## Related - [[]] (source project) - [[]] ``` Check `wiki/` for an existing article covering the same ground first — if one exists, update and extend it (append to its `harvested_from`) instead of creating a near-duplicate. 6. **Cross-link both directions.** In the project file, add under the title (or in a closing section): `Harvested: [[]] ()` Link the new article from any obviously related wiki articles. 7. **Update the maps.** Add the article to `wiki/_index.md`; update the project's line in `projects/_index.md` (status done, harvested). 8. **Commit** in the vault repo: `harvest: from `. 9. **Report** back: article title(s), the one-sentence lesson each captures, and the links created. ## Run this — do not improvise This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `harvest_agent.py` and embedded as the fenced Python below (sha256 7dbd8459d682d355…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `harvest_agent.py` first: ```bash python3 harvest_agent.py '{"key": "value"}' # arguments as one JSON object echo '{"key": "value"}' | python3 harvest_agent.py # or on stdin python3 harvest_agent.py --tool # emit the JSON tool contract ``` Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`. ````python # rapp:deterministic """Harvest -- Distill a finished project from the Obsidian vault into a wiki article and cross-link it Generated by the rapp skill from harvest. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact.""" import json import re import sys try: from agents.basic_agent import BasicAgent except ImportError: # running OUTSIDE the brainstem -- stay executable anyway. class BasicAgent: # noqa: D101 - minimal stand-in, same contract def __init__(self, name=None, metadata=None): if name: self.name = name if metadata: self.metadata = metadata def perform(self, **kwargs): return "Not implemented." def system_context(self): return None def to_tool(self): return {"type": "function", "function": { "name": self.name, "description": self.metadata.get("description", ""), "parameters": self.metadata.get("parameters", {})}} # The procedural layer, verbatim from the source capability. The brainstem # returns this to the model, so the skill's instructions still drive behaviour # -- now behind a typed, deterministic tool contract. INSTRUCTIONS = '# /harvest — distill a finished project into the wiki\n\nVault: `~/Documents/Obsidian Vault` (read its `CLAUDE.md` for full conventions before writing anything).\n\nArgument: `$ARGUMENTS` — the project to harvest. May be a filename in `projects/`, a partial name, or empty.\n\n## Steps\n\n1. **Resolve the project file.** Look in `projects/` for a file matching `$ARGUMENTS` (fuzzy match on filename is fine). If no argument was given, list files in `projects/` with `status: done` (or `abandoned`) that have no `Harvested:` line, and ask which one to harvest. If the match is ambiguous, ask.\n\n2. **Gate on status.** Only projects with `status: done` or `status: abandoned` may be harvested — the whole point of the wiki is that articles trace back to finished work. If the project is still `active` or `paused`, ask whether the user wants to close it first (and if yes, set `status: done` before continuing). Never silently harvest an active project.\n\n3. **Gather the full story.** Read the entire project file, plus any `output/` artifacts it links to or that reference it (`grep -rF` for the project name across the vault). The article should be grounded in what actually happened, not what was planned.\n\n4. **Distill — do not summarize.** Write the wiki article as the reusable lesson, stripped of project-specific narrative:\n - What is the transferable pattern, principle, or pipeline?\n - When does it apply, and when does it NOT apply?\n - What would you do differently next time? (failed approaches are often the most valuable part)\n - Concrete anchors: one or two short examples from the project, kept brief\n A reader should be able to apply the article to a new situation without ever opening the source project. If the project taught more than one distinct lesson, write multiple small articles rather than one grab-bag.\n\n5. **Create the article(s)** in `wiki/` — plain markdown, no plugin syntax, no emojis:\n\n ```markdown\n ---\n created: \n tags: [relevant, kebab-case, tags]\n harvested_from: "[[]]"\n ---\n\n # \n\n

\n\n ## Related\n\n - [[]] (source project)\n - [[]]\n ```\n\n Check `wiki/` for an existing article covering the same ground first — if one exists, update and extend it (append to its `harvested_from`) instead of creating a near-duplicate.\n\n6. **Cross-link both directions.** In the project file, add under the title (or in a closing section):\n `Harvested: [[]] ()`\n Link the new article from any obviously related wiki articles.\n\n7. **Update the maps.** Add the article to `wiki/_index.md`; update the project's line in `projects/_index.md` (status done, harvested).\n\n8. **Commit** in the vault repo: `harvest: from `.\n\n9. **Report** back: article title(s), the one-sentence lesson each captures, and the links created.' # Ordered commands lifted verbatim from the capability's own documentation. STEPS = [ { "cmd": "grep -rF", "line": 13 } ] class HarvestAgent(BasicAgent): def __init__(self): self.name = 'Harvest' self.metadata = { "name": "Harvest", "description": "Distill a finished project from the Obsidian vault into a wiki article and cross-link it", "parameters": { "properties": {}, "required": [], "type": "object" } } super().__init__(name=self.name, metadata=self.metadata) def perform(self, **kwargs): # toaster:generated-perform missing = [k for k in self.metadata["parameters"].get("required", []) if k not in kwargs] if missing: return json.dumps({"status": "error", "missing_required": missing}, indent=2) resolved, unresolved = [], set() for step in STEPS: cmd = step["cmd"] for key, value in kwargs.items(): for token in ("<" + key.replace("_", "-") + ">", "<" + key + ">", "{{" + key + "}}", "$" + key.upper()): cmd = cmd.replace(token, str(value)) for leftover in re.findall(r"<[a-zA-Z][a-zA-Z0-9 _.-]{1,40}>", cmd): unresolved.add(leftover) resolved.append(cmd) return json.dumps({"status": "ok", "steps": resolved, "unresolved_placeholders": sorted(unresolved), "note": "Resolved deterministically by the agent; " "run in order. Nothing was executed here."}, indent=2) if __name__ == "__main__": # Standalone entry point: the deterministic layer runs with NO brainstem, # no framework, no install. This is what lets a "simple SKILL.md" platform # keep real determinism -- the host model shells out to this file instead # of improvising the procedure in prose. # echo '{"arg": "value"}' | python3 harvest_agent.py # python3 harvest_agent.py '{"arg": "value"}' # python3 harvest_agent.py --tool # emit the JSON tool contract _a = sys.argv[1:] if _a and _a[0] == "--tool": print(json.dumps(HarvestAgent().to_tool(), indent=2)) else: _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}") print(HarvestAgent().perform(**json.loads(_raw))) # rci-capsule:v1:H4sIAAAAAAAC/3VaZ5PjRpL9K4zWRZy0mBl416fVBkk4ggYgSAIkJcU2vPeOwIbut18B7J4Z6Xb7wwxZhcrMl/nSAOC/Xsy2CfLq5TVrk+TTi+NWYWc2YZ69vP76+/S9tquweC68cGHdhEmyMBdemIV14DqLosoj124WXpWniyZwF4pVh05oZovObJNmEWZNDq7vwzhcmFUT2om7MDNnYVd5XX9OwixehM3Lpxf3YaZF4tZPtUGeuoXpux9mhWDv5fVfL4mZ+cCOYgA2Z+BU3bjFdORfL3bqgA2/covF50oAW0A0OI7if/z+Bzif1U3V2hMMcPnLDws4MKvOrZvFby2GoMTC+c/IZgQTsgnDb9lvmT4Be128/S/M5XabullTw19Rz5tvix8r13QAsnrxtt4tLxz/JXXeFl5eLTwAaGHnWQfOTeYsLBcsA+lV2ISZD5wDwIEPP32ZdC0rf9YA1P3XUhMve/5wPr19WD1Z9WEmMPId05fF3hyA2BlM4mZm6gIQi7f3K2v47RPYKqZomMli2v60AIa5adEMs9IfflicJsdOn9Evi7/9TXPrPOncP+mbRH/5298WuzyP/yJ+xvlUvkjNxp7g/Nn+H712HIfn5iLPvrOzngLg/vRlsfEWGWDOO/5Fb9YLPwRO+7RIQKzmE/Vf9fZhEyze6sZs2vp14eSZC1QBW95MC3AOfHXefgIgzAa4CsAB8t+kp89c5/VtMVHm08xOs44XfRDOxrl/ci0wa/LC03JgrZlaod/mbf1pOjS7D5tcJpqNOyF7GjM5SsmS4cN79b81dbL0Y+WbxUDXHM3gw9Lvo98HOXBykQOSLnLvK00ny2ac7ykHvlWm7S4s044nOF9J3udV/BXUV8bXi2c2vJkgZbp3ywqzrYE5n96d44IT1XwMLFcgPiANJtF2ktcgjlOEKhCnHyd3ht5icIGHarf5K+Z39oOEAOxvZ94vDm4HJNYTJxrgs49UBdn1tOfD0Nnb+Lu3P6yZ86tu8mqYnK5NaTgtT9lW/Zm9nxZF0tZTwi3e8rYp2gZQaHKYZ04RAhCm8jSDyqunOyvXcys3s2eAP759lJsn47934Uxmcy5y8/pcDAG0M/j8UQbrIG8TZwqtX+Vt5oBwAD73c9jspjWTGXtRuIAFnwBZm+felAkFKIRgdXYAMTngozJ/lLN8vr5u09SswnFOVAMUGPcbQ74W46eBldvWpgW+A7LUOcgyUDBDoNuZaPWO6nNduHbohTaAV1XmFIrX37LFYvF5YUyWhU9RgGpZDfw0iyvMpnErIK+owswOi+RZbIqwcKd0+8fX824GrHZntwPMyfBMxP779YNyfu7943ut/ezFIW8n1E7ozRGaeJO5D1AWw9T9B6g3Jgi4M52uctMOgDwTsCH3GiB+zuccEKwzk/bd6Kr56V3HOs/sym2mrmWDRgmIO5WEKdx9PoWwahYfvetbE3x32KdF7BbNwqpC15vFLRdTX5jI/TX2s8KpR07A5sMfkZkbZ+b2IBMAG6ZmMVcNQNXFnCA5YMZUWaczdd5W9rfE+GtGN2brBw1AWU0MAIk0YZh7XgZ2P0LezwxJAVOnOC3q1Jw64kcFqT5y7P24DwL82TL9mYTkRMI1APdOsfdTP9Y/AeZNVXriHPy1cQH+gkXAzdjJ+2wi95SLPlirh6wxH/OKm+ZRWL9O4oHr3t7ePq5/Rubz5/l/e1bqvC5+bnIHVMob+Pu833/muF/m/cb0Qcx+rdzE7UCNmkJiAcNtswZMnDZ/ny/7Wl3/OQXxdfHby6+//qy+u094b0+//P77by/ftM+fflj8rD45/rluhilsYQP+fQc614HJIR8+ntLyu8j88i7l54+gW7kzfCyCPqy5yYTufeHz4t8btfjxz/H/6dvVz8L2rax9AgycRf6pCtQTsncvv+taBy7oFR9xmzt6Bpg+c8b/ylE7B0z8SsIJ7LOWvVf/dy+A+j8xZj4NLGgLx2yeYyBIUXdqEFOjmCqdM7F+Hpv+HBDQt6cZbqrmoB7NMZ/NAPlhVp+dtkhCG8icuUg9ufh1vrRy0GodUP2f899UCzfZ/5tlQL1xnMVUhp+V/BnGaXoArDTntjZprJ9SfnoWvu/Gh8nZxuTS5btrzpOAOTg/T3B/+eltPrKbTJoUTJn94ca5cEyNKLe6EAwToBL82zDN+OgJ3+Xpw+csUsyglo7z1/rxDN8/Q4DqMU2g//Ph++/Q/3c9Dz5/nqW+HQHcmhv23K8/fcuT53zKzL7O0zRsnon+tdkBAEX++jWOID+fKfDLE+zPHwnwNsthn2NmAcopkDONKa/fcEyOBJXk0ywcWPG5BgV+7sJPkQsXlPSFbRZNW01jxkSsOefm9v1eIL7M9wS2m9Vf7yqm7AF3A+8xBPug7oMlkMv1dLMBLCxcYMN0U/IvcBNRuWULWOQ8b1GaoZgO59aE4gVsg5rWgDRJnxcXwBIXyHUmSXUMevP0waKISSFRb5bPvzWM6wZ230aDLLMjxfpt7dRlUt6Dqyl33FWX/TTgLmmMHGjDCa51uL74a57r99zN2+sMcR037lbWddTZ47sTpq/9wIsCBqZDptshTLCuH1c6sKXYfuypNasGRZY/NlgeIntCaDC+rWh1Y5PDcXQe542yW+8oU2kvte8rihKaOBrsrrYgbe6nCN26rijsVbp1W1Ja7y7oFTsfKWlFHY/ZcOu6IKAzv2CujIojW8aFFciCWBqGg1GFGsvbbjtVpDmCpe9ppUP0cKWHDXs+9key6iVcuHADbMvYJlzifYKLl5jDrRDiG//K+TueIbf9LVX6fmyPd0IuyKTndqZ8YvFjkC13VL2EM5ph1CO2uTqxdIcZCOMf2AALZYlSpS3ERAT7fts5+y5b7fQNJ2z9zrurwZo9jAlsY3w3jlxOBCsfHxT1Sqeb9W0MRX8FBLOJrhTiSVfPBNLhIgRbds9DZEbGA5uyddN1OD88GtI7C16Ba+i4NDfUAO2ynmPvph4bLGHbXAZt9oaYpyQE2xzwNSXyNJGMyiUnsN6osyj21snZOal0ecDdokeRU5iGZidtRC5G+5Ul2PHSEgIKFsnzPvZP0XKlpEvY7pCocIpOoJf20u/3cuk/lMPwoLD1HofWgi2H20MfBVig5bsECxs1TnVkTcn5vl6moVx3leSXnKnh1OOo0KTIcPcrD50rWlqTVKlEpapw0DaL6huzQfpY3nGobzeGvtLDFkICjOGpk5ltHDRKr8NRqT0eYqLMh3jZwTx45cYXYkdJgskjvh66OM4/iKNZVDX/8J3NkrknBW7bciIv9QBZy2rEx9dbXZ2Xy2zvJh6P05lCH5cMhzXpnmrGZV/Lea8IyxXNrq8IMmKuRi2XjlI7FG/X+plJBFlZExYaWLarGFdXy7CMHnkdSqulSAwEUegg/Od6v/PbBxEv1+f1uky4NekjyX4YtLQEcfSs6vbYDIQw7JzSIyJ/Zewup8uIXuNEI7mb0JHwWpBC4nDEDUFv/EY9+uJmGXtKaXLFIdbZRIovKa8s7wZfdSPq7zSO2BLa+WTRKiIxxY3SzPYa46cNz/E7s0vVzdpID1DBmCLPsuEmPd03PgQvY1/j7sPlonHu2t6sVLpPWVkWi5Y0qyNm8hvB5NDBVQiUIpZEJ3d6vQ7zi7wRtHv4WCeyL4z8VeDwG8609J230tPAdR1rO8h+Y5hjH7Qn9tbQFHSBsxI2MLgdcRzuU93LhXOQbLW21XVDrE8XxKLWKyvlIM7fcvlhKbZI3yMB0gfEUsC2st8Zhc/tzttYhqAAGXOSbnf8kURvyQbbZUeJx5ZV3K8MUhvGIZC26dm+aNthVEtLVdBIzURHTA7KxkSrPZavIqpmNrAuPYYAs86noaJaKtoerst1jdjYkrFvayWI/djfCja6Ot6g/VpInMfW5juU3sMDGg7J1ro56yg/6hJZSoXOHRpt7apl6ejQcePLUK0l/WnDWahUFiupwFGvwUSiIvNhaZWOFqFawZkK7bk0/tiqvpqk6+NYdxLOHE98oB38QvR8VdTqe41mPZqcgtXZKYP92As27l7RMBWQNmE8S78I/rFWpGsWj9cq7YQVGHvXHuHqcXtFzg8Rb/bxEROgXLl1RboSK54yV+s2Cna+lHWEjHfVEVHh4ZhLIXPxPPyh46F4xkPv2IwwDCvXIMFxaMhQnc4e0MmIVkOrnaB9cyrR0iE9Zpnsrh6HXQk9yAioDLsgB6f989kxVGonQ5kCeZDq0y3CsAncjQ8mwZAu7ow9vK7rzjmng+MTD1wbahjUM55XN8V5dP1jK4xa31F+fqNW1ophbkPmgp7uoEIW5kq65x1dZ2RUXJU7nD3mJiKewpE5edTdWxkC0qgjG3GYeuNc3irLU7pNCX0JL71Ys6Po+riuyG7ZuJRjPlgrJe6BHZQ+JZXogRO1lLjC1sivtmd+H8VZdb3jnWYo1XK1VPWbsoUuXatH5DXsLG5bL3s1WsYrCPKtQ8aWoeYZJu2pCXzj7rHw2A9jleG+jsVrcUBvAzre1hS3g+kbEQutSlomneOOty03ZX4TAli43OnkShd9fccdX4RujERFbAwHrNzYEtOtNLbrZaSs793hinndaHOgMdGCnRNr4UFZkKgix+tKgyM0x+mjL7mYR9cZ1F1X7LG58S5F3/yjudatW99JlHp0xc064m3sYm6yQD7uTlV4zfFBjn2EHJRNkZ+aaNkryfWg3/CQhQlJRmGYdi8Cz/NHJUvR2+V0OgU+EmPZdmBa3Mxi/Wiv5J67OcySjCq9SO6OE+rHFWUia5xYeygaIxy+2VBLuCY5z1w7Pevl7a6l1a2AUUcdMJRbPRRf8Me9vtyrh62hBiUviCTTn8vVg5CqIWKc2z12tSOyPh4V09+MxkXhIfXgbguzEeBYy6o8LDsqyrTrfY1u+YRlRr3qeNoajEcPP7JH5WGaCnOmG/o7CB12K8xh7+iS5K9heGoPcnq+7bubmWAZX45Gb0vs0n5UWFFW9MWSDF0oEfRMR7eVjadnnXOwwarlPQa1ei3XJeOyGEk3j9NJy5OLJfZSmjHlUDT3R0zE161+Lm8jtWIyvbPKHEr9YdAH3SjowwVTPdanSKeJmuMR37or1GkuOttgbBuyV50aWnb5iJENxtp0ISImf29xmahHQR9Om4qwFLZkTiWGKAXwWn6kHqMkj/urYGl5Wpq0Qu1IMCVcBxwrW5L2LrQsyCgZHJQ0ayq5Ci9ar2HhGfhrFzP1gd+d7NP6dMsuA4lcSYuKz2ZWFeOx2dSs0rAFbeBYLQ+7JbU1xjAuDpYvEya9220hxx3IPZh+dRiL9qSlhmeWvQzJoMGySy+rrS5GqGPlpLMfKDWovVV6QDP2ceocxLzFO3HASJiWEeoRP/aa4hgixpayfr94Ud9DEm0RSWMaEUglqWq0Cx04qYzXwWOLVxdj3G+IvV2svENFYBodxHeMh68bk9KbRiw1hd+z+e1Gj9TdulAo6qzv2Eqvcvxax4zDhq1x61LhrpssXjWHAa1SBsbYE904thwpy57yg6p73FKXSQ+7JNwnlozQ19I+Fhs14ZxtpYidMDqxv+7PQujKoVZfx2qTeZc9GBSFRjRRTY/h6GLwoCnewCBAMrSLuQ+3Utdde3Sz6xhHKnZvZFMSMvjQt/h2t8M3u9o6nFxfFE8bQkJcNA1tAYmR7d2G9Auvdls7eZQbz4C2+dY9q3Xo3qEDCCXMBUM0Hrd6jeHjrmod1ultrGpZxXhsTDApjFKZ6iI2js3YGyRtIwi8OeNupGptZewPTtcJarh5WO0R90hKcBGbHJ1VjBDQHorKMuybQ3uiBoHrSFXsLyUu0B1HFl2J803qE+zNSsfGCo5BGV/OqOfChsbz1ww5X7Aglu9NU6GEW5TRpYfsfHSJlVajnYrKsXTqKrJzmK1WrCwmNzIU5aQyb6qileTW0Zlchod6vyqK9sGZzKNY22lN4Vu9rYZDZGrn7YZKGgLi/ZPlYY5y54vb1mketInhWyWgLng35GhPUIkdDIcGPtbhbV8kN7a5uzGhPXZHg6crzapqSbtxJYkfOObiQg7ooUbvMPrGdHrzMFamzvkOS9LW8pq0MYX3wa5FFTwS8WNy4CgzTMuyNZEHGVwTVmEFlHYltYwr7jxgm1zEWlpc48f64Mq5fzdzBLdlJCaxnZOtxtopQ6NEYLHVtHNHr+0IJLCkIbAuomA1DGT6vNqDy0KXPahimwpaHu1ihVLaXnEqu0lUHdfJrXl8WDuP3DdGIQbSrSxxm2j2RVaX0FnJfLSjzbOobejiWoMbmyCPOvFw0DzeT8mak1khHqhjN1zqAXiONGsPNeL6bOJBdXk0GJTb1i4+75kxZiPF2itQUXdEvCrZmBIl7yba1K5JHH2Z7iO2sihMVhHkegqQG0LpiLexOekwgtIhuD5+bk5IQtvZLXRRmRZwHTNXdnakjG48xXJw7wnyjqJs3Zpq0Vc+kclwiDKU7OFyodxJgqQKXxyocFVqmAQyu6m4HM+SUccok2V9lLzHpIVBl1DaDvudJgqPLFpyj2shSXru6t0OZcTHlr+vdhfFxJpe3F2Lsbns6eV5R/aUooGJXB+STWZUp3Me3fSo2/dY2Dk0Zz1W6z6XMnCnWjNqjRaGk59QWiT0hsN5Blt5Eu3sXRotQR0cPOnMGteTszJ1/SDu8cyzbMzaSCVm2YYGkzcU3EGZj+aCQc2Kb8rYfHRuqRwTdYNsSxsB09EKYldXNjmg+zWZ7NAgeZwJoooRnHMg3rwEuQvpWBXKhWQbLR+3uxuK3kJJont1H1rsarwgkjAE1O4Aq4eYp9s4HIQ88XabSL9J9rm4ubXrxocIcKikjmA8WfE8K2Lkug3vzApdhbEBSYfSoUCaqlfrLBnJzt16V80BU/zFiJqTPPrsSSBbH6HuV5DxDJwZcuc41ghVS89ivbvb6EJOIaRj68oGUFRrwG1+ql4cz69LFotN2kXcUmIOrRT1kp2dLraSkMl+BYWYge4aR6k8LpQ1pjBFMFnVBl46W05f0XfY09M8r05OCnKy3MSxURtezqPoCiycpRhio0LZaibdYn5XRoxiX0lZMXFsULINFT+KM475cpFtgdl536QN26IXGC30C4XQpI24+omS7uYQNca5MsOba6JR2EGp1GBKQzUnLCw5QY3cDGG3N9WIk6rOyayTqQxKYiswYNLNQMMADGja0i3k0QywIRiNKlRUjyGUs31o5fhwpyiSfFBSU91xC3EDfIOZm7UN5UV5dRRLuXePa8DtqLJykPUlqlhDFC+3rGBNVqhdKivLbXvoQvQiYtUDvZc2XhtHHEfh7TpR99TRNbEE13rCtcLyRESmc92HqXzu1nw03sdH6fKtAysM/zjQTYDL2zV0JqMsEs5ZY0E3VmNjMa52CPBlcTkcJNLEuioxmgtCZ5BFd7J4PiL2/mCFUAvb6tKkDSOGdTCKcmc0I5A6kR0015gyUPNY6k+Xg7oiWqEMzF2eBWiQ6gUNUVWLs0WFyVeoZssc9uHzvdxzbnBbLpd///vLp5ePl8Avry/vDw1h+LTd7HZfUmd61x+YGEmBTYw2XdN2acZ1cIb2PNLyGBbHTYRkGRzDKNqhTIZmEZNgWNqhwayFORZOMZSNEybmoi9/zM/n8g5oy2yg7teX6a3M6/yU7vU7jU1u1s3r/JOI6fcA08PB+u/IYv7Rwd/Rl0//4dj8cr9qXj//8nzu9zu40A6B4egXZD5U5HU4vaD8eA5ZJ63/DfQEdQA60n9OL0XdR/Nx2fSy4vnsEYivn7/IACKB0D/+D5GA9iDHIQAA ````