/similarity counts, DB sizeanalyze_growth(days): Growth rate and projectionshealth_check(): Validate data integrityestimate_archival_benefit(): Space savings simulation
Common Query Patterns
1. Find Similar Decisions
When: You want to see what past deliberations are related to a new question.
from decision_graph.integration import DecisionGraphIntegration
from decision_graph.storage import DecisionGraphStorage
# Initialize
storage = DecisionGraphStorage("decision_graph.db")
integration = DecisionGraphIntegration(storage)
# Get similar decisions with context
question = "Should we adopt TypeScript for the project?"
context = integration.get_context_for_deliberation(question)
if context:
print("Found relevant past decisions:")
print(context)
else:
print("No similar past decisions found")
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 · 560 lines · 27 tokens per session scan A 382fe70c3748
decision-graph-analyzer is a skill published in the GitHub repository blueman82/ai-counsel (1 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 4,736 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.
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