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 NVZver/claude-marketplace --skill reconcilegit clone --depth 1 https://github.com/NVZver/claude-marketplaceWrote 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/nvzver/claude-marketplace/reconcile)<a href="https://agentmods.dev/skills/nvzver/claude-marketplace/reconcile"><img src="https://agentmods.dev/badge/skills/nvzver/claude-marketplace/reconcile/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/nvzver/claude-marketplace/reconcile"><img src="https://agentmods.dev/badge/skills/nvzver/claude-marketplace/reconcile.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 | $0.00050 | $0.02417 |
| Opus 5 | $0.00025 | $0.01208 |
| Sonnet 5 | $0.00010 | $0.00483 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade A, and why
reconcile 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 10d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trace. On load, print first:
=============== [lsa/skills/reconcile/SKILL.md] [lsa] ===============
LSA Reconcile (after — correctness)
See CORE.md §6 (the two checks). This is the after check; verify is the before check.
Role
Result verifier + spec maintainer.
Goal
Confirm the returned diff satisfies the spec; where reality diverged, the spec absorbs it.
Inputs
| Input | Source |
|---|---|
| The implementer's diff | delegate |
The spec + <flow>.feature files |
specify |
| Quality-gate checks (required input) | .lsa.yaml gate: — see knowledge/quality-gate-contract.md. No gate: block → report the gate status NOT-RUNNABLE explicitly (Step 1); never silently skip. |
| Scenario-run count N | .lsa.yaml reconcile.runs — default 3 when the key (or the file) is absent |
Steps
Three questions — does · only · all:
- Does it work — run each Gherkin scenario against the diff N times, where N =
.lsa.yamlreconcile.runs, default 3 when the key is absent (agents are stochastic); pass = succeeds on ≥95% of runs. At the default N = 3, ≥95% means all 3 runs pass (3/3); a 2/3 scenario fails. Whenreconcile.runsraises N for a high-stakes epic, pass stays ≥95% of runs (e.g. N = 20 → at least 19 passing). Then run the.lsa.yamlgate:block — required input, not an optional extra: run each configured check and cite its command + exit/output as the proof (the Rule 7 gate artifact). Run the block in one pass where the repo provides an aggregate runner (this repo:bash scripts/gate.sh, which reads thegate:block and prints each check's command + exit), and cite its consolidated output; absent a runner, run each configured command. If the repo has nogate:block, report the gate status explicitly asgate: NOT-RUNNABLE — no gate: block in .lsa.yamlinconformance.mdand alongside the verdict — never silently skip the gate step. (→ scenario results + per-check gate results, or an explicitNOT-RUNNABLEgate status) - Only what's needed — every changed hunk traces to a requirement; an untraced hunk is over-delivery. Prove it by reading the Step-4 coverage table in reverse: every diff hunk appears in at least one requirement row; an orphan hunk (in the diff, in no row) is drift. (→ scope check)
- All of the plan — every requirement (F1…, including non-scenario ones) maps to a change in the diff or a covering test; an uncovered requirement is under-delivery. Prove it with the Step-4 coverage table: a requirement row with no implementing hunk and no covering test is a failing row. (→ completeness check)
- First run
bash scripts/coverage-skeleton.sh <feature-dir>to get the enumerated skeleton — it lists every requirement ID as a table row and every changed file as a candidate hunk (spec files under<feature-dir>excluded), the two deterministic axes computed once so you cite them instead of re-enumerating by hand (enumeration is scripted; the does·only·all judgment stays yours —.lsa/VISION.md:67§2 principle 10). Then fill only the semantic mapping column (which hunk satisfies which requirement) and read off orphans / uncovered from the filled table. Writeconformance.mdaround the requirement ↔ hunk coverage table — one row per requirement ID (F1…Fn fromrequirements.md) with four columns: requirement · the diff hunks/files that implement it · the scenario runs that prove it (e.g.3/3) · a per-row verdict. Below the table, write the orphan-hunk line in the canonical, machine-readable form — exactly one line, at column 0, eitherOrphan hunks: none.orOrphan hunks: <integer>(a prose heading such as## Orphan hunks (over-delivery vs …)does NOT satisfy this contract), optionally followed by a prose breakdown on subsequent lines — then the gate results (or the explicitNOT-RUNNABLEstatus, per Step 1). The judge cites this table — the only and all verdicts are read off it, not asserted in prose. Pass → done. Any check fails or the code diverged → present the drift (percore/outputRule 7 Delivery test — never only in a subagent transcript or pre-tool-call text), take approval, and edit the spec in place to match reality. (→ verdict + conformance.md + any spec update) - Metrics emit step — PASS verdicts only. When Step 4 reaches a
reconcile: PASS @ <sha>verdict, runbash scripts/metrics-harvest.sh <feature-dir>/conformance.md, quote its four-line output as the cited source, and append one row to.lsa/metrics.mdusing its existing six-column schema (feature · archived · accuracy (M/N) · Citation resolve-rate (M/N) · only-required-changes (M/N) · notes) — the harvest script'saccuracy-to-taskmaps toaccuracy,citation-resolve-ratetoCitation resolve-rate,only-required-changestoonly-required-changes. On a FAIL verdict, append no row. This step is descriptive only: it never changes the PASS/FAIL verdict, the gate threshold, orreconcile.runssemantics; a non-zero exit or anUNPARSEABLEline frommetrics-harvest.shis recorded verbatim in the row'snotescolumn and never turns a PASS into a FAIL.
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.
- 10d ago First seen · 69 lines · 50 tokens per session scan A 484a4af2fce3
reconcile is a skill published in the GitHub repository NVZver/claude-marketplace (1 stars, last pushed 16d ago), licensed MIT. It adds 50 tokens to every session and 2,417 once invoked, about $0.0003 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
improve-code-quality
Guided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy…
refactoring-patterns
Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", "decompose conditional", or "clean up this messy code". Also trigger when…
team-review
Review changed code for reuse, quality, and efficiency using a team of persistent named reviewers. This skill should be used when the user says "team review", "review with team", or wants parallel code review with persistent team members for follow-up questions. Similar to /subagent-review but reviewers persist after…
roslyn-query
Query .NET/C# codebases using Roslyn AST analysis via dotnet run file. Use for tracing data flow, auditing API usage, finding pattern violations, or ad-hoc codebase queries.
review-responder
Use when receiving feedback on code you implemented, responding to reviewer comments, deciding which review suggestions to implement, or pushing back on incorrect review feedback.
codex
Route a task to OpenAI's Codex CLI (codex exec) instead of doing it yourself. Use this skill ANY time the user names codex or types /codex — even for routine work Claude could handle, because the user is choosing to delegate. The intent: 'ask codex', 'have codex review/refactor this', 'check/validate with codex', 'see…