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pi-agent

Builds with and operates Pi, the minimal terminal coding harness. Use for installing Pi, configuring providers/models/settings/environment variables, creating Pi skills/extensions/packages/themes/prompt templates, embedding Pi through the SDK, integrating over RPC or JSON event streams, parsing sessions, running local models through the llama.cpp router, developing custom Pi providers and TUI components, or using ecosystem packages such as pi-subagents (delegation/orchestration), pi-mcp-adapter (MCP servers), pi-interview (interactive forms), and pi-web-access (web search, fetching, video understanding).

77

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-architected routing skill: the body is a decision table over a verified, complete one-level-deep reference bundle, with concrete commands and genuinely non-obvious operational defaults (RPC lifecycle semantics, safety posture). The only weakness is the Source Coverage section's placement of version/date metadata outside a dedicated compatibility-style heading, which costs it conciseness points under the rubric's time-sensitive-information rule.

Suggestions

Condense the Source Coverage section: keep the pinned baseline in one or two lines under an explicitly labeled 'Version baseline' heading and drop the redundant sentence "These releases are the compatibility baseline, not interchangeable historical APIs" plus the release-specific change list, which restates what the version pins already imply.

Trim the citation section's arXiv fetch procedure to the essential rule (cite the current version via the DOI) — the conditional journal-reference and export-api details can live in a reference file if needed.

The RPC lifecycle paragraph mixes build-time defaults with protocol semantics; moving the correlation/settled-event details fully into references/rpc.md and keeping only the one-line "correlate by id, not arrival order" summary here would sharpen the overview's token budget.

DimensionReasoningScore

Conciseness

The body is largely lean — a routing table, terse defaults with specific flags ("pi --mode rpc --no-session"), and non-obvious knowledge Claude lacks ("Split records on \n only — Node readline is not protocol-compliant") — but the "Source Coverage" section embeds time-sensitive version numbers and dates ("0.99.2 release (2026-09-30)", "pi-subagents 0.74.0") outside a deprecation/old-patterns section, which the rubric explicitly penalizes, and the citation section's arXiv fetch procedure could be trimmed. This matches the 4 anchor (efficient with minor trimmable instances) rather than 5, and is well above 3 since no space is spent on concepts Claude already knows.

4 / 5

Actionability

Fully executable guidance throughout: copy-paste commands ("npm install -g --ignore-scripts @earendil-works/pi-coding-agent", "pi install npm:pi-subagents", "pi --mode json \"List files\""), named SDK entry points ("createAgentSession()", "createAgentSessionRuntime()", "ModelRuntime.create()"), and an intent-to-file routing table. It matches the 5 anchor (copy-paste ready commands covering the common cases); there are no gaps that would drop it to 4.

5 / 5

Workflow Clarity

The primary workflow — "Pick the reference before answering or coding" — is an unambiguous decision table, and the Source Coverage section adds an explicit pre-flight checkpoint ("Check pi --version, pi --help, the installed package README, and TypeScript declarations before adapting examples to another release"). No destructive or batch operations are involved, so the validation cap does not apply, and the single routing action is unambiguous — matching the rubric's simple-skill exception for a 5.

5 / 5

Progressive Disclosure

A clear overview with a complete intent-to-file routing table where all 35 cited reference files exist in references/ (verified against the actual bundle) and are one level deep — spot-checks show substantive content (e.g., quickstart.md's single pointer to providers.md is normal navigation, not a pass-through chain). Nothing that belongs in a reference file is inlined, matching the 5 anchor rather than 4 ('minor organization gaps').

5 / 5

Total

19

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: it states what the skill does and when to use it with concrete, enumerated trigger scenarios, all anchored to a distinct product niche. Its only cost is density — it is a long single sentence — but every clause names a distinct capability rather than padding, so it stays within the rubric's tolerance.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions with comprehensive coverage: "installing Pi", "configuring providers/models/settings/environment variables", "embedding Pi through the SDK", "integrating over RPC or JSON event streams", "running local models through the llama.cpp router", "developing custom Pi providers and TUI components", each tied to named ecosystem packages. It exceeds the 4 anchor ('minor gaps in coverage') because every major capability surface — install, config, authoring, embedding, integration, session parsing, local models, custom development, ecosystem packages — is explicitly enumerated.

5 / 5

Completeness

Both 'what' and 'when' are explicit: what — "Builds with and operates Pi, the minimal terminal coding harness" with enumerated capabilities; when — an explicit "Use for installing Pi, ..." clause listing concrete trigger scenarios. This parallels the rubric's 5-anchor example exactly; the 'when' is explicit and enumerated, not merely present, so it is not a 4.

5 / 5

Trigger Term Quality

Natural trigger terms are comprehensively covered: "installing Pi", "providers", "models", "settings", "environment variables", "SDK", "RPC", "llama.cpp", "MCP servers", "web search", plus the exact ecosystem package names ("pi-subagents", "pi-mcp-adapter", "pi-interview", "pi-web-access") a user would naturally mention. It matches the 5 anchor (comprehensive coverage including synonyms) rather than 4 ('a few natural terms missing') because no commonly used trigger for this domain is absent.

5 / 5

Distinctiveness Conflict Risk

Clear niche with distinct triggers: every capability is anchored to Pi or its named packages ("pi-subagents (delegation/orchestration)", "pi-mcp-adapter (MCP servers)"), so generic overlap terms like "MCP servers" and "web search" are qualified by their Pi-specific context. Conflict risk is minimal, matching the 5 anchor rather than 4 ('minor overlap risk with closely related skills').

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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