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 parisgroup-ai/imersao-ia-setup --skill prompt-maintenancegit clone --depth 1 https://github.com/parisgroup-ai/imersao-ia-setupWrote 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/parisgroup-ai/imersao-ia-setup/prompt-maintenance)<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.
<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>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 | $0.00029 | $0.01566 |
| Opus 5 | $0.00015 | $0.00783 |
| Sonnet 5 | $0.00006 | $0.00313 |
| Haiku 4.5 | $0.00003 | $0.00157 |
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.
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:
- Load
.prompt-maintenance.jsonfrom project root - 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
- Report errors and warnings
- 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:
- Run full audit (silent mode)
- Generate
prompts.jsonfor each source - Generate individual catalog per source
- Generate combined
catalog.mdwith Mermaid graph - 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:
- Parse template header
- Validate structure (E001-E003)
- Check encoding (E004)
- Verify includes exist (E005)
- 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
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.
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 · 244 lines · 29 tokens per session scan A f318978781cb
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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