prompt-maintenance

prompt-maintenance is a skill for Claude Code from parisgroup-ai/imersao-ia-setup. It costs 29 tokens per session (1,566 once invoked), scanned A, original, MIT.

A maintenance tool for reusable prompt templates, which are stored instructions used by an application to guide an AI model. It tracks how templates include or depend on one another and checks their structure.

In plain words
What is it for?
Use it to audit prompt files, validate template rules, map dependencies, and regenerate prompt catalogues and documentation.
Why use it?
It helps find broken references, unused templates, documentation gaps, and other problems before prompt changes spread through a project.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the imersao plugin — 51 skills, 7 commands shipped together

Good fit Use it to audit prompt files, validate template rules, map dependencies, and regenerate prompt catalogues and documentation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance
Install

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.

Any agent
npx skills add parisgroup-ai/imersao-ia-setup --skill prompt-maintenance
Clone the repo
git clone --depth 1 https://github.com/parisgroup-ai/imersao-ia-setup

Made for: Claude Code.

Or install imersao, the plugin that ships this one along with the rest of its 51 skills, 7 commands.

Wrote 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.

agentmods badge for prompt-maintenance

README.md
[![agentmods](https://agentmods.dev/badge/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance/github.svg)](https://agentmods.dev/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance)
Your own site
<a href="https://agentmods.dev/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance"><img src="https://agentmods.dev/badge/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance/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.

agentmods 80×15 button for prompt-maintenance

Your own site · 80×15
<a href="https://agentmods.dev/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance"><img src="https://agentmods.dev/badge/skills/parisgroup-ai/imersao-ia-setup/prompt-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.01566
Opus 5 $0.00015 $0.00783
Sonnet 5 $0.00006 $0.00313
Haiku 4.5 $0.00003 $0.00157

Measured 12d ago against content hash f318978781cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

prompt-maintenance 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.

plugins/imersao/skills/prompt-maintenance/SKILL.md · 244 lines

How it starts

The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prompt Maintenance

Manages prompt template health across the project.

Commands

/prompt-maintenance audit

Full analysis of all configured sources.

Process:

  1. Load .prompt-maintenance.json from project root
  2. For each source:
    • Scan files matching pattern
    • Parse headers and extract metadata
    • Build dependency graph from {% include %} statements
    • Scan Python files for direct template references (PromptLoader, get_template)
    • Validate against rules in references/validation-rules.md
  3. Report errors and warnings
  4. Show statistics

Output Format:

prompt-maintenance audit

Scanning apps/ana-service/app/templates/...

✓ 68 templates analyzed
✓ 156 dependencies mapped

ERRORS (N):
  ✗ path/file.jinja2:line - Error description

WARNINGS (N):
  ⚠ path/file.jinja2 - Warning description

Stats:
  By domain: lesson (12), brainstorm (8), _shared (4), ...
  Orphans: 2 templates not referenced

Run '/prompt-maintenance sync' to update catalog.

/prompt-maintenance sync

Regenerate documentation artifacts.

Process:

  1. Run full audit (silent mode)
  2. Generate prompts.json for each source
  3. Generate individual catalog per source
  4. Generate combined catalog.md with Mermaid graph
  5. Report files updated

Output Format:

prompt-maintenance sync

Scanning sources...
✓ ana-service: 68 templates

Generated:
  ✓ apps/ana-service/prompts.json
  ✓ docs/prompts/ana-service.md
  ✓ docs/prompts/catalog.md

Done.

/prompt-maintenance check <file>

Validate single template (for hooks).

Process:

  1. Parse template header
  2. Validate structure (E001-E003)
  3. Check encoding (E004)
  4. Verify includes exist (E005)
  5. Return pass/fail

Output Format (pass):

✓ lesson/exercise.jinja2 - Valid

Output Format (fail):

✗ lesson/exercise.jinja2
  E002: Missing Context field in header
  E005: Include not found: '_shared/missing.jinja2'

Parsing Logic

Header Extraction

def parse_header(content: str) -> dict:
    """Extract metadata from template header."""
    # Find header block
    match = re.search(r'\{#\s*=*\s*(.*?)\s*=*\s*#\}', content, re.DOTALL)
    if not match:
        return None

    header = match.group(1)

    # Extract fields
    result = {
        'path': None,
        'description': None,
        'context': [],
        'output': None,
    }

    # First line is path
    lines = header.strip().split('\n')
    if lines:
        result['path'] = lines[0].strip()

    # Find Context: { ... }
    context_match = re.search(r'Context:\s*\{\s*([^}]+)\s*\}', header)
    if context_match:
        vars = context_match.group(1)
        result['context'] = [v.strip() for v in vars.split(',')]

    # Find Output: ...
    output_match = re.search(r'Output:\s*(.+?)(?:\n|$)', header)
    if output_match:
        result['output'] = output_match.group(1).strip()

    # Description is everything between path and Context/Output
    # (simplified - extract middle lines)

    return result

Read the full file on GitHub · 244 lines

Files

What ships with it

4 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.

Changes

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.

  1. 12d ago First seen · 244 lines · 29 tokens per session scan A f318978781cb

Subscribe to this mod's changes

prompt-maintenance is a skill published in the GitHub repository parisgroup-ai/imersao-ia-setup (1 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,566 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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