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 investigation-dag-plannergit 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/investigation-dag-planner)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/investigation-dag-planner"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/investigation-dag-planner/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/investigation-dag-planner"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/investigation-dag-planner.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 | $0.00192 | $0.01858 |
| Opus 5 | $0.00096 | $0.00929 |
| Sonnet 5 | $0.00038 | $0.00372 |
| Haiku 4.5 | $0.00019 | $0.00186 |
Grade A, and why
investigation-dag-planner 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 12d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigation DAG Planner
Overview
Real investigation rarely follows a linear path. When multiple hypotheses exist, some can be tested in parallel while others have dependencies. Testing whether the database is overloaded and whether the payment service has memory pressure use different data sources — they can run simultaneously. But testing for a network partition between two services only makes sense after ruling out simpler explanations.
This is the dynamic-DAG-construction pattern (Example 5-15) applied to incident diagnosis. The planner:
- Analyzes dependencies between hypotheses/tasks.
- Identifies groups that can safely run in parallel (a topological level —
the same computation
bd readyperforms in Beads, andparallel_groupsin Example 5-15). - Organizes them into phases. Within each phase, tasks are ordered by priority (the historically/structurally most-likely hypothesis first).
- Estimates each parallel phase's duration as the max of its concurrent tests, since they run concurrently — not the sum.
Execution then proceeds phase by phase with early termination: as soon as a
hypothesis is confirmed with sufficient corroborating evidence, remaining
phases are skipped. A dependency cycle raises CycleError — that is a malformed
plan, surfaced rather than silently mis-executed.
In the DevOps latency investigation (account 123456789012), the checkout
latency spike yields three hypotheses. The DAG groups db_pool_exhaustion and
payment_memory_pressure into one parallel phase (different data sources);
network_partition depends on ruling out the pool hypothesis, so it lands in a
later phase. When the pool hypothesis confirms in the first phase, early
termination skips the network-partition test entirely.
When to Use
- A planning node must decide which tasks are parallel-safe and order phases
- Incident-investigation hypothesis testing, multi-track research, multi-party claim processing (the chapter's multi-vehicle-accident example)
- You want an explicit duration estimate and an auditable phase structure
What ships with it
3 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.
- 12d ago First seen · 143 lines · 192 tokens per session scan A f05260f3ce2c
investigation-dag-planner is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 192 tokens to every session and 1,858 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.
Other skills, from other repositories
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
jurisd-research
Expert Australian/NZ legal research and AGLC4 citation using the jurisd MCP server. Use when finding cases or legislation (AustLII), looking up a provision offline, formatting or resolving citations, building a pinpoint, tracing who-cites-what, or producing an AGLC4 bibliography. Triggers on case law, legislation…
repo_search
Search repository text through a deterministic first-class fak command.
container-manager-kg-ingestion
Snapshot a host's Docker/Podman/Swarm inventory into the epistemic-graph knowledge graph as typed OWL nodes via the container-manager-mcp MCP server — containers, images, volumes, networks, swarm services and nodes, with their :usesImage / :runsOn / :builtFrom links. Use when the agent must record live container state…
cortex-design
Use this skill to generate well-branded interfaces and assets for Cortex, either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for prototyping.