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 digital-stoic-org/agent-skills --skill post-mortemgit clone --depth 1 https://github.com/digital-stoic-org/agent-skillsWrote 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/digital-stoic-org/agent-skills/post-mortem)<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/post-mortem"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/post-mortem.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 6 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00142 | $0.01520 |
| Opus 5 | $0.00071 | $0.00760 |
| Sonnet 5 | $0.00028 | $0.00304 |
| Haiku 4.5 | $0.00014 | $0.00152 |
Grade A, and why
post-mortem 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Mortem
Retrospective of a working session — reasoning, decisions, dead-ends, spend — honest, root-caused, quantified, priced, ending in a reusable playbook.
Introspection for the narrative, aggregation for the cost. The payload — "I trusted the fork's summary, it had dropped 2 lines, I lost 15 min, lesson: grep the source first" — lives only in context. A log parser sees Bash ran 110×; it cannot see that 4 of those were the same decoder rewritten. Never mine logs for the why.
Carve-out — cost only. Summing
message.usagecounters from session JSONL and sub-agent.outputfiles is authorized. It is the only route to a[measured]cost: nototal_cost_usdfield exists, cost must be computed. Numbers may be summed, reasoning may not be mined. Recipe, sources and boundary →reference.md§Cost.
Output: post-mortem-YYYYMMDDHHmm.md + a 5-line summary in chat. Always full — every core section, every qualifying session.
Distinct from /save-context (forward-looking resume state) and /instruct-compact (compaction steering). This is a backward-looking debrief for learning.
When to Use
- End of a long (30min+) or multi-phase session, a hard problem solved, or sub-agents/forks were spawned.
- User says: "post-mortem", "retrospective", "session retro", "debrief", "what did we learn", "write up this session".
- Skip the ritual for trivial sessions: if it was a greeting or a one-file fix, say "Session is light — a post-mortem adds little" and write a 3-line note instead of the full ceremony.
Gotchas — the two rules that make or break the report
Non-obvious, and they counter your defaults; violating either makes the report worthless.
- Main-context tokens are not self-observable — unless you have the JSONL. Two regimes, and you must state which one you are in (Annexe provenance notice):
- Live introspection (no JSONL) — accumulation, cache ratios, compaction pre/post are
[estimate ±]+ a one-line basis. Measurable: sub-agent tokens (from the completion notifications) and tool calls you recall. - Third-party post-mortem (JSONL reachable) — main and sub-agents become
[measured]by aggregation. The narrative still comes from context/handoff: never let it pretend to live fidelity on the why.
- Live introspection (no JSONL) — accumulation, cache ratios, compaction pre/post are
What ships with it
1 file 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.
- 7d ago First seen · 72 lines · 142 tokens per session scan A b6865a33ecb6
post-mortem is a skill published in the GitHub repository digital-stoic-org/agent-skills (20 stars, last pushed 4d ago), licensed MIT. It adds 142 tokens to every session and 1,520 once invoked, about $0.0007 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 2026-08-30.
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