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 skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill intervention-selectorgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote 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/anthonyalcaraz/agentic-graph-rag-skills/intervention-selector)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/intervention-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/intervention-selector/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/skills/anthonyalcaraz/agentic-graph-rag-skills/intervention-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/intervention-selector.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 | $0.00198 | $0.03387 |
| Opus 5 | $0.00099 | $0.01693 |
| Sonnet 5 | $0.00040 | $0.00677 |
| Haiku 4.5 | $0.00020 | $0.00339 |
Grade A, and why
intervention-selector 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 11d 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intervention Selector
Overview
A diagnosis is only valuable if it leads to the right intervention. Different failure types call for different responses. A reasoning failure at a single node calls for a prompt update; a systemic knowledge gap calls for fine-tuning; a format violation calls for an architectural constraint. Applying the wrong fix wastes time at best and makes things worse at worst.
The diagnostic report already contains the failure type and target nodes, so mapping them to an intervention is a straightforward routing function (Ch7 the select_intervention routing example). The chapter Tip is explicit about why this is a function and not a human call: intervention selection should be deterministic and auditable, not a judgment call made differently by each on-call engineer.
The router applies four branches in strict order:
- Insufficient context -> RETRIEVAL_FIX. The context was insufficient, so the fix lives upstream of the model in the retrieval pipeline. Flag the Knowledge Graph or retrieval gap.
- FORMAT_VIOLATION -> STRUCTURAL_CONSTRAINT. The agent had the right knowledge and reasoning but failed to produce a machine-readable output. Attach an output schema to that node so the format error is impossible rather than less likely. Architectural change, not a model change, permanent fix for that component.
- Localized REASONING failure -> PROMPT_REFINEMENT. REASONING failure,
few low-InfoGain steps (
len(low_infogain_steps) <= low_step_max), high knowledge index (knowledge_index > ki_floor). The agent has the capability; steer it at that node. Fast, reversible, low risk, the right first resort. - Everything else -> FINE_TUNE. A systemic knowledge gap, a recurring pattern of the same reasoning failure, or a persistent misalignment. Generate a curriculum via SEAL/TPT and retrain.
The second axis, the self-modification intensity hierarchy, orders the intervention types by cost and risk (Ch7): prompt tuning is the lightest intervention (fast, reversible, low risk), weight adaptation sits in the middle (slower, semi-reversible, moderate risk), and code modification is the heaviest (slowest, requires explicit rollback, highest risk). The router never emits CODE_MODIFICATION; it is the heaviest tier, reserved for explicit code-level self-modification loops (SICA) run in a sandbox with full rollback.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 208 lines · 198 tokens per session scan A ed66865f4d69
intervention-selector is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 198 tokens to every session and 3,387 once invoked, about $0.0010 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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