Skills API

Skills are reusable bundles of system prompt, tool filter, and trigger conditions. They let you package domain expertise (code review, translation, summarization, RAG) into a single file that PAR auto-discovers and activates when relevant.

Status: v0.5.2-beta. API may change before v1.0.

---

What is a skill?

PAR has four abstraction layers. Skills sit alongside tools, agents, and middleware:

Layer	What it packages	Example
Tool	A single function callable by the LLM	read_file, web_search, bash
Skill	System prompt + tool subset + trigger conditions	"Code reviewer" (review prompt + read/grep tools only)
Agent	Full config: model, tools, middleware, retry policy	Default agent with GPT-4 + 20 tools
Middleware	Cross-cutting hook (logging, retry, rate limit)	Retry with exponential backoff

When to create a skill vs an agent: - Skill: You want to switch behavior within a conversation without restarting. The agent stays the same; the skill overlays its prompt and tool filter. - Agent: You need a fundamentally different model, tool set, or middleware chain.

---

Quick start

Create a skill in 30 seconds:

mkdir -p ~/.par/skills/greeter
cat > ~/.par/skills/greeter/skill.md << 'EOF'
---
schema_version: 1
id: greeter
name: Greeter
description: Greet users warmly. Use when the conversation starts or the user says hello.
system_prompt_override: "You are an enthusiastic greeter. Always start with a warm welcome."
tool_filter: All
trigger: Keyword [hello, hi, hey]
---

# Greeter Skill

When activated, greet the user warmly and ask how you can help.
Use their name if provided in the conversation context.
EOF

Now start PAR and check:

par
> /skills
  greeter              Greet users warmly. Use when the conversation starts...
> /skill greeter
ID:          greeter
Name:        Greeter
Description: Greet users warmly. Use when the conversation starts or the user says hello.
Trigger:     keyword

The skill is auto-discovered and registered. When the user types "hello", the keyword trigger activates the skill.

---

File format

Each skill lives in its own directory: ~/.par/skills/<id>/skill.md.

Directory structure

~/.par/skills/greeter/
└── skill.md          ← YAML frontmatter + markdown body

YAML frontmatter reference

Field	Type	Required	Default	Description
schema_version	int	yes	—	Must be 1 for v0.5.2. Loader rejects unknown versions.
id	string	yes	—	Lowercase-hyphen identifier. Must match directory name.
name	string	yes	—	Display name.
description	string	yes	—	What the skill does + when to use it. Max 1024 chars. Always loaded (L1 metadata).
system_prompt_override	string | null	no	null	Replaces the agent's system prompt when skill activates.
tool_filter	All | Only [...] | Except [...]	no	All	Restricts which tools the LLM can use.
trigger	Auto | Manual | Keyword [...]	no	Auto	When the skill gets activated.
expected_output	JSON | null	no	null	Forward-looking: typed success criteria (informational in v0.5.2, LLM judge planned for v0.5.3+).

Markdown body

The body after the second --- is the skill's instructions. It's lazy-loaded (L2) — only read into memory when the skill activates, not at startup. This keeps startup fast even with 50+ skills installed.

---

Trigger types

The trigger field controls when a skill becomes active:

Auto (default)

The skill description is loaded into the LLM's system prompt. The LLM itself decides whether to activate the skill based on the user's message and the description text.

trigger: Auto

Best for: skills that should be "always available" to the LLM (summarizer, RAG assistant).

Manual

The skill is never auto-activated. Users must explicitly invoke it:

par skill use my-skill

Best for: skills that should only fire on explicit request (dangerous operations, specialized workflows).

Keyword

A fast substring pre-filter runs before LLM judgment. The skill is only considered if one of the keywords appears in the user's message:

trigger: Keyword [pdf, form, document] confirm

• confirm (default): after keyword match, LLM still judges whether to activate
• deterministic: keyword match immediately activates, no LLM judgment needed

trigger: Keyword [pdf, form] deterministic

Best for: skills with clear domain keywords (PDF extractor, translator for specific languages).

Token budget

To prevent system prompt explosion, PAR caps total skill description tokens at 2048 (configurable via runtime config skill_token_budget). When the budget is exceeded, lowest-priority skill descriptions are dropped with a warning.

---

Tool filter

Restricts which tools the LLM can access when the skill is active:

tool_filter: All                    # all registered tools (default)
tool_filter: Only [read_file, grep] # only these tools
tool_filter: Except [bash]          # all tools except these

When multiple skills are active simultaneously, filters compose by intersection (most restrictive wins). This ensures no skill can widen another skill's restriction.

---

Discovery

PAR discovers skills from three sources, in this precedence order (first match wins):

1. SDK-registered (highest): Runtime.register_skill(descriptor) — explicit, programmatic
2. Project: ./.par/skills/<id>/skill.md — checked into the repo
3. User: ~/.par/skills/<id>/skill.md — user-wide defaults
4. Builtin (lowest): 4 starter skills shipped with PAR

Hot-reload: On each Runtime.invoke, PAR checks if any skills directory's mtime changed. If so, it rescans automatically. You can also force a rescan:

par skill reload

---

CLI usage

REPL commands

Command	Description
/skills	List all registered skills with description preview
/skill <id>	Show full detail of a specific skill

Standalone commands

par skill list                    # list skills (non-interactive)
par skill show <id>               # show skill detail
par skill reload                  # force filesystem rescan

---

SDK API

Python

from par_runtime import Runtime
import json

rt = Runtime(config_json)

# Register a skill programmatically
rt.register_skill(json.dumps({
    "schema_version": 1,
    "id": "my-skill",
    "name": "My Skill",
    "description": "Does something useful.",
    "system_prompt_override": "You are a specialist.",
    "tool_filter": "Only [read_file]",
    "trigger": "Auto"
}))

# List all registered skills
skills = rt.list_skills()
for s in skills:
    print(f"  {s['id']}: {s['description']}")

OCaml

(* Create a skill descriptor *)
let descriptor =
  Par.Runtime.make_skill
    ~id:"my-skill"
    ~description:"Does something useful."
    ~system_prompt_override:"You are a specialist."
    ~tool_filter:(Par.Types.Only ["read_file"])
    ~trigger:Par.Types.Auto
    ()
  |> Result.get_ok

(* Register it *)
let _ = Par.Runtime.register_skill rt descriptor

(* List all skills *)
let skills = Par.Runtime.list_skills rt
List.iter (fun s -> Printf.printf "  %s: %s\n" s.Par.Types.id s.Par.Types.description) skills

---

Builtin skills

PAR ships with 4 starter skills. Override them by creating a skill with the same id in ~/.par/skills/ or ./.par/skills/:

ID	Trigger	Tools	Description
code-reviewer	Keyword: review, audit	read_file, grep, glob	Reviews code for bugs, security, style
summarizer	Auto	All	Summarizes long text into key points
translator	Keyword: translate, 翻译	All	Translates between languages
rag-assistant	Auto	add_documents, invoke_with_rag	Answers using retrieved document context

---

Examples

Example 1: Custom skill — Python data analyst

mkdir -p ~/.par/skills/python-analyst
cat > ~/.par/skills/python-analyst/skill.md << 'EOF'
---
schema_version: 1
id: python-analyst
name: Python Data Analyst
description: Analyze Python code for data science patterns. Use when the user asks about pandas, numpy, or data analysis code.
system_prompt_override: "You are a Python data science expert. Focus on pandas/numpy patterns, performance, and correctness."
tool_filter: Only [read_file, grep, glob]
trigger: Keyword [pandas, numpy, dataframe, data analysis]
---

# Python Data Analyst

When analyzing Python code:
1. Check for common pandas anti-patterns (chained indexing, in-place modification)
2. Verify numpy vectorization opportunities
3. Suggest type annotations for data science functions
4. Flag potential memory issues with large DataFrames
EOF

Example 2: Override a builtin

To customize the summarizer skill, create a project-level skill that overrides the builtin:

mkdir -p .par/skills/summarizer
cat > .par/skills/summarizer/skill.md << 'EOF'
---
schema_version: 1
id: summarizer
name: Tech Summarizer
description: Summarize technical documents. Use when the user asks for a summary or TL;DR.
system_prompt_override: "You are a technical writer. Summarize with structured headers, preserve all technical terms, and include a one-sentence TL;DR at the top."
tool_filter: All
trigger: Auto
---

# Tech Summarizer

Format summaries as:
## TL;DR
[one sentence]

## Key Points
- bullet points

## Technical Details
[code blocks, configs, commands]

Because this is in ./.par/skills/ (project), it takes precedence over the builtin summarizer (which is lowest priority).

Example 3: Deterministic keyword skill

A skill that activates immediately on keyword match, without LLM judgment:

mkdir -p ~/.par/skills/log-parser
cat > ~/.par/skills/log-parser/skill.md << 'EOF'
---
schema_version: 1
id: log-parser
name: Log Parser
description: Parse and analyze log files. Use when the user mentions logs, stack traces, or error output.
system_prompt_override: "You are a log analysis expert. Extract timestamps, error codes, and stack traces. Correlate events across log lines."
tool_filter: Only [read_file, grep, bash]
trigger: Keyword [log, stack trace, error output, traceback] deterministic
---

# Log Parser

1. Identify the log format (syslog, JSON, plain text)
2. Extract all ERROR/WARN lines
3. Find the root cause (first error in the chain)
4. Suggest fixes based on the error pattern

The deterministic flag means: if the user's message contains "log", "stack trace", "error output", or "traceback", this skill activates immediately — no LLM round-trip needed.

---

Tiered context loading

PAR uses a 2-level context loading model inspired by Anthropic Agent Skills:

Level	What	When loaded	Token cost
L1	id, name, description (frontmatter only)	At startup, always in memory	~100 tokens per skill
L2	Markdown body (instructions)	Only when skill activates	Variable (skill-specific)

This means you can install 50+ skills without bloating every LLM call — only the L1 metadata is always resident. The L2 body is loaded on-demand when a skill triggers.

---

Migration and versioning

The schema_version field (currently 1) lets PAR evolve the skill format without breaking existing skills:

• Unknown versions are rejected at load time with a clear error: "skill targets schema_version 2, current is 1. See MIGRATION.md."
• Old versions may be auto-migrated (future) or rejected with upgrade instructions.

When PAR adds new frontmatter fields in v0.6+, skills with schema_version: 1 will still work — new fields default to sensible values.

---

See also

• Agent API — agents, Runtime.invoke, tool handlers
• Tools — 20 builtin tools, custom registration
• RAG API — embeddings, vector store, retrieval (used by rag-assistant skill)
• Architecture — how PAR works internally