Borrowing it
Nothing to install: this file belongs to loerei/chronicle-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/loerei/chronicle-mcp/main/.agents/skills/afterplay/SKILL.mdgit clone --depth 1 https://github.com/loerei/chronicle-mcpWrote 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/loerei/chronicle-mcp/afterplay)<a href="https://agentmods.dev/skills/loerei/chronicle-mcp/afterplay"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/afterplay/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/loerei/chronicle-mcp/afterplay"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/afterplay.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 | $0.00046 | $0.02669 |
| Opus 5 | $0.00023 | $0.01334 |
| Sonnet 5 | $0.00009 | $0.00534 |
| Haiku 4.5 | $0.00005 | $0.00267 |
Grade A, and why
afterplay 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 9d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Afterplay: Post-Prototype Distillation & Diff Audit
Use Afterplay when a prototype branch achieves a critical performance win or complex goal (the Goal), but the codebase has become dirty, unmaintainable, or contains subtle bugs.
Afterplay provides a disciplined 6-phase pipeline to freeze reference baselines, reconstruct clean target branches, isolate bugs, extract minimal clean abstractions, run multi-subagent diff audits, and cast confidence votes on every modified file using an extended 6-tier bug & goal-relevance taxonomy (Type 0, Type 1, Type 2, Type 3, Type U, Type 2U).
Workflows
flowchart TD
Start["Dirty Prototype with Performance/Goal Win"] --> CheckPR{"!GPR<PR_ID/URL> Tag Supplied?"}
CheckPR -->|"Yes"| FetchPR["Run scripts/get-pr-description.js<br/>Save to <appDataDir>/brain/<id>/PR.md<br/>Set Goal = PR.md"]
CheckPR -->|"No"| Phase1["1. Freeze Reference Baseline"]
FetchPR --> Phase1
Phase1 --> Phase2["2. Reconstruct Clean Target Branch<br/>(origin/<target-base-branch>)"]
Phase2 --> Phase3{"3. Isolate Bug Origin by Discarding Dirty Code of Prototype Branch<br/>(!SC<A|B> Override)"}
Phase3 -->|"Scenario A (Dirty Code Bug Disappears)"| Verify["Verify Build & Test Execution"]
Phase3 -->|"Scenario B (Goal Code Bug Persists)"| Phase4["4. Extract Minimal Implementation<br/>(Atomic Commits: feat vs test)"]
Phase4 --> Phase5["5. Per-File Diff & Multi-Subagent Audit<br/>(Supply PR.md as Goal Context to Subagents)<br/>[!HU Fast Bloat Hunt Mode Option]"]
Phase5 --> Phase6["6. Confidence Voting & Bug Taxonomy"]
Phase6 --> CheckCategory{"Check Subagent Taxonomy Classification"}
CheckCategory -->|"Type U / Type 2U (Unrelated to Goal)"| StripCode["Filter & Discard Non-Goal Code<br/>(Do NOT spend time fixing Type 2U!)"]
CheckCategory -->|"Type 0 (Clean Goal Code)"| KeepCode["Keep Clean Goal Code"]
CheckCategory -->|"Type 1 / Type 2 / Type 3 (Goal-Relevant Bug)"| SurgicalFix["Identify Single-Point Surgical Fix<br/>(Minimal Code Edit / Implementation)"]
StripCode --> Verify
KeepCode --> Verify
SurgicalFix --> Verify
Verify --> Done["Clean Production-Ready PR"]
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.
- 9d ago First seen · 145 lines · 46 tokens per session scan A 3204f2959b0a
afterplay is a skill published in the GitHub repository loerei/chronicle-mcp (0 stars, last pushed 5d ago), licensed MIT. It adds 46 tokens to every session and 2,669 once invoked, about $0.0002 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.
Other skills, from other repositories
adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
gsd-ns-review
Route to the appropriate quality / review skill based on the user's intent. gsd-code-review-fix was absorbed by gsd-code-review --fix in #2790.
issue
Use when starting a chain from a GitHub issue — turning an issue URL or number into a triaged, planned, dispatched, and reviewed pull request. Classifies the thread (bug → root-cause discipline, feature → plan chain, question → drafted reply), synthesizes a spec from the issue's own acceptance criteria, then runs the…
gitnexus
A code-graph analysis add-on for examining an existing codebase, including symbols, call paths, execution flows, and effects across repositories. It can query GitNexus through its command-line or MCP interfaces.
cleanup-code-inspections
Reduce technical debt and improve code quality by systematically resolving static analysis warnings.
dorodango
Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.