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 ali-demirbas/claude-lifecycle --skill lifecycle-auditgit clone --depth 1 https://github.com/ali-demirbas/claude-lifecycleWrote 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/ali-demirbas/claude-lifecycle/lifecycle-audit)<a href="https://agentmods.dev/skills/ali-demirbas/claude-lifecycle/lifecycle-audit"><img src="https://agentmods.dev/badge/skills/ali-demirbas/claude-lifecycle/lifecycle-audit.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.02012 |
| Opus 5 | $0.00036 | $0.01006 |
| Sonnet 5 | $0.00015 | $0.00402 |
| Haiku 4.5 | $0.00007 | $0.00201 |
Grade A, and why
lifecycle-audit 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 7d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Audit — Portfolio Review
Score an existing set of journeys against the same rules the engine uses to generate them. Output: an Audit Report with per-journey findings and a portfolio-level verdict.
When NOT to use this
- No journeys exist anywhere yet — nothing described, generated, or imported — there's nothing to score; that's a
lifecycle-connect→lifecycle-journeysconversation instead. - The user wants the findings acted on, not just diagnosed — this skill never rewrites journeys itself (see Never do below); generation is a separate, offered step via
lifecycle-journeys. - Real performance or holdout data exists and the ask is whether a journey is actually working — that's
lifecycle-results, which judges measured incremental outcomes; this skill judges structure and methodology without live results.
Inputs
Journeys as: prior engine output, user description ("we have a welcome email and a cart reminder"), or CRM exports/screenshots. Sparse descriptions are fine — audit what is known, list what could not be assessed. If available, also use the DQS + stage map; without them, stage-coverage and depth-vs-data-fit findings are marked "data-blind" — dimension 3 specifically cannot be judged without knowing what the data supports, so guessing "over-engineered" or "under-leveraged" from journey shape alone is a fabrication risk (CLAUDE.md rule 3), not a finding.
Audit dimensions (score each 0–5, with evidence)
| # | Dimension | What is checked |
|---|---|---|
| 1 | Stage coverage | Each lifecycle stage with events has ≥ 1 journey; blind spots named (uses playbook pattern_priorities as the expectation) |
| 2 | Priority fit | Are the sector's P0 patterns running? A missing P0 (e.g. no abandoned-cart in e-commerce) is automatically a Critical finding |
| 3 | Depth-vs-data fit | Journey complexity matches what the data supports: 10-step branched flows on thin data = over-engineered; 2-step flows on rich data = under-leveraged |
| 4 | Trigger & exit hygiene | Event triggers vs blast schedules; success exits defined; re-entry policies exist |
| 5 | Frequency & conflict | Aggregate worst-case messages/user/week vs caps in knowledge/compliance/consent-and-quiet-hours.md; overlapping triggers |
| 6 | Measurement | Primary KPI + guardrail per journey; holdout existence; primary KPI defined as incremental lift vs holdout, not an attributed number (see knowledge/measurement.md) |
| 7 | Copy compliance (if copy provided) | Spot-check against channel hard rules + lexicon banned lists |
| 8 | Portfolio currency | Has the portfolio changed (journeys added/removed) since the last conflict review? A stale conflict review — cap math not covering the current journey set — is automatically a High finding |
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.
- 7d ago First seen · 77 lines · 73 tokens per session scan A e9364a107e6b
lifecycle-audit is a skill published in the GitHub repository ali-demirbas/claude-lifecycle (2 stars, last pushed 20d ago), licensed MIT. It adds 73 tokens to every session and 2,012 once invoked, about $0.0004 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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